关键词: 滚动轴承故障, 奇异值分解（SVD）, 时域分析, 堆栈稀疏自编码器（SAE）

Abstract: In order to solve the problem that the feature information of rolling bearing fault is difficult to be extracted under high-dimensional data and the signal fault classification needs supervised training to achieve classification, a method is proposed based on Singular Value Decomposition（SVD）and time domain feature analysis and Stacked Sparse AutoEncoder（SAE） and Softmax classifier for classification of rolling bearing faults. This method uses Hankle matrix to reconstruct the original data, then singular value decomposition and time domain analysis is used to conduct feature preprocessing. The integrated features are used as the input of the SAE for feature optimization. The optimized features are entered into the Softmax classifier for classification recognition. The experimental results show that the recognition rate of ten types of data under three kinds of working conditions is above 96%. Compared with other methods, the recognition rate is improved. Therefore, this method can effectively perform feature preprocessing and classification of complex signals such as rolling bearings.

Key words: rolling bearing fault；Singular Value Decomposition（SVD）, time domain analysis;Stacked Sparse AutoEncoder（SAE）