The wind industry has experienced tremendous growth over the past few decades. Advancements from design and manufacturing to O&M have led to reduced capital and maintenance costs, which make wind power an indispensable source for a comprehensive solution to global energy needs. The global cumulative installed capacity, combining land-based and offshore applications, exceeded 1 trillion watts by the end of 2023 . Despite the success measured by the scale of deployment, the industry is still challenged by premature component failures, which have become a bigger issue for newer turbine technologies. Furthermore, mature turbine technologies with longer operational experience have and will be subject to an increased number of component failures, like all ageing mechanical systems, leading to increased O&M costs. These turbines typically need overhauls or replacements of major mechanical components or blades throughout their service life, despite the fact that structural components such as the foundation and tower can normally last longer. Major component overhauls or replacements, although infrequent, are typically associated with long downtimes. When compounded by more frequent failures of minor components, they significantly increase O&M wind turbine costs and subsequently wind power LCoE. O&M costs typically increase as wind turbines age and are significantly higher for offshore wind power plants. For land-based wind plants, approximately half of the O&M costs derive from the turbines themselves. Comparatively, offshore O&M costs can be several times higher than those of land-based counterparts. This difference is largely attributed to higher failure rates in offshore turbines compared to onshore ones, a result of harsher environmental conditions, higher rotating speeds, and higher rated powers . Moreover, the hard-to-access nature of offshore wind farms increases O&M costs substantially. Turbine O&M costs represent the primary area for potential cost reductions, which can be accomplished through improved strategies enabled by condition monitoring technologies.

Wind turbines are complex systems operating under highly dynamic environmental conditions. Offshore turbines, in particular, are exposed to more severe environments than onshore counterparts, due to higher wind speeds, salt-laden humidity, and extreme weather events, which collectively accelerate degradation and increase failure rates . The emergence of floating offshore wind turbines introduces further complexity via platform-induced motions and additional dynamic loads, amplifying operational stresses on critical components . Consequently, both fixed and floating offshore assets exhibit higher failure frequencies and face greater maintenance challenges. Hence, it is necessary to identify and monitor critical components to ensure system reliability and reduce life cycle costs. A detailed understanding of component-specific failure mechanisms is therefore essential for developing the advanced condition monitoring, diagnosis, prognosis, and predictive maintenance strategies required to ensure the economic viability of the sector.

Reliability studies indicate that a minor portion of wind turbine assemblies (38 %) is responsible for over half of all failure rates and 60 % of total downtime . While electrical and mechanical components may fail at similar frequencies, mechanical failures result in significantly longer outages, accounting for more than 75 % of total downtime . Specifically, electric systems, control systems, and blade/hub assemblies drive the highest failure rates, whereas gearboxes, generators, and hub/blade assemblies cause the most severe downtime .

The gearbox remains one of the most critical drivetrain elements; it experiences a high number of failures, and its breakdown typically results in the longest downtime and highest economic loss . Similarly, generators consistently account for high failure rates and significant downtime across multiple reliability studies . In offshore systems, transformers and electrical components also represent a massive portion of critical failure modes requiring extensive condition-based maintenance .

Rotor blades are the third major contributor to O&M costs after the generator and gearbox . They are prone to leading-edge erosion, fatigue cracking, and delamination caused by sustained aerodynamic loading and environmental exposure . These loads are managed by the pitch system, which consists of pitch bearings and drives. While the pitch system acts as a primary safety mechanism for controlling aerodynamic loads, it is highly susceptible to wear and fatigue, presenting the highest failure rates in offshore systems . Although a pitch system failure can lead to devastating catastrophic events , it generally has a lower overall impact on maintenance and downtime costs compared to the energy-producing systems mentioned above .

Finally, the main bearing supports the rotor and transmits loads to the main shaft, making it vulnerable to various failure modes under highly non-steady loading. Historically, main bearing failure incidents may have been under-reported , as they are sometimes categorized under broader terms such as “drivetrain” or “other”. However, recent data suggest that main bearing failure rates can reach up to 30 % over a turbine's lifetime . While these typically result in lower annual failure costs than generators or gearboxes, the main shaft and bearings remain highly critical assemblies because their specific downtime costs can equal those of the primary drivetrain components .

Once wind turbines are installed in the field, the main opportunity for cost reduction lies in the improvement of O&M practices. Condition-based maintenance, enabled by various condition monitoring technologies, is a practice that the wind industry has investigated for O&M strategy improvements. Typical condition monitoring technologies include several modules: data acquisition, signal processing, feature extraction, modelling and analysis for fault detection, diagnostics and prognostics, and O&M decision-making. Without investing in any dedicated instrumentation, modelling and analysis for the purpose of fault detection, diagnostics, or prognostics can be conducted using supervisory control and data acquisition (SCADA) system data. Based on dedicated instrumentation, there are various condition monitoring technologies, e.g. vibration analysis typically using data collected by accelerometers, oil debris monitoring using magnetic field or image-based sensing principles, oil condition tracking based on certain parameters (such as cleanliness, contamination, or oxidation levels), and electric signature analysis, among others. All of these monitoring solutions can be conducted continuously, which is recommended if budget is not a challenge, and periodically, which has the downside of possibly missing fault signatures embedded in the data that were not measured. A typical value from SCADA-based monitoring is the deviation from baseline or healthy states that can be detected by comparing incoming data against predicted values using the normal behaviour models, which are developed based on data collected under healthy states. For major component monitoring solutions, the main benefit is to convert a potential full replacement to an up-tower repair. Fault detection is good information that the industry can benefit from. However, most end users would like to understand how bad the situation is, e.g. fault severity, and how much longer they can keep the turbine running, e.g. RUL prediction, until maintenance actions must be taken. The request is along the line of prognostics, which has not been widely adopted by the industry but has been actively investigated in the research community. The gap between academic exercises and field adoption needs to be filled before the industry can benefit fully from prognostics. Reliable prognostics can enable the bundle of a few different maintenance activities into one crane rental or one vessel trip. In addition, if root cause analyses of frequent failure modes are conducted and confirmed, the feedback can be provided to component designers and manufacturers to help improve future product performance.

With the gradual adoption of condition monitoring in the wind industry, various standards, recommended practices, and guidelines have been developed. They will become increasingly important as the industry adopts more predictive maintenance practices. A few well-established standards include the following: (1) ISO 20816-21: 2025 provides information on the measurement and evaluation of the mechanical vibration of wind turbines and their components. It covers both geared and direct-drive types of drivetrain configuration. Recommended zones for evaluation of vibration under continuous load operation are provided, although caution needs to be taken for early fault detection by using the recommended zone boundaries. (2) ISO 17359 provides a general methodology for setting up condition monitoring programmes. (3) ISO 13379 provides data interpretation and diagnostics techniques applicable to wind turbines. (4) IEC 61400-25 provides uniform information exchange for monitoring and control of wind power plants. (5) IEC 61400-1 includes design requirements on condition monitoring systems in turbines. (6) IEC 61400-13 provides methods for measuring mechanical loads in turbines that are relevant for condition monitoring. For recommended practices, one notable effort is the O&M recommended practices published by the American Clean Power Association (formerly the American Wind Energy Association). It has a chapter on condition-based maintenance, which covers the general overview, vibration analysis, grease analysis on different components (e.g. main bearing, pitch bearing), temperature measurement, nacelle parameter monitoring, oil monitoring, etc. In terms of guidelines, two older efforts are GL-IV-4 and the Allianz Zentrum für Technik report 03.01.068 on requirements for certification of condition monitoring systems in wind turbines. Their contents were mostly incorporated into DNVGL-SE-0439, a service specification for the certification of wind turbine condition monitoring systems.

