Bispectrum analysis for feature extraction of pitting fault in wind turbine gearbox

This paper discusses the theory of higher-order statistical analysis and its application in gear pitting fault feature extraction from gearbox vibration signals analysis of a large scale wind turbine generator system (WTGS). The bispectrum was used to inhibit the Gaussian noise in measured vibration signals and to reveal the fault related non-Gaussian information. We propose to divide the dual-frequency plan of bispectrum into several partitions and use the total amplitude value of each partition, which related to the non-Gaussian intensity of vibration signals, as feature values for identification of pitting fault. It can be seen by comparing the results between pitting fault and normal condition that the proposed method are effective for the extraction of gear pitting fault information from noised vibration signals and bring stable performance, high sensitivity.