灰度矩向量, 最小二乘支持向量机(LSSVM), 极大提升形态小波(MLMW), 滚动轴承

A fault diagnosis method for rolling bearing was proposed based on MLMW denoised CWT gray moment vector-LSSVM.Sampled signals were denoised by MLMW, and the CWT scalogram of denoised signals was partitioned into several areas, whose gray moment were calculated to form a gray moment vector that was used as the input of LSSVM for fault classification. The experimental results show fault features of MLMW denoised signal’s scalogram is clearer and more distinctive than original,and its gray moment vector can describe bearing condition effectively.With the number of scalogram partitions increasing,the diagnosis accurate rate increases.Compared with diagnosis methods of original gray moment vector-LSSVM and wavelet denoised gray moment vector-LSSVM,the proposed method has higher accuracy,better generalization and requires fewer training samples,and can identify bearing fault patterns accurately.

gray moment vector, least square support vector machine(LSSVM), max-lifting morphological wavelet(MLMW), rolling bearing