Performance degradation assessment of wind turbine gearbox based on maximum mean discrepancy and multi-sensor transfer learning

Abstract

Gearboxes are critical transmission components in the drivetrain of wind turbine, which have a dominant failure rate and the highest downtime loss in all wind turbine subsystems. However, load variations of wind turbine gearbox are far from smooth and usually nondeterministic, which result in inconsistent data distributions. To solve the problem, a novel performance degradation assessment and prognosis method based on maximum mean discrepancy is proposed to test the difference between data distributions and extract the characteristics of multi-source working conditions data. Besides, the increase in sensors will bring more difficulties to establish prediction models in real-world scenarios due to different installation locations. In view of this, a transfer learning strategy called joint distribution adaptation is utilized to adapt data distribution between multi-sensor signals. Nevertheless, the presence of background noise of wind turbine signals restricts the applicability of these algorithms in practice. To further reduce the distribution difference, a novel criterion is proposed to evaluate and measure the data distribution difference between known and tested working conditions based on the witness function of maximum mean discrepancy. The application and superiority of proposed methodology are validated using a wind turbine gearbox life-cycle test data set. Meanwhile, model comparison and cross-verification are conducted between conventional and proposed prediction models. The results indicate that the proposed method has a better performance in performance degradation assessment for wind turbine gearbox.

Scientometric review of artificial intelligence for operations & maintenance of wind turbines: The past, present and future

“…However, to the best of our knowledge, there has been scarce application of transfer learning towards predicting faults in turbines, which is an integral aspect of O&M. The only works in this area either focus on prediction of faults in different turbine sub-components [3] or monitoring vital parameters e.g. of the gearbox to identify deviation from normal behaviour towards fault prediction [149].…”

Estimation of Wind Turbine Performance Degradation with Deep Neural Networks

“…There are several reasons for this age-related performance degradation. The mechanical wear and tear of various components over time could affect the turbine's performance, reliability, as well as efficiency (Hamilton, Millstein, Bolinger, Wiser, & Jeong, 2020;Pan, Hong, Chen, Feng, & Wu, 2021). Another major reason for this declining performance is the reduction in aerodynamic efficiency due to material erosion over the blade tips (Sareen, Sapre, & Selig, 2014;Zweiri et al, 2022).…”

Data-driven dictionary design–based sparse classification method for intelligent fault diagnosis of planet bearings

“…Planetary drivetrains have been widely applied among many industrial applications including the helicopter 1 and wind turbine 2 transmission systems, because of their strong load-bearing capacities and high transmission ratios in a relatively compact space. However, due to the harsh operating conditions, planetary drivetrains are easily subjected to component damages, which causes unexpected failures to the transmission systems and costly maintenance expenses.…”