Bearing Fault Diagnosis Based on Compressed Data and Supervised Global-Local/Nonlocal Discriminant Analysis

A three-stage bearing fault diagnosis method based on compressed data and supervised global-local/nonlocal discriminant analysis (SGLNDA) is proposed. In the first stage, compressed sensing is used to reduce the burden of storage. The compressed data can be obtained from the original vibration signals for subsequent fault diagnosis. In the second stage, a new manifold learning algorithm, namely SGLNDA is used to map the compressed data to low-dimensional space and retain its global and local/nonlocal discrimination information. In the third stage, the low-dimensional features obtained in the previous step are used as inputs of support vector machines to recognize fault diagnosis. The experimental results show that the proposed method can shorten the diagnosis time and obtain high diagnosis accuracy.