Fault Diagnosis of Wind Turbine Gearbox Based on Least Square Support Vector Machine with Genetic Algorithm

Abstract

Gearbox affect the normal operation of the wind turbines, to study the fault diagnosis, support vector method was used. Parameters selection is very important and decides the fault diagnosis precision. In order to overcome the blindness of man-made choice of the parameters in least squares support vector machine (LSSVM) and improve the accuracy and efficiency of fault diagnosis, a method based on LSSVM trained by genetic algorithm was proposed. This method searches the optimized parameters in LSSVM by taking advantage of the genetic algorithms powerful global searching ability. The research is provided using this method on the fault diagnosis of wind turbine gearbox and compared with the diagnostic method of LSSVM. The experimental results show that the method achieves a higher diagnostic accuracy.

Fault Diagnosis of Rolling Bearings Based on WPE by Wavelet Decomposition and ELM

“…It provides a ‘time-frequency’ window that varies with frequency. In the area of pattern recognition, the main methods include back propagation (BP) neural networks [ 7 ], support vector machines [ 8 ], and deep learning [ 9 ]. BP neural network algorithms are slow to converge, whereas deep learning requires a large number of samples and takes too long to train.…”