A bearing fault detection method with low-dimensional compressed measurements of vibration signal

The traditional bearing fault detection 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 is used in fault detection. 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 detection 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 detection. Firstly, an over-complete dictionary is trained, on which the vibration signals corresponded to normal state can be decomposed sparsely. Then, the bearing fault detection can be achieved based on the difference of the sparse representation errors between the compressed signals in normal state and fault state on this dictionary. The fault detection results of the proposed method with different parameters are analyzed. The effectiveness of the method is validated by the experimental tests.