Bearing Fault Diagnosis Based on State-Space Principal Component Tracking Filter Algorithm

Abstract

To obtain a high-precision bearing fault diagnosis of different speeds under intense background noise, a novel method called state-space principal component tracking filtering (SPCTF) is proposed for fault diagnosis. SPCTF can recover the fault information through the sequence of 'Prediction - Measurement - Correction - Optimal estimation - Principal component extraction' from the polluted raw signals with noise. Firstly, the switch Kalman filter (SKF) is utilized to establish a dynamic filter model for time-series signals. And then, a feature tracking matrix is generated from the principal component features extracted from the optimal estimated state matrix of each time in the model. Thirdly, the principal component analysis extracts the effective fault feature from redundant information generated in the feature tracking matrix. Finally, the fault characteristic frequency is analyzed with the envelope spectrum to achieve a high-precision fault bearing diagnosis. The proposed method is demonstrated on signals from simulation signals and laboratory tests (several speeds), outperforming traditional methods.