Preprints
https://doi.org/10.5194/wes-2022-111
https://doi.org/10.5194/wes-2022-111
01 Feb 2023
 | 01 Feb 2023
Status: a revised version of this preprint was accepted for the journal WES.

Anomaly-Based Fault Detection in Wind Turbine Main Bearings

Lorena Campoverde-Vilela, María del Cisne Feijóo, Yolanda Vidal, José Sampietro, and Christian Tutivén

Abstract. Renewable energy is a clean and inexhaustible source of energy, so every year interest in the study and the search for improvements in production increases. Wind energy is one of the most used and therefore the need for predictive maintenance management to guarantee the reliableness and operability of each of the wind turbines has become a great study opportunity. In this work, a fault detection system is developed by applying an anomaly detector based on principal component analysis (PCA), in order to state early warnings of possible faults in the main bearing. For the development of the model, SCADA (supervisory control and data acquisition) data from a wind park in operation are utilized. The results obtained allow detection of failures even months before the fatal breakdown occurs. This model requires (to be constructed) only the use of healthy SCADA data, without the need to obtain the fault history or install additional equipment or sensors that require greater investment. In conclusion, this proposed strategy provides a tool for the planning and execution of predictive maintenance within wind parks.

Lorena Campoverde-Vilela et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on wes-2022-111', Davide Astolfi, 13 Feb 2023
    • AC1: 'Reply on RC1', Yolanda Vidal, 02 Mar 2023
  • RC2: 'Comment on wes-2022-111', Anonymous Referee #2, 13 Feb 2023
    • AC2: 'Reply on RC2', Yolanda Vidal, 02 Mar 2023
  • RC3: 'Comment on wes-2022-111', Anonymous Referee #3, 17 Feb 2023
    • AC3: 'Reply on RC3', Yolanda Vidal, 02 Mar 2023

Lorena Campoverde-Vilela et al.

Lorena Campoverde-Vilela et al.

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Short summary
In order to provide early warnings of faults in the main bearing, a fault detection system is developed in this work by applying an anomaly detector based on principal component analysis. Without the need to obtain the fault history or install additional equipment or sensors that would require a larger investment, this model is constructed using only healthy SCADA data. The results obtained enable failure detection even months before the fatal breakdown takes place.