Compound fault diagnosis of rolling bearing using PWK-sparse denoising and periodicity filtering

Since the noise in signals influences the diagnosis and separation of bearing compound faults, this study proposes a new method combining periodicity weighted Kurtosis (PWK)-sparse denoising (SD) with periodicity filtering (PF) to extract repetitive impulses of compound faults. The proposed method involves fault identification and fault separation. Firstly, since Kurtosis only measures transient feature and neglects periodic feature when evaluating repetitive impulses, to overcome this drawback, a new index, PWK, is proposed to adaptively select optimal regularization parameter in SD. Then, with PWK measuring repetitive impulses extracted by SD, PWK-SD is used to identify fault types. Subsequently, based on the fault types, the proposed PF separates the compound bearing faults. During separation processing, PF uses periodic feature of sparse coefficients selecting the relevant impulse atoms to separate mixed repetitive impulses. Simulation and experimental results indicate the effectiveness of the proposed method in diagnosing and separating compound bearing faults.