Piecewise Hybrid System with Cross-Correlation Spectral Kurtosis for Fault Diagnosis in Rolling Bearing of Wind Power Generator

Abstract

As the core equipment of wind turbines, rolling bearings affect the normal operation of wind power generators, resulting in huge economic losses and significant social impacts in the case of faults. Most faults are not easily found because of the small vibration response of these rolling bearings that operate in harsh conditions. To address the problem that the fault identifications of rolling bearings are disturbed by the strong noise in wind power generators, an adaptive nonlinear method based on a piecewise hybrid stochastic resonance system with a novel cross-correlation spectral kurtosis is proposed. Then, the vibration signals collected from the fault point of the outer and inner rings are used to clarify the outstanding capability of the proposed method when compared with the maximum cross-correlation-kurtosis-based unsaturated stochastic resonance method. Furthermore, the machine learning method based on the medium tree was adopted to further prove the excellent performance of the piecewise hybrid stochastic resonance system with a novel cross-correlation spectral kurtosis for realizing the efficient detection of rolling bearing faults in wind power generators, which has important innovation significance and practical engineering value for ensuring the safe and stable operation of wind turbines.

Review of Wind Power Bearing Wear Analysis and Intelligent Lubrication Method Research

“…[22,23]. These bearings play a vital role in the regular operation and efficient generation of electricity from wind turbines [24]. According to the statistics, a complete set of onshore wind turbine units will typically require the use of 26 sets of bearings [25].…”

Research on Rolling Bearing Fault Diagnosis Method Based on Simulation Source Domain to Experimental Target Domain with Improved Alternating Transfer Learning

“…However, these methods often heavily depend on prior knowledge [11] and often require adjustments to calculation parameters for specific diagnostic tasks, making them less generalizable [12]. In recent years, intelligent bearing fault diagnosis based on deep learning, with convolutional neural networks as a representative method, has enabled automatic feature extraction for fault diagnosis [13][14][15][16][17][18][19][20]. Li et al [21] for example, combined the Improved Particle Swarm Optimization algorithm and Improved Envelope Entropy with Support Vector Machines to develop the Improved Grey Wolf Optimization-SVM, which effectively diagnoses strong noise-related rolling bearing faults with high accuracy.…”