Current efforts on standards, recommended practices, and guidelines address the evolving needs of the industry, such as the ageing onshore fleet with increased failure rates and offshore wind turbines, which face harsher environmental conditions and greater accessibility challenges. There is also a need to define open data formats and improve interoperability among condition monitoring systems, which can be accomplished through international collaborations on standard harmonization and knowledge exchange among various standard organizations, such as ISO, IEC, and IEA. Future efforts are expected to account for impacts from artificial intelligence (AI) and machine learning (ML) on modelling and analysis in support of fault detection, diagnostics, and prognostics, especially as turbines increase in rating and become more complex, automated, or intelligent. Additionally, cybersecurity and data integrity in condition monitoring systems need to be better addressed because of the increased connectivity and digitalization of wind plants.

DNV has classified a digital twin in six functional element capability levels where the capability increases for each level. Level zero represents a stand-alone model with no data or decision exchange between the model and the asset. In this case, the physical asset may not yet exist. A finite element model during the design phase is an example of this stage. Level one is the “descriptive” level, for instance, a multi-body dynamic model which is updated by data from the real asset. Here, the digital twin can describe the asset behaviour and provide alarms, for example. The second level, “diagnostic”, is where the digital twin is able to identify the faults and be used as a diagnostic tool. If the digital twin is able to estimate the RUL, then level three, “predictive”, is achieved. Level four is “perspective”. In this phase, the digital twin is able to provide recommended actions based on predictions. Finally, level five is the “autonomous” level where the digital twin can take over control from the user and control the asset.

As highlighted by , the main aim of employing a digital twin is to reduce the risk in operations; therefore, the digital twin itself should not pose or bring new risk. Specifically, these technologies target the reduction of risks associated with design errors, maintenance-related downtime, and structural fatigue over an asset's life cycle. As the digital twin functionality level increases, the associated risk may also increase. It is therefore essential to evaluate the risk and uncertainty throughout each level .

It is well known that the wind industry R&D and technology innovations, including condition monitoring, are hindered by data sharing. There are a few concerns by data owners: (1) the disclosure of sensitive information may impact their business competitiveness; (2) data-sharing infrastructure introduced to facilitate the data sharing may pose new cybersecurity risks; (3) uncertainty regarding specific data-driven project objectives and required data streams; and (4) a lack of clarity concerning the value generated by their data-sharing efforts.

The contradictory fact is that the wind industry is data rich. There are thousands of turbines, each with hundreds of channels of measurements of various types at different resolutions. There are a few possible ways to mitigate the data-sharing challenge: (1) use normalization and anonymization; (2) specify task objectives, data needs, and value (e.g. standardization and FAIR data principle) returned to the data owner; (3) adopt a clear data protection mechanism, making it beneficial to members of a consortium (e.g. NLR gearbox reliability database , EPRI WinNER , and ORE Catapult Sparta ), while releasing educational information to the public (e.g. data organized by turbine ratings, technology types without disclosing turbine or component original equipment manufacturers); and (4) investigate novel data-sharing approaches that enable proprietary data sharing without disclosing sensitive information.

To support condition monitoring research and root cause analysis for a wind plant, an ideal dataset should include the following five categories of information:

Basic plant information, such as the site layout, resource measurements from met towers or turbine nacelles, the number of turbines, and specific turbine specifications;

To address the data access and digitalization challenges, several international initiatives and research proposals have emerged, targeting the technical, cultural, and business barriers that currently hinder sector-wide collaboration. These efforts, such as those coordinated by the IEA Wind Task 43 , focus on streamlining the digitalization of wind energy through international research on data standards, sharing protocols, and advanced analytics to improve life cycle efficiency and reduce costs. By developing collaborative ontologies, data maturity roadmaps, and guidelines for best practices, these projects aim to foster a culture of “coopetition”. Ultimately, these initiatives seek to publish practical recommendations and success stories that demonstrate how priority use cases can be solved through improved data sharing and organizational transparency.

Condition monitoring based on the NBM methodology generally consists of two phases: (1) developing a model that can be used to predict normal or healthy behaviour of the machine (this model is called the NBM) and (2) analysing the prediction error, which is the difference between the observed and predicted data, for abnormal patterns and deviations. Normal behaviour models can be developed using various approaches, including statistical models, shallow ML, and deep learning. Table  gives an overview.

Statistical methods. This is a group of models based on traditional statistical techniques. These can be unsupervised or supervised. The advantage of statistical models is that they are in general computationally undemanding, data efficient, well studied, and well understood. The disadvantage, however, is that they are less suitable to model highly complex non-linear dynamics. Algorithms like principal component analysis, ordinary least squares, ARIMA, and, more recently, cointegration have been/are popular. However, statistical techniques are used less frequently in recent research than ML techniques.

The previous section focused exclusively on the NBM methodology. However, other methodologies have also been used, although less frequently. For this reason, they are only briefly discussed in this section. This overview is based on the classification proposed by . The following methodologies (excluding NBM) are distinguished in : trending, clustering, damage modelling, and the assessment of alarms and expert systems.

Trending techniques involve tracking long-term time-series SCADA data to identify patterns, trends, and anomalies that can indicate potential faults or inefficiencies. The main challenge lies in interpreting trends due to varying operational conditions, as changes in SCADA parameters do not always indicate faults. Several works have demonstrated how these techniques can be used for anomaly and fault detection. For example, found that decreasing gearbox efficiency leads to a temperature rise approximately 6 months before failure, while explored comparing temperature differences between turbines at the same site but found the approach unreliable due to environmental variations. introduced a binning method averaging wind speed, generator speed, and power output to track damage levels. While trending SCADA parameters can help to detect failures by comparing historical and current data, interpretation challenges and turbine-specific variability limit accuracy. Without addressing these issues, uncertainty and false alarms remain a risk in maintenance applications .

Clustering techniques are unsupervised ML methods that are used to group data points into clusters based on their similarities or differences. They use distance metrics, such as Euclidean distance or cosine similarity, to measure the similarity between data points. Clustering algorithms applied to SCADA data have been proposed for the automated classification of normal and faulty observations by identifying patterns or distinct operational states representative of potential anomalies or faults. For example, applied self-organizing maps to classify turbine operating conditions and detect anomalies in the gearbox. proposed the k-means clustering algorithm to perform an exploratory analysis of SCADA data to identify patterns and anomalies that can indicate potential faults or abnormal behaviour which can then be further investigated by experts. More recently, introduced a novel approach to dynamically cluster wind turbines based on real-time SCADA signal analysis, thereby accounting for temporal and operational variations. Unlike static approaches, this method proved to be more adaptive and accurate in identifying performance trends and anomalies. However, research showed no clear advantage of clustering over trending methods. Additionally, the requirement for faulty data in the methodology could pose a challenge in an industrial setting . In damage modelling, the observed signals are interpreted using physical models of the machine or component. This is different from the NBM approach where an empirical or data-driven method is used. However, the applicability of modelling-based methods depends on the availability of a physical model.

