A bearing fault diagnosis method based on the low-dimensional compressed vibration signal

The traditional bearing fault diagnosis method is achieved often by sampling the bearing vibration data under the Shannon sampling theorem. Then, the information of the bearing state can be extracted from the vibration data, which will be used in fault diagnosis. A long-term and continuous monitoring needs to sample and store large amounts of raw vibration signals, which will burden the data storage and transmission greatly. For this problem, a new bearing fault diagnosis method based on compressed sensing is presented, which just needs to sample and store a small amount of compressed observation data and uses these data directly to achieve the fault diagnosis. First, several over-complete dictionaries are trained by dictionary learning method using the historical operating data of the bearings. Each of these dictionaries can be effective in signal sparse decomposition for a particular state, while the signals corresponding to other states cannot be decomposed sparsely. According to this difference, the bearing states can be identified finally. The fault diagnosis results of the proposed method with different parameters are analyzed. The effectiveness of the method is validated by experimental tests.