A Transient Fault-signal Extraction Scheme for Bearing Compound Fault Intelligent Diagnosis based on Vibration Signals

As a compound fault of bearing is characterized by complexity, disproportion, and interaction, its fault diagnostic accuracy tends to decline sharply. To solve this problem, the present study proposes a transient fault-signal extraction scheme for bearing compound fault intelligent diagnosis. First, the single fault vibration and compound fault vibration signals are transformed into the time-frequency domain by wavelet transform. Then, according to the normal condition signal, the transient fault signal of the single signal and compound signal is extracted through the positive k sigma principle. Next, the single fault signal symptom parameters are calculated to build the fault diagnostic model. Thereafter, the symptom parameters of the extracted compound fault transient signal are brought into the diagnostic model to obtain the model output result. Finally, according to the developed fault diagnosis discrimination criterion, the method can diagnose the compound fault successfully. The effectiveness of the proposed method is validated by bearing fault vibration signals under various conditions. The results show that the diagnostic method has superior performance in intelligently diagnosing the bearing compound fault.