Resonance Detection Method and Realization of Bearing Fault Signal Based on Kalman Filter and Spectrum Analysis

Abstract

The rolling bearing is an important part of mechanical equipment, and its performance significantly affects the quality and life of the mechanical equipment. This article uses the integrated fiber Bragg grating resonant structure sensor excited by periodic micro-shocks caused by micro faults to realize the extraction of information relating to potential faults. Because the fault signal is weak and can easily be interfered with by ambient noise, in order to extract the effective signal, this article determines the autoregressive model of bearing vibration by the final prediction error criterion and the recursive least squares estimation algorithm. The augmented state space model is established based on the autoregressive model. A Kalman filter is used to reduce the noise interference, and then the reduction noisy signal is analyzed by power spectrum and improved autocorrelation envelope spectrum to realize the detection of bearing faults. Through data analysis and method comparison, the proposed improved autocorrelation envelope spectrum analysis can directly extract the bearing fault frequency, which is superior to other methods such as cepstral analysis.

Synthesis of the structural diagram with algorithms of the units of the on-board diagnostic system of induction motors of vehicles

“…Control of the condition of the bearings in the conditions of the built-in diagnostic system can be carried out by one of the following main methods [10,56]: -peak factor; -by the spectrum of the envelope of the vibration signal; -by spectral analysis of vibration signals.…”

“…When diagnosing bearings, the method of spectral signal analysis is most often used. To reduce noise interference when diagnosing bearings based on vibration spectra, a Kalman filter is used [10]. Diagnosis of early motor bearing damage can be performed using an intelligent algorithm based on meta-learning with generation of missing samples and enhancement of weak features at the Wasserstein distance [38].…”