Dear editor,

I have completed a thorough review of the manuscript titled "Active Trailing Edge Flap System fault detection via Machine Learning." After careful evaluation, I would like to provide my feedback on this manuscript.

The manuscript analyzed an important topic in the wind turbine field/industry, which is leveraging machine learning methods to identify the health state of active trailing edge flaps (AFlaps) on wind turbines. The study explores two methods, namely the manual feature selection (MFS) and automatic feature selection (AFS) approaches. The ML models were trained and tested using simulated data.

Overall, the topic and analysis of the manuscript is of a great interest of the wind energy science/industry. The procedure and results of the research is well organized and presented. The performance of the two methods and final conclusions are also well delivered.

However, I have several concerns that need to be addressed before considering the manuscript for publication:

• Lack of clarity regarding the problem addressed: The abstract and introduction do not clearly articulate the specific real-world problem or practical application that the research work aims to solve. I can see that the main goal of the two models is to predict the health state of AFlaps with the accessible signals without installing additional sensors. However, this main goal is not stated in the abstract. Also, the current stage (e.g., the performance of similar research on this topic) of related research effort is not presented in the introduction. I recommend providing a clear statement of the problem, presenting the previous reported research, and highlighting the potential impact or benefit of solving it.
• Incomplete analysis of AFS method performance: As reported in the manuscript, the AFS method works very well for the AF_Off and AF_Off_Fault cases, which shows the potential of the AFS method in accurately distinguishing the flap on-off states. In this case, people might wonder why the AFS cannot do well in the opposite cases (i.e., AF_On and AF_On_Fault) which basically differ from the AF_Off and AF_Off_Fault cases only by the Flap actuator control signal. I recommend having further analysis/investigations to answer the following questions: i) What are the potential reasons why the model works worse on the AF_On and AF_On_Fault cases? ii) Will putting the Flap actuator control signal as a feature into the AFS method increase the performance?
• Potential improvement: As reported in the manuscript, the MFS method showed reliable classification for asymmetrical flap faults but struggled with symmetrical flap faults, and the AFS method had limited success in both cases but outperform in two special symmetrical cases. I recommend having an investigation on integrating the two model into a hybrid model since combining different models/techniques can often lead to improved performance and enhanced capabilities, leveraging the strengths of each individual method.

I believe addressing these concerns and incorporating the suggested improvements will strengthen the manuscript and enhance its contribution to the field. Overall, the research work has done a great contribution to advance the understanding of fault detection in active trailing edge flap systems and improve the reliability of wind turbine operations.

Thank you for considering my feedback. I am available for any further clarifications or discussions.

Sincerely

The paper deals with a very interesting topic and is appropriate for the scientific objectives of the journal.

The objective of the work is stated extremely clearly. The quality of the work is high, the methods are applied rigourously. The presentation is excellent.

Therefore, my recommendation is to accept this paper upon the following minor revisions:

• I am interested in a few further details about the type of employed data. What is the time resolution of the data?
• Do the authors think that the same kind of objective can be accomplished for on site wind turbine with standard SCADA data averaged on ten minutes?  Could you please elaborate a little more on this?