Expert systems have also been used for wind turbine condition monitoring research. These systems can be used together with NBM-based systems as a way to interpret the results . An expert system is composed of several parts, e.g. a knowledge base, a reasoning or inference engine, and some sort of user interface through which experts can interact with the AI. The knowledge base contains facts and rules, and the inference engine applies the rules to deduce new facts or to give an explanation or prediction. An advantage is that the rules are, in general, easily interpretable. Disadvantages are computational complexity and maintenance problems when the number of rules and facts become very large. Expert systems have been applied to wind turbine failure diagnosis cases, e.g. , , and .

In the categorization of ML, supervised learning utilizes labelled datasets for training, aiming to approximate a mapping that predicts output labels based on input data. Supervised learning is further classified depending on whether the output labels are numerical variables (regression problems) or categorical variables (classification problems). When applied to wind turbine diagnostics, the classifier-based methods leverage an input vector composed of features extracted from pre-processed data collected from the component. The input vector is labelled with the categories reflecting the status of wind turbine components. Fault detection can be regarded as a binary classification problem, where the system is categorized into one of two labels, i.e. “healthy” or “faulty”. Fault diagnosis involves a multi-class classification task where the input label is classified into multiple non-overlapping classes encompassing specific faults/failures of the component. The model is subsequently trained to classify component status into the predefined categories.

Commonly used classifier-based methods for wind turbine diagnostics are SVM , k-nearest neighbour (kNN) , logistic regression , artificial neural network (ANN) , naïve Bayes , decision tree , extreme gradient boosting (XGBoost) , light gradient boosting machine (LightGBM) , and random forests . Current research primarily focuses on improving conventional classifiers, developing ensemble methods that aggregate multiple learners, and utilizing deep learning to process high-dimensional data, enhancing data pre-processing and feature extraction techniques . Moreover, exploring the application of transfer learning and increasing model transparency, explainability, and interpretability are also critical endeavours . These efforts aim to improve the diagnostic performance of classifiers on real data from wind turbines.

The typical performance metrics of diagnostics include accuracy, recall, precision, and F1 score, which are derived from the observations, including the number of true positive (TP), false positive (FP), true negative (TN), and false negative (FN) observations. Accuracy is calculated as 1Accuracy=TP+TNTP+TN+FP+FN. The recall is calculated by 2Recall=TPTP+FN. The precision is calculated by 3Precision=TPTP+FP. The F1 score is calculated by 4F1=2×Precision×RecallPrecision+Recall. The performance superiority of different classifiers is generally assessed through these metrics. However, classifier performance can vary across datasets due to factors such as class distribution, feature correlation, and dataset size. To systematically compare the performance of different classifiers, tested and compared 16 classifiers for detecting pitch system faults, utilizing a dataset containing SCADA data and alarm logs collected from 14 wind turbines across two wind farms in Brazil. The results indicated that most classifiers achieve an average accuracy of 80 % in distinguishing between healthy and faulty operating modes. Among them, the random forest, extra tree, XGBoost, LightGBM, CatBoost, gradient boosting (GBoost), and kNN demonstrated superior performance. In the paper , the authors tested 13 classifiers by using a SCADA dataset originating from a 3 MW direct-drive turbine in Ireland. The type of wind turbine faults cover generator heating faults, mains failure, feeding faults, air cooling faults, and excitation faults. The comparison results reveal that the best-performing classifier is the ensemble learner based on bagging, achieving the highest accuracy, precision, recall, and F1 score at approximately 79 %. This was followed by GBoost, with CatBoost, random forest, and XGBoost trailing behind. Building on these basic classifiers, researchers have continually innovated algorithms to improve performance. For instance, in the same paper , the authors proposed a two-tier fault detection and diagnosis framework. In binary classification, bagging achieved accuracy, precision, recall, and F1 scores exceeding 95 %, outperforming the voting classifier and CatBoost. For multi-class classification, the stacking ensemble learner demonstrated an overall precision of 86 %, with accuracy, recall, and F1 scores all surpassing 84 %, followed by random forest and CatBoost.

Single-turbine models for condition monitoring provide tailored fault detection by aligning with each turbine's unique operational profile, capturing faults specific to individual units. However, these models are computationally demanding in large farms, as each turbine requires its own model and frequent recalibration to address evolving conditions. Additionally, without cross-turbine comparisons, single-turbine models can struggle with environmental variability, increasing false positive or false negative rates. These limitations highlight the potential benefits of a comparative, multi-turbine approach, where analysing performance deviations among turbines can enhance fault detection and provide a broader, context-aware perspective on turbine health.

Comparative analysis of wind turbines within a farm using SCADA data offers a considerable advantage for condition monitoring, as identifying performance deviations among turbines can help operators to detect anomalies early . However, this approach also presents challenges due to the unique operational signatures of each turbine, influenced by factors such as manufacturing variances, site-specific conditions, and individual wear patterns . Differences in assembly processes can cause performance disparities even among turbines of the same model. Additionally, environmental influences, such as the wake effect, can result in variations in energy output and mechanical stress among turbines within the same farm. Exposure to differing levels of sunlight can also lead certain turbines to consistently operate at higher temperatures, a condition that reflects site specifics rather than equipment faults. Over time, wear and tear on components such as gearboxes, bearings, and blades further contribute to distinct performance profiles for each turbine. These differences can complicate the establishment of performance baselines and the application of uniform diagnostic models throughout the farm. The challenge lies in balancing the need for standardized approaches with the need to customize to reflect the unique conditions of each turbine.

The signal trending approach examines long-term changes in operational data, assuming failures have identifiable signatures in variables like temperature. introduced a method tracking the relationship between binned active power and key sensor readings (e.g. rotor and generator bearing temperatures) to visualize turbine health over time and support early fault detection. advanced this with a control chart monitoring algorithm comparing turbine performance against the farm average to detect generator issues. In contrast, used median differences across turbines to create a condition vector, incorporating monitoring charts with strategies to handle autocorrelated data. However, these methods are often univariate, limiting their ability to capture interactions among variables within the complex, interconnected systems of wind turbines. Even more, automated online monitoring based on trending methods has been shown to fall short of achieving the required accuracy. This limitation is attributed to the case-specific nature of the problem, which necessitates offline visual interpretation of trends. The challenge is further exacerbated when monitoring extensive wind farms operating under varying conditions. Clustering algorithms have been proposed as one potential approach to mitigate the limitations of trending methods.

Clustering-based anomaly detection offers several advantages for wind turbine condition monitoring. By grouping turbines based on similarities in their performance data, clustering enables the identification of outliers that may indicate faults. This approach provides a powerful framework for analysing complex, high-dimensional data from wind farms. For example, distributing the wind turbines into peer clusters such that the wind turbines within each of the clusters have similar environmental conditions has been used in . This clustering enabled the identification of underperforming turbines within each peer group using a predictive performance model. By analysing the performance metrics of these underperforming turbines, a critical component was identified and the end-of-life of the component was subsequently predicted. Additionally, ensemble approaches that integrate clustering with advanced algorithms, such as isolation forests applied at the farm-wide level, have demonstrated significant potential in detecting anomalies with improved precision . The high dimensionality and variability of SCADA data can complicate the clustering process, making it difficult to ensure the meaningful separation of operational states. Additionally, the selection of appropriate clustering algorithms and the definition of similarity measures often require domain-specific knowledge, which may not generalize across different wind farms. Furthermore, clustering methods can be computationally intensive, particularly when applied to large-scale wind farms with extensive data streams. The dynamic nature of turbine operations and environmental conditions also pose challenges, as clusters may need frequent updates to remain accurate and relevant. To address these challenges, NBMs have been proposed in the literature as an effective alternative.

