Combination of Thermal Modelling and Machine Learning Approaches for Fault Detection in Wind Turbine Gearboxes

Abstract

This research aims to bring together thermal modelling and machine learning approaches to improve the understanding on the operation and fault detection of a wind turbine gearbox. Recent fault detection research has focused on machine learning, black box approaches. Although it can be successful, it provides no indication of the physical behaviour. In this paper, thermal network modelling was applied to two datasets using SCADA (Supervisory Control and Data Acquisition) temperature data, with the aim of detecting a fault one month before failure. A machine learning approach was used on the same data to compare the results to thermal modelling. The results found that thermal network modelling could successfully detect a fault in many of the turbines examined and was validated by the machine learning approach for one of the datasets. For that same dataset, it was found that combining the thermal model losses and the machine learning approach by using the modelled losses as a feature in the classifier resulted in the engineered feature becoming the most important feature in the classifier. It was also found that the results from thermal modelling had a significantly greater effect on successfully classifying the health of a turbine compared to temperature data. The other dataset gave less conclusive results, suggesting that the location of the fault and the temperature sensors could impact the fault-detection ability.

Intelligent Condition Monitoring of Wind Power Systems: State of the Art Review

“…According to [103,104] one may observe several health levels of gears by taking into account different defects on gears teeth such as cracked, chipped, missing root, surface defect and healthy gears as addressed by Figure 5b. Additionally, bearing faults such as internal race faults could affect the mechanical transmission process of the drivetrain (Figure 5c) [105]. Under the operating conditions of harsh environments, each of these components could be affected by the high rotational speed of the high-speed shaft of the gearbox.…”

“…In the deep learning approach proposed by Cheng et al [104], a new learning path for fault classification (diagnosis) for gearboxes of dual-power induction generator WTs is designed depending on the current signal processing. As a new contribution in the gearbox fault diagnosis, Corley et al [105] used a thermal modeling method coupled with the ML technique to be able to strengthen the CM system of the WT. In the work of Fu et al [106], an efficient approach to select gearbox temperature measurements was adopted using an elastic neural network.…”

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“…This method reduced the FNR, but the optimization of the FPR was not obvious. Corley [34] combined thermal modeling and machine learning methods to detect gearbox faults. The method had less conclusive results, which may have affected the accuracy of fault detection.…”