Nonlinear convolutional sparse filtering with Gaussian fitting for rolling bearings compound fault diagnosis

Abstract

The operational precision, stability, and lifespan of servo motors are directly influenced by the healthy operation of rolling bearings. Early diagnosis of bearing faults holds significant importance in enhancing the reliability of servo motors and preventing substantial economic losses and personal injuries. However, strong noise interference in industrial environments poses challenges to bearing fault diagnosis. Additionally, the difficulty in detecting single faults in bearings often leads to the occurrence of compound faults. To achieve compound fault diagnosis under intense noise interference in industrial environments, this paper proposes the nonlinear convolutional sparse filtering (NCSF). First, generalized sigmoid and Z-score normalization are utilized to amplify fault features and reduce the influence of irrelevant interference. Subsequently, multidimensional filters are learned based on a nonlinear objective function to achieve the separation of compound faults. Then, the Gaussian function is utilized to fit the trained filters, reducing the impact of low-frequency noise. Finally, the filtered signals are envelope demodulated to analyze fault types. The capability of NCSF to distinguish compound faults amid noise interference is confirmed by both experimental and simulation results. The proposed NCSF is an effective method to realize compound fault diagnosis under strong noise interference.