As mentioned in the previous section, NBMs are data-driven frameworks designed to predict the expected operational behaviour of wind turbines. When extended to include cross-turbine information, NBMs leverage data from multiple turbines to enhance condition monitoring by enabling comparative analysis across units with similar operating conditions. For example, use cross-turbine NBMs to detect failures by analysing the temporal evolution of signals from several SCADA systems belonging to geographically proximate turbines. By evaluating joint variations in these signals, the overall behaviour of the turbine assembly is assessed to identify deviations indicative of faults. Furthermore, introduce a framework that uses the meta-predictive power score to construct a fleet-wide similarity matrix, capturing operational similarities between turbines based on environmental data, operational metrics, and multivariate regression outcomes. By incorporating this similarity matrix into a community detection algorithm, the framework identifies groups of turbines with similar behaviour. This approach transforms NBMs from a single-turbine tool into a fleet-wide monitoring solution.

Finally, transfer learning between turbines has emerged as a promising approach. Transfer learning involves using knowledge gained from one domain (source) to improve learning in another domain (target). This technique enables the application of models trained on data from certain turbines to others within the same farm or across different farms. For instance, a study by introduced a deep boosted transfer learning method for wind turbine gearbox fault detection. This approach prevents negative transfer by focusing on relevant information from the source machine, updating the weights of both source and target models to enhance fault detection accuracy. Similarly, explored transfer learning approaches for wind turbine fault detection using deep learning. Their research demonstrated that models trained on data from multiple turbines could be fine-tuned with minimal data from a target turbine, achieving performance comparable to models trained solely on extensive target data.

While the use of SCADA data for condition monitoring offers notable economic and operational advantages, several inherent limitations and complexities must be considered. highlight the following key challenges:

Low sampling frequency. SCADA data are typically recorded at low sampling rates (e.g. 10 min averages of 1 Hz data). This limits the ability to capture transient and dynamic phenomena, which are often critical for early fault detection.

As wind turbines increase in size, the rotational speed of the blade, and consequently the rotor, decreases, while gearboxes often amplify the rotational speed by up to 100. Hence, specific challenges arise for vibration signal acquisition. On the one hand, signals must be sampled over long enough durations to capture an adequate number of cycles from each component. On the other hand, the sampling rates must be sufficiently high to capture high-frequency bursts generated by defects on the rotating components . For condition monitoring purposes, the typical highest frequency range is around 20 kHz, while the lowest range can go below 1 Hz . Meeting these conditions requires the acquisition of long-duration signals at high sampling rates, which poses challenges related to data storage and the development of memory-efficient signal processing techniques for the continuous monitoring of drivetrain health.

Another challenge is identifying the optimal placement of accelerometers to maximize the signal-to-noise ratio during vibration sampling. Vibrations from all excitation sources within the system contribute to the recorded signal, and placing a sensor on one part of the machine does not prevent it from detecting vibrations originating from other components. Furthermore, its transfer path significantly influences the sampled signals . In a particular example, planetary gears, a typical stage in gearboxes of non-direct-drive wind turbines, feature revolving components that alter the transfer path relative to the sensor's position over time. This variability introduces another challenge for signal processing, isolating the target signal by suppressing interference from extraneous vibrations.

Order tracking is a crucial pre-processing technique in vibration-based condition monitoring, particularly for wind turbines, where the rotational speed varies continuously. Speed variations can cause spectral leakage without order tracking, complicating fault detection unless statistical features in the time domain are used. This method transforms a non-stationary time-domain vibration signal into a stationary angular domain, ensuring that spectral content remains independent of speed fluctuations . As a result, spectral peaks remain sharp and unaffected by speed variations, and the resampled signal's spectrum is represented in the order domain, the angular equivalent of frequency. Figure  displays an overlaid spectrum of original and angular resampled vibration signals to show the effect of order tracking.

Order tracking is a fundamental step in spectral analysis, enabling the accurate interpretation of vibration signals. However, it requires knowledge of the instantaneous rotational speed, typically obtained from a tachometer or estimated from vibration data. While wind turbine manufacturers often integrate tachometers into gearboxes, this is not always the case, particularly in direct-drive wind turbines. Various techniques for instantaneous angular speed (IAS) estimation from vibration signals have been proposed in the literature. One common approach involves time–frequency analysis to track dominant speed-synchronous peaks , while another widely used method relies on phase demodulation of band-pass-filtered signals . provide a comprehensive review of IAS estimation techniques. Although these studies present general vibration-based IAS estimation methods, some research focuses explicitly on order tracking for wind turbine drivetrain condition monitoring .

For instance, introduced an instantaneous frequency estimation method followed by order tracking to enhance vibration spectra for wind turbine gearboxes under non-stationary conditions. By analysing the time–frequency representation of vibration signals, the study successfully detected shaft misalignment in order-tracked spectra. Similarly, proposed two IAS estimation methods, which are the time–frequency ridge fusion and the logarithm scheme, where the estimated speed was used for order tracking to identify a faulty bearing in a wind turbine's planetary gearbox. Additionally, developed a tacho-less diagnostic technique that applies a fast dynamic time-warping algorithm to resample gearbox acceleration signals, aligning a filtered shaft harmonic with a reference signal based on an estimated constant rotational speed. Validated through simulations and experimental data, this method enhances fault detection without requiring speed sensors, demonstrating its effectiveness in industrial applications.

Although order tracking itself is not a fault detection technique, it serves as a crucial pre-processing step and is often followed by spectral, envelope, or time–frequency analysis methods for effective condition monitoring.

Following angular resampling, signal editing, or pre-processing constitutes an essential step in the signal processing pipeline for wind turbine drivetrain condition monitoring. This stage primarily aims to enhance the signal-to-noise ratio and reveal relevant information in the vibration signals. These techniques can be combined or employed alone, depending on the component's potential failure modes and the nature of the acquired signals.

Cepstral editing. Cepstral analysis, in essence, represents the spectrum of the vibration spectrum. It is a powerful tool for examining harmonic families and their sidebands in vibration spectra. This makes cepstral analysis a particularly valuable technique for monitoring wind turbine gearbox vibrations characterized by numerous harmonics and sidebands. The complex cepstrum of a signal is defined as the inverse Fourier transform of its logarithmic spectrum, expressed as follows:5Cr(τ)=F-1(ln⁡(F(x(t)))),where F and F-1 represent Fourier and inverse Fourier transform. The free parameter τ denotes the quefrency, with its unit being second, of the cepstrum of the vibration signal x(t). Two additional variations of the cepstrum are commonly defined: the power cepstrum and the real cepstrum. The power cepstrum is derived by squaring the magnitude of the signal's spectrum and then applying the Fourier transform . Alternatively, the average power cepstrum can be estimated through Welch-averaging techniques. The real cepstrum, meanwhile, is obtained by extracting the real part of the complex cepstrum, effectively disregarding the phase information of the signal .

Cepstral editing is a powerful, indirect method for monitoring component conditions. In this technique, vibration signals are processed in the cepstral domain (using a method called liftering, which is similar to filtering) to remove first-order cyclostationary components within specific quefrency ranges, as shown in . This process, known as signal pre-whitening, ensures that strong first-order cyclostationary components do not hide important second-order cyclostationary details, thus improving diagnostics. Another use of cepstral methods is detailed in , where comb-liftering is applied to the cepstra. This removes the effects of varying operating conditions from the vibration spectra, allowing for more precise identification of gear meshing frequency amplitudes and enabling the tracking of gear tooth fault deterioration. Cepstral editing is also an effective tool for detecting and suppressing harmonics from the signal spectrum, which is essential for performing modal analysis used to assess the fatigue life of wind turbine components .

