Wind Turbine Gearbox Fault Detection Using Multiple Sensors With Features Level Data Fusion

Abstract

Fault detection in complex mechanical systems such as wind turbine gearboxes remains challenging, even with the recently significant advancement of sensing and signal processing technologies. As first-principle models of gearboxes capable of reflecting response details for health monitoring purpose are difficult to obtain, data-driven approaches are often adopted for fault detection, identification or classification. In this paper, we propose a data-driven framework that combines information from multiple sensors and fundamental physics of the gearbox. Time domain vibration and acoustic emission signals are collected from a gearbox dynamics testbed, where both healthy and faulty gears with different fault conditions are tested. To deal with the nonstationary nature of the wind turbine operation, a harmonic wavelet based method is utilized to extract the time-frequency features in the signals. This new framework features the employment of the tachometer readings and gear meshing relationships to develop a speed profile masking technique. The time-frequency wavelet features are highlighted by applying the mask we construct. Those highlighted features from multiple accelerometers and microphones are then fused together through a statistical weighting approach based on principal component analysis. Using the highlighted and fused features, we demonstrate that different gear faults can be effectively detected and identified.