关键词: 自适应, 流形学习, 集合经验模式分解, 双谱分析, 纹理特征构造, 故障诊断

Abstract:

For the problem of rolling bearing fault type and damage degree with manual intervention, a new fault diagnosis method based on Self-Adaptive Manifold Learning（SAML） is proposed. This algorithm extracts fault features of vibration signal by means of Ensemble Empirical Mode Decomposition（EEMD） and bispectrum analysis, constructs texture feature matrix of fault information by texture analysis method, and reduces the dimension of high-dimensional texture feature matrix by adaptive manifold learning method. The whole process can remove noise well, select parameters adaptively, and have good clustering performance and complex signal processing ability. The experimental results show that this method can distinguish different fault types well, and have a good classification performance in judging the fault degradation degree of inner ring fault, outer ring fault and rolling element fault.

Key words: self-adaptive, manifold learning, ensemble empirical mode decomposition, bispectrum analysis, texture feature construction, fault diagnosis