After the vibration signals undergo pre-processing, the analysis typically proceeds to investigate the cyclostationary properties. For condition monitoring applications, comprehending the signal signatures from healthy and faulty drivetrain states is a vital stage of signal processing. While the vibration signatures vary among drivetrain components (e.g. blades produce different vibrations than generator bearings), these signals are classified as cyclostationary. This classification is named from signals' inherent property of exhibiting an underlying periodicity in the temporal flow of energy , a characteristic prevalent in rotating mechanical systems.

Cyclostationary signals cover a wide range of statistical features and are classified with respect to their orders. Generally, cyclostationarity up to the second order is prevalent in vibration signals from rotating machines, and higher orders are typically not of interest for condition monitoring. However, some studies explore the potential to utilize higher orders of cyclostationarity for condition monitoring of rotating machines . By definition, an nth order cyclostationary signal exhibits periodicity in its nth order moment . The first-order cyclostationarity represents the simplest form, where the first moment is periodic. In other words, purely first-order cyclostationary signals have a constant periodic mean. First-order cyclostationary signals can arise from gear meshing, shaft imbalances, or misalignments, and they typically produce prominent peaks in vibration spectra . As expected, second-order cyclostationary signals have a second moment that remains constant over periods, or their auto-correlation function is periodic . Examples of drivetrain components that emit second-order cyclostationary signals include bearings and blades with high aeroacoustic noise. The term pseudo-cyclostationarity, introduced by , describes a specific type of cyclostationarity generated by roller element bearings. This term refers to motions where the cyclic behaviour is not strictly periodic, but the bursts forming cyclostationary signals slightly deviate from the expected repetition frequency.

Since the dominant vibration emitted by rotating drivetrain components exhibits cyclostationary characteristics, signal processing techniques designed to handle cyclostationarity are widely used in drivetrain condition monitoring, such as envelope analysis.

The envelope analysis begins with high-pass or band-pass filtering to isolate the high-frequency components where fault-induced modulations occur, hence removing low-frequency machine vibrations and background noise. The filtered signal is then demodulated to obtain its envelope, which contains information on faults near their theoretical frequencies . Focusing on amplitude modulations rather than raw vibration data provides a more explicit spectrum, reducing noise interference and improving diagnostic accuracy. However, determining the optimal band-pass or high-pass filter settings remains a challenge. The literature discusses predefined and adaptive filtering techniques and correlation maps, which provide carrier frequency information . Despite its widespread use, envelope analysis requires extensive pre-processing to reveal fault signatures effectively.

Second-order cyclostationary signals exhibit periodic second-order statistics characterized by periodic autocorrelation functions. A typical example is the vibration signal from a rolling element bearing fault, such as a crack in a raceway, which generates impulses convolved with the structure's resonance frequency. The envelope spectrum is traditionally obtained via the Fourier transform of the envelope, estimated from the analytic signal. Alternatively, spectral correlation provides a method to compute the envelope spectrum , referred to as the enhanced envelope spectrum.

investigated spectral correlation density maps for diagnosing bearing faults using experimental vibration signals. Their findings suggest that 2D spectral correlation density maps reveal bearing fault frequencies in the discrete cyclic frequency domain. Additionally, the study introduced the enhanced envelope spectrum derived by integrating spectral correlation density maps over the continuous carrier frequency domain. The authors also demonstrated that spectral correlation density maps assist in determining optimal band-pass filter frequencies to highlight modulations in vibration signals from complex machinery.

Several spectral correlation estimation methods have been extensively studied and compared by . While cyclic spectral analysis provides deeper insights into vibration signals, it is not significantly more complex than conventional spectral analysis. However, a significant drawback is estimating the discrete modulation frequency α up to an unknown upper limit, which is computationally intensive . To mitigate this, a fast algorithm employing the short-time Fourier transform has been proposed for spectral correlation or coherence map estimation .

Following the previous signal processing operations, a range of health indicators is extracted from vibration data as the next step. These include statistical features in the time domain and spectral features in the frequency domain, which are commonly employed to monitor specific kinematic elements of rotating machinery. However, as highlighted in recent research , no single indicator can effectively detect all types of fault in such systems. As a result, multiple indicators are computed to improve the coverage of fault detection. To capture signal characteristics across various frequency bands, filtering techniques are applied, enabling the estimation of statistical parameters from both the deterministic and stochastic components of the signal. Spectral features are derived from both the signal and envelope spectra.

In this context, and introduced a statistical approach to systematically construct indicators that target specific characteristics in the time and frequency domains. Additionally, a framework for designing new statistical indicators aimed at early fault detection has been proposed . This work also reviews commonly used indicators in condition monitoring and links conventional health indicators to actual component degradation mechanisms. Apart from the statistical indicators, fault detection also leverages indicators derived from spectra or cepstra. For instance, several cepstral indicators have proven effective in detecting gear tooth faults . While the evolution of harmonic families can be traced in signal or envelope spectra, monitoring their transformation into the cepstral domain offers a more straightforward approach .

Given the complex structure of modern wind turbines, which contain numerous rotating elements, a wide range of characteristic frequencies must be monitored. These health indicators are further organized according to different operational and environmental contexts to enable more accurate trend analysis, as their behaviour is influenced by such conditions. Consequently, each vibration measurement generates a large volume of indicators, making manual expert evaluation highly resource intensive . This issue is compounded by the continuous generation of such indicators across all turbines in a fleet, significantly increasing the complexity of condition monitoring tasks . To address this challenge, a practical approach involves integrating health indicators with contextual data to form composite high-level features that better represent the turbine's condition .

Advancements in technology have enabled innovative data acquisition and signal processing methods for condition monitoring. One emerging approach involves analysing vibration signals derived from video recordings. Initially, video frames were utilized for performing modal analysis . With improvements in camera sampling rates and the availability of larger storage capacities, it is now feasible to capture high-frequency data, even using smartphone cameras . These advancements have allowed applications such as instantaneous rotational speed estimation from video-based vibration data. Moreover, recent studies have demonstrated the feasibility of diagnosing faults in rotating machinery directly from high-speed video frames .

According to DIN EN 13554, the physical phenomenon of structure-borne sound is characterized by “transient elastic waves within a material” . The loading of material by external forces or environmental conditions generates structure-borne sound, for example, caused by “local plastic deformation, crack growth and friction” . The resulting elastic waves propagate in the material or neighbouring fluids. In the following, the elastic waves are called structure-borne sound waves. Structure-borne sound waves contain information about the processes, such as wear processes, within the material. The molecular lattice theory can explain the generation of structure-borne sound through friction, as shown in Fig.  .

For the explanation, a frictional process between the base and counter body (P1), in the form of a shaft and journal bearing, is considered as an example. The friction process causes a material molecule to change from stable to unstable (P2). The energy is added up during friction until a limit value (P3) is exceeded. The molecule is then transferred to the stable initial state (P4). During the transition from the unstable state (P3) to the stable state (P4), part of the stored energy is released as a structure-borne sound wave from the inside of the material to the surface (P5) .

