Resonance frequency band analysis-based shock pulse method for quantitative characterization of bearing fault

The quantitative diagnosis of bearing failures is pivotal to ensuring equipment reliability and safety. The Shock Pulse Method (SPM) represents an effective technique for quantitative diagnostics. However, SPM relies on the resonance of specialized sensors, requiring additional sensors to be added to existing vibration monitoring systems, limiting its industrial application. To address the issue, this paper proposes a bearing fault quantitative diagnosis method based on existing vibration sensors. It selects the bearing system's resonance band from the signals collected by existing sensors and integrates it into the SPM, broadening its application. Firstly, a resonance band analysis method (RFBA) for the bearing system is proposed, which selects the bearing system's resonance band from the collected signals with weighted fusion indicators and improved sliding filters, replacing that of specialized sensors. The improved sliding filter aims to improve the stability and accuracy of band selection at low signal-to-noise ratios. Subsequently, RFBA is integrated with SPM, and the resonance band information obtained from RFBA is used as a bandpass filter in SPM. This forms the RFBA-based SPM method, which is applied to assess bearing conditions. Experimental results confirm the method's effectiveness in the quantitative diagnosis of rolling bearing faults, with comparisons highlighting its superiority over other approaches.