Research and application of Volterra series theory in rolling bearing fault state feature extraction

Due to the generally strong non-linear characteristics of bearing failure, leading to overall mechanical system failure, fault state feature extraction is difficult. In this paper, a fault feature extraction method based on the Volterra series kernel under multi-pulse excitation is proposed. To avoid reliance on simplified models based on traditional mechanics, a nonlinear Volterra series model was constructed by introducing the input and output signals of the system, and using a low-order Volterra series kernel from the time domain and frequency domain, which was then solved using a multi-pulse excitation method. Furthermore, the state of the rolling bearing was determined using different characteristics of the corresponding generalized frequency response, and the current fault stage was inferred. The rolling bearing failure was validated experimentally, and it was shown that the Volterra series model can be more easily used to extract fault characteristics and trends of a rolling bearing in comparison to the traditional wavelet algorithm, therefore serving as a better method for fault prediction