In addition to detecting friction states, according to , inevitable friction and wear phenomena can be detected depending on the frequency range, as shown in Fig. . In the diagram, the amplitude is qualitatively assigned to various causes. A quantitative statement is impossible due to many factors influencing the AE signal, such as the measurement system and conditions. Characterizing individual phenomena based on a frequency range mainly depends on the test conditions. Research focusing on the early detection of fatigue damage using the AE technique has shown that fatigue damage in the rolling element bearing raceway surface can be detected from AE signal components in the frequency range of approximately 0.15 to 0.4 MHz . Accordingly, peaks observed in this frequency region are considered to be associated with the occurrence of flaking. However, interpreting AE signals solely based on their frequency range remains challenging. Numerous damage mechanisms may overlap in the same frequency band, making it difficult to distinguish between them clearly. Moreover, amplitude-based indicators are often unsuitable, as the signal transmission path, sensor characteristics, and the measurement setup significantly influence them.

In the 1950s and throughout the 20th century, research in the field of monitoring systems was characterized by the fact that the performance of these systems was severely limited. For example, continuous recording of AE signals over a long period required disproportionate effort. For this reason, the AE signals were reduced to a few parameters for storage. At the beginning of AE technology, the recording was carried out using analogue methods so that only easily measurable parameters could be recorded, and the approach of reducing a signal to characteristic parameters is known as the parameterized approach . The characteristic parameters of a transient signal are shown in Fig.  using the terms commonly used in the literature.

A hit corresponds to the detection of an AE event and is characterized by the occurrence and decay of a structure-borne sound wave. The number of hits per time unit is suitable for evaluating the machine status. A hit begins when a predefined limit value is exceeded for the first time and ends when the limit value is exceeded for the last time. Counts define the number of times the limit value is exceeded during a hit. The amplitude describes the maximum signal level that occurs, which depends on various factors such as the sensor, the coupling between the sensor and the measurement object, and the distance of the sensor to the signal source. The period of a hit is referred to as the duration. The rise time corresponds to the period between the first time the limit value is exceeded and the maximum signal amplitude .

Other parameters, such as the energy content of a hit, are not displayed. The parameterized evaluation includes a large number of statistical parameters that can be extracted from a signal. These include the root mean square (rms) value, the standard deviation, and the kurtosis value .

Compared with traditional vibration analyses, AE technology can obtain valuable information about the condition of a measurement object in the preliminary and early stages of damage. However, the evaluation of the data poses a particular challenge. AE signals frequently have a low amplitude (in the microvolt to millivolt range) and are overlaid by ambient noise, necessitating specialized methods for separating relevant events from noise in the AE signal. The following subsections present time-domain analysis, frequency-domain analysis, and time–frequency-domain analysis.

The evaluation of measurement signals in the time domain is typically predicated on two fundamental principles: the signal progression over time and the analysis of distinctive characteristics. A general distinction between dynamic time signal analysis and time trend analysis can be made. Dynamic time signal analysis is particularly well suited to the assessment of short-term behaviour, for example, in the form of burst events. An integral component of this analysis is statistical feature extraction, in which a range of parameters is calculated to characterize the signal. These include the mean value and the variance, which provide fundamental information about the signal level and its dispersion. The rms value is another key parameter, as it allows for information about the energy of the signal and correlates with the mechanical load on the bearing. The crest factor is also essential, as it describes the relationship between the peak value and the rms value, and high crest factors indicate impulse-like disturbances that can be caused by developing damage. Furthermore, kurtosis can be utilized to identify non-stationary surge signals, as it exhibits sensitivity to outliers in the signal. The time trend analysis is employed to assess the long-term behaviour of the signal curve, for example, with the help of the envelope curve analysis, which is particularly useful for identifying repetitive pulse patterns .

The transformation of a time domain signal into the frequency domain enables the detection of recurring structures and modulation patterns, which can be utilized to diagnose journal and rolling element bearing damage. The fast Fourier transformation (FFT) is the most prevalent method for decomposing an AE signal into its frequency components. The subsequent frequency analysis of the signal facilitates the classification of its frequency components and, by extension, their attribution to either critical operating conditions or specific damage .

In rolling element bearings, only a fraction of the race is in contact with a rolling element (and therefore under load) at any given time. At a specific point on the race, the load is oscillating at a frequency dependent on the shaft's rotational frequency. Since initialization and growth of faults occur under load, monitoring the over-rolling frequencies allows for the detection of damages with AE. For rolling element bearings, the over-rolling frequencies are listed as follows:

BPFO: ball pass frequency outer race6BPFO=nBfr21-dBdmcos⁡ϕ.

Bearing damage often generates non-stationary signals, which is why a pure frequency analysis is usually insufficient to capture all the dynamics of the signal. Time–frequency analysis combines both domains and enables a detailed investigation of signal changes over time. One possibility is to use the short-time Fourier transform (STFT). With the STFT, the signal is divided into time windows to create a local frequency analysis. One disadvantage of the STFT is the constant window size, which requires a compromise between frequency and time resolution. A longer time window leads to better frequency resolution with poorer time resolution. In contrast, a shorter time window leads to better time resolution and poorer frequency resolution. Wavelet analysis uses an alternative segmentation technique and allows variable window sizes. In wavelet analysis, longer time intervals are used to analyse low-frequency information, while shorter time intervals are used for high-frequency details. This allows the signal's low-frequency and high-frequency parts to be examined at different times and frequency resolutions .

Journal bearings generate highly non-stationary AE signals as they have been studied on component test rigs that try to mimic critical conditions of planetary gearbox journal bearings. The non-stationary AE signals originate from transient wear events such as two-body abrasion, adhesion, or three-body abrasion caused by particle contamination. Time–frequency methods such as the continuous wavelet transform (CWT) are particularly well suited to capture these events adequately. Unlike traditional methods that rely on statistical descriptors or STFT, the CWT maintains high resolution in both time and frequency domains and enables the localization of specific frequency components over time. This is particularly advantageous for detecting the onset and evolution of wear modes in journal bearings.

Recent studies have demonstrated that AE signals from running in, oil starvation, and particle-contaminated lubrication exhibit distinct 40–700 kHz time–frequency signatures. Figure  shows CWT of AE signals recorded under four different operating conditions of a journal bearing, namely hydrodynamic operation, running in, oil starvation, and particle-contaminated lubrication .

While each condition yields characteristic signal patterns in the 40–700 kHz range, the overall morphology of the CWT spectrograms shows significant overlap between certain operating states, particularly between running in and oil starvation. As a result, a reliable manual classification of these time–frequency representations is nearly impossible. Human observers cannot consistently distinguish subtle yet relevant spectral differences across large datasets. Therefore, ML models are required – specifically, deep convolutional neural networks – trained on labelled CWT spectrograms. These models have been shown to effectively learn discriminative features and achieve classification accuracies above 80 % .

The previous sections show that AE is a highly sensitive method for detecting early damage mechanisms in bearings. AE is particularly suitable for monitoring non-stationary and transient processes such as abrasion. Manual data evaluation of the AE signals by human observers is hardly practicable, as the spectral patterns of different operating states sometimes overlap considerably. Integrating ML methods, such as CNNs, opens new perspectives for the automated classification of AE signals in the form of time–frequency information. Existing challenges must be solved before AE technology can be used in field applications. Real-time processing requires powerful signal processing hardware and robust software solutions to process large amounts of data reliably. At the same time, the analysis algorithms must be robust against interference signals, varying environmental conditions and structural differences in the systems to avoid misclassifications.

