FIR noise reduction using GWO and Fast Kurtogram for extracting fault characteristic frequencies in rolling bearings

Abstract Fast Kurtogram (FK) is a widely recognized method for detecting characteristic fault frequency bands. Nevertheless, its sensitivity to noise often causes diagnostic errors. This research seeks to enhance FK’s identification performance by implementing an adaptive FIR filter that mitigates noise. To validate this approach, single-point fault signals were obtained from the outer race, inner race, and rolling elements of bearings operating at four distinct motor speeds. These raw signals were then subjected to statistical analysis after normalization. To design the adaptive FIR filter, the study employs an enhanced Grey Wolf algorithm along with the Parks-McClellan algorithm to determine the optimal filter order and impulse response coefficients. Subsequently, band-pass filtering is applied to the fault characteristic frequency bands identified through FK. The fault characteristic frequencies are then extracted using envelope spectrum analysis. The bandwidth and fault characteristics derived from the processed signals are compared against those obtained from the unfiltered original signals. The findings indicate that optimal normalization preprocessing is crucial before filter design. The determined optimal order and impulse response coefficients enable the adaptive design of FIR filters. As a result, the FIR-filtered signal exhibits significantly fewer noise-induced anomalous peaks, thereby improving the signal-to-noise ratio and enhancing FK’s robustness against noise interference. Implementing an adaptive FIR filter for noise reduction significantly aids in more precise analysis and diagnosis of bearing conditions. Looking ahead, this study proposes integrating machine learning algorithms to further enhance the accuracy in identifying characteristic frequency bands associated with single-point faults in rolling elements.