Fault Detection of Wind Turbine Planetary Epicyclic Gears Using Adaptive Empirical Wavelet Decomposition Based Hybrid Features

Abstract

Vibration based analysis is a proven technique in condition monitoring of rotating machinery. Particularly in wind turbines, incipient fault detection based on vibration signature analysis plays a vital role in reliable operation and maintenance planning. The synergistic effect of non-stationary Vibration signals due to wide variation in wind speed and non-linearity due to inherent turbine load dynamics is a major challenging issue. Extraction of fault signatures from the non-stationary vibration depends on an efficacious decomposition of multi components into mono component intrinsic mode functions. In this work, an Adaptive Empirical Wavelet Decomposition (AEWD) based feature extraction technique with Quadratic Kernel Function Support Vector Machine (QKSVM) classifier is proposed to detect mechanical faults in Wind Turbine Planetary Epicyclic Gears. Instantaneous Amplitude and Frequency components are reconstructed from Empirical wavelet coefficients. Sample Entropy, Permutation Entropy, Signal descriptors (RMS, Peak, and Crest Factor) and Statistical moments are extracted to frame the hybrid feature space. Correlation analysis of hybrid features clearly shows non-linear distribution. The performance of proposed AEWD–QKSVM is analyzed with practical Wind Turbine Gearbox Condition Monitoring Vibration Analysis Benchmarking Datasets. The results are proven 91.5% accuracy with 2000 time segmented sampling vibration signals in scuffing fault detection in planetary stage with good performance index as 90% sensitivity and 93% specificity in fault detection.