Statistical-model-based approaches aim to determine the presence of a specific fault using available condition monitoring information . These methods utilize specific models or distributions, along with their parameters, to formulate hypotheses regarding fault presence. Subsequently, test statistics are developed to summarize condition monitoring data, facilitating a decision on whether to accept or reject fault prediction . Statistical model-based approaches include various methods that aim to estimate the RUL of equipment or components. According to , these approaches consist of three models: stochastic filter models, stochastic process models, and similarity-based models. Stochastic filter models, such as the Kalman filter and particle filter, combine noisy measurements with probabilistic models to refine state estimates in real time, addressing uncertainties in both the system and measurement noise . Variants like the extended Kalman filter and the unscented Kalman filter tackle non-linear challenges, while advances such as the unscented particle filter enhance accuracy . Stochastic process models describe equipment degradation using stochastic processes to estimate RUL by identifying when degradation first reaches a failure threshold. Unlike time-series models, they provide probability distributions for RUL predictions, capturing uncertainty in the forecasts . Examples include Wiener process models , gamma process models , inverse Gaussian models , and Markov models . Similarity-based models estimate RUL by comparing degradation trajectories of a target system with reference systems using historical failure data. This method constructs health indicators and performs similarity matching, proving effective when detailed degradation mechanisms are not available .

Traditional statistical analysis methods, which rely on signal processing technology and specific expert knowledge, struggle to address the complexities of mechanical systems . In contrast, AI approaches are taking a leading role by offering innovative solutions for the effective RUL prediction of mechanical equipment . Unlike traditional physical or statistical models, AI methods eliminate the necessity for precise modelling while effectively managing the complexities associated with degradation prediction in dynamic systems. categorize AI approaches into two methods: shallow learning and deep learning. Shallow learning algorithms, such as ANNs, SVMs, and relevance vector machines, have demonstrated reliable performance in RUL prediction tasks. For instance, one of the earliest studies to utilize an AI approach was conducted by , who developed an RUL prediction model for thrust ball bearings (inner diameter = 3.96 mm) using ANNs based on vibration-based signals. While these algorithms are particularly effective for smaller datasets and specific applications, they may face challenges such as difficulties with parameter tuning. Shallow learning focuses on feature extraction and making predictions from data without requiring deep hierarchical structures, which makes these algorithms faster and easier to train compared to deep learning models . Deep learning algorithms, including CNNs and recurrent neural networks (RNNs), along with their variants such as LSTM, excel in capturing high-dimensional features and temporal correlations within large datasets . These methods provide improved predictive accuracy and adaptability for complex mechanical systems. For example, utilized CNNs to establish a direct relationship between condition monitoring data collected from approximately 21 sensors, such as total temperature and pressure at the fan inlet, and RUL for aero-engines.

The detailed classification of ML-based approaches has been thoroughly discussed in the preceding sections. Building upon this classification, we now summarize the commonly employed methods and relevant studies in the context of gearbox fault/failure diagnosis.

In the category of shallow learning, supervised learning is mostly used for classification, with common methods applied for gearbox fault/failure diagnosis such as kNN , SVM , random forests , and logistic regression . Unsupervised learning mainly addresses data dimensionality reduction and clustering. Key algorithms for dimensionality reduction include principal component analysis , t-distributed stochastic neighbour embedding , and linear discriminant analysis . Clustering methods often include partition-based clustering such as k-means , density-based clustering such as density-based spatial clustering of applications with noise , and model-based clustering such as the Gaussian mixture model . Furthermore, recent studies have explored the application of reinforcement learning in diagnostics. For instance, in these studies , the diagnostic problem is transformed into an optimization problem, where reinforcement learning is employed to automatically learn optimal classification policies. Deep learning methods for diagnosing wind turbine gearboxes can be broadly categorized as follows: multilayer perception as a basic neural network structure ; CNNs, which are effective for handling data with a clear grid structure (e.g. images) ; RNNs, which specialize in processing sequential data ; transformer networks, which leverage self-attention mechanisms to process sequential data ; autoencoders, which use unsupervised learning to reconstruct input data, aiding in feature extraction and dimensionality reduction ; and generative adversarial networks, consisting of a generator and discriminator for data generation and augmentation . In addition to shallow and deep learning, ensemble learning (including stacking, boosting, and bagging) leverages multiple compatible ML algorithms to perform a single task, enhancing diagnostic performance and reducing overfitting. For example, in , the authors proposed a hybrid ensemble method integrating boosting, bagging, and stacking techniques to identify multi-component faults in a wind turbine gearbox. In the context of digitalization, transfer learning has also been applied to wind turbine gearboxes to improve fault diagnosis performance across different turbines and operational conditions, thereby strengthening generalization and advancing the realization of digital twin technologies. For instance, employed three feature-based transfer learning methods to minimize data distribution discrepancies between wind turbines and applied these methods in case studies for fault detection in two 2 MW wind turbines. proposed a novel method for early fault detection and diagnosis in wind turbines suited for data-limited scenarios, achieving accurate and transferable fault diagnosis. The proposed method was validated using vibration data from actual wind turbine gearboxes.

Wind turbine gearbox condition monitoring using vibration analysis can be broken down into monitoring the three key components: shafts, gears, and bearings. Each component exhibits unique fault modes and requires specific signal pre-processing and analysis techniques.

Shafts. Shafts are typically well designed and robust but can experience significant issues that may lead to catastrophic failures. As the critical link between rotating components, shaft failures can arise from unbalance, misalignment, bending, or cracks. These defects often manifest through increased amplitude at the shaft frequency or its harmonics over time.

An imbalance in rotating machinery can cause mechanical wear, reduced efficiency and failures if not promptly addressed. It occurs when mass distribution in the rotor is uneven, inducing excessive vibrations and stress on bearings. Traditional methods for detecting imbalances focus on vibration analysis, where deviations from expected patterns indicate the issue. Recently, advanced techniques, including frequency spectrum analysis and ML algorithms, have improved the precision of unbalance detection .

Shaft misalignment, which can occur in coupled shafts, also affects vibration signatures, particularly at the second and fourth harmonics of shaft speed. However, some studies report no direct correlation between misalignment and increased second harmonic . Vibration-based methods are widely employed for detecting unbalance and misalignment by analysing the machine's vibration signature .

Although less common, cracks in shafts can manifest as increases in shaft harmonic amplitudes in vibration spectra . However, small cracks may not significantly alter vibration characteristics. Severe cracks, although dangerous, may only influence vibration patterns once they are sufficiently large . Detection methods using vibration analysis, often combined with acoustic emission techniques, are commonly used for condition monitoring . Despite advances in monitoring techniques, there remains substantial potential for developing more effective tools for the early detection of shaft faults as rotating machinery grows more complex.

Oil debris monitoring refers to the continuous identification and trending of gearbox debris, including ferrous and non-ferrous, shed from contacting surfaces of these components or trapped through contamination and carried in the oil using an oil debris sensor. Whenever a ferrous or non-ferrous particle larger than a certain size passes through the sensor, the magnetic field formed inside the sensor is disturbed and an electric pulse is generated and counted. The counts over time represent the cumulative damage that occurred to the monitored components (e.g. bearings or gears inside wind turbine gearboxes). The oil debris sensor can be installed in the main filtration loop or the side-stream (or kidney) filtration loop (relatively smaller bore size, slower flow rate, and lower oil pressure than main filtration loop) permanently, either before or after the oil pump, but always before the filter. The minimum detectable ferrous wear debris size for this type of sensor can be down to 100 µm and, for non-ferrous wear debris, down to 300 µm, if installed in the main filtration loop. If in the kidney loop, these can be reduced to about 30–50 µm for ferrous debris and 130–150 µm for non-ferrous debris. Measurements of both ferrous and non-ferrous wear debris can be grouped into different size bins and trended, based on which levels of alarms can be generated. The data can be processed alone or integrated with a vibration analysis system, offering more comprehensive monitoring of the gearbox. With the advanced computing and modelling capabilities brought by AI/ML or digital twins, there are opportunities to gain additional benefits from oil debris monitoring.

The detection of external damage through visual measurements or inspection has until recently relied on human experience in the field with technicians climbing up the outside or within the structure of a blade to take photographic images and inspect damage. However, ML algorithms have been developed to automate visual damage detection . This, aided by the advent of low-cost uncrewed aerial vehicles or drones, has opened up the field of detailed visual inspection and damage detection in wind turbine blades . These techniques can be effective in detecting and locating external damage but are not so useful in detecting internal blade problems. Moreover, they generally require the turbine rotor to be stationary while a drone is flown around the turbine to record images.

Thermographic imaging, using infrared cameras, can be useful for detecting both external and internal blade anomalies. Damage within a blade such as cracking or de-bonding leads to thermal discontinuities. When the structure of the blade is heated by the sun or cools at night, this can lead to “hot spots” around the discontinuities, which can be detected by a thermal imaging device close to the blade. Furthermore, if the blade is cyclically stressed during operation, the hot spots may be enhanced. By appropriate analysis of the infrared images, different types of damage can be detected . There are challenges in using this technique in the field, which require some degree of image processing , but ML techniques offer a potential way to enhance the detection of damage . Changes to the surface of a blade due to damage such as erosion lead to changes in the characteristics of the flow. This gives rise to turbulence patterns which can be imaged in the infrared spectrum using a thermal camera. The detection of these patterns can be automated using AI techniques .

Damage to the structure of a blade can give rise to changes in the local stiffness of the surrounding material. Under loading, this can change the local deformation and thus the strain experienced. Measurements of the strain at targeted locations along the blade where damage might be expected to occur could then be used as a way to detect damage. Such measurements can be made using foil strain gauges whose resistance change is related to the local change in strain. This can be measured electrically with a suitable bridge circuit or fibre Bragg diffraction gratings (FBGs) etched at periodic distances within a fibre glass filament attached to the surface of a blade. When a light source is passed down the filament, each grating passes all but a very narrow band of frequencies that depend on the strain local to the FBG. These are back-reflected and can be measured using a suitable optical detection system. Each FBG is designed to reflect within a unique frequency range so that strain at different locations along the blade can be made simultaneously. Foil or FBG-based strain measurement systems have been shown to be able to detect local blade damage . The drawbacks of such systems is that the strain gauges are also sensitive to temperature change so that some form of temperature compensation measurement is required, and the strain gauges will only detect damage if it causes changes to the strain close to the location of the gauge. The latter limitation can be overcome to some extent by using strain measurements along the blade to look at changes to the characteristic mode shapes of the blade, which can change in response to localized damage . It is nonetheless still challenging to infer the location of damage using this approach.

Cracks in the material of a blade resulting from damage when stress is applied to the structure release energy that is dissipated through the structure in the form of elastic waves with frequencies in the ultrasonic range. By the placing of appropriate acoustic sensors on the surface of the blades, these waves can be detected . By using multiple sensors, the signals can be triangulated to locate the origin of the crack-induced sound waves . By appropriate analysis of the signals received using time-domain or frequency-domain techniques, it is possible to differentiate different types of damage such as fibre breaking, matrix cracking, and de-bonding . This technique requires the placing of sensors within the blade, which may not be straightforward if done post-construction and once the blade is in service.

Three types of blade monitoring using microphones have been tested. The first is an active technique where speakers are placed within a blade and the sound emitted is measured using external microphones . This relies on the sounds being modulated or less attenuated by the presence of cracks in the blade structure. The second is a passive technique where microphones are placed within the blade . This relies on changes to the sound detected being driven by wind flow over the blade as it moves over or through cracks in the structure. These first two techniques require equipment to be placed within the blade and therefore are challenging to be used at scale in the field. A third, which is entirely remote, is a passive aeroacoustic measurement technique that uses remote microphones only and measures changes to the far-field sound spectrum resulting from changes to the wind flow over a blade, which may be modified by cracks (e.g. trailing edge) or surface damage such as erosion . This technique has been tested under laboratory conditions on a small scale but is yet to be tested in the field.

The use of ultrasonic guide waves for blade monitoring employs a transducer attached to the blade surface, which produces ultrasonic pulses that travel within the laminate structure and whose characteristics can be measured at another location on the blade using a detector. The ultrasonic wave propagation is affected by cracks in the surface material or dirt on the blade surface. By suitable signal processing and the use of ML, the received pulses can be used to detect dirt build-up or damage in the blade material . This technique can detect damage relatively close to the position of the receiver (a few metres) but requires the receiver to be moved over the blade surface for effective monitoring. Furthermore, as the propagation of the waves is restricted with the confines of the material sheet being examined, it may not be suitable for monitoring damage further inside the blade, e.g. within the spar.

The characteristic mode shapes of a blade, as mentioned in Sect. , can be used as a way of detecting damage as they can be modified due to changes in stiffness resulting from cracks, for example. Aside from using strain gauges, instrumenting the blades with accelerometers or scanning the structure with a laser can characterize vibration along the blade's length when excited by a surface-mounted actuator or wind loading. By processing the data, it is then possible to infer the blade vibrational modes and monitor changes over time. In the case of a laser, a common technique to measure the motion of the blade is to measure the Doppler shifting of the laser light after it is back-scattered off the blade surface to a detector. Laser Doppler vibrometry has been used, for example, to detect delamination in a small-scale model blade in the laboratory, using wavelets to process the data . Changes to the mode shapes were seen, which allowed damage to be detected and localized but changes were small. In addition, changes could only be seen where the largest movements related to a mode shape were observed. This would represent a challenge if damage were located close to a mode-shape node where levels of vibration are small.

The use of X-rays to scan the interior of wind turbine blades to detect damage is quite new. Two drones are flown in tandem where one generates a beam of X-rays which are focused on the blade, and the second drone has an X-ray detector. Commercial systems have been developed such as SpectX , which uses AI techniques to process X-ray images to provide a scanned image of the interior of a blade and to localize potential sources of damage. A similar idea has been developed to inspect lightning conductors in blades that may have been damaged by a strike . The use of terahertz waves, or T-rays, which have widely been used in food safety, package imaging, and airport security, has been investigated in the laboratory for blade imaging . Similar to X-rays, the system uses a source of T-rays which is projected through the blade, and the resulting transmitted radiation is detected by an imaging system. The system was tested for scanning the trailing edge of a blade in the laboratory but so far has not been used to detect damage.

Although there are a multitude of methods that can potentially be used for wind turbine blade monitoring, many of them are, as yet, impractical to use in the field or not tested at full scale. At present, the most promising are using drones for visual, infrared, and X-ray inspection. In the future, we are likely to see the use of a combination of different technologies and a preference for those technologies that can be used remotely rather than require sensors attached to the blades. AI will be used increasingly to process and interpret the data from multiple scanning technologies to provide the best estimate of the health of a blade and its potential RUL.