Automatic fault diagnosis of rotating machines by time-scale manifold ridge analysis

Abstract

This paper explores the improved time-scale representation by considering the non-linear property for effectively identifying rotating machine faults in the time-scale domain. A new time-scale signature, called time-scale manifold (TSM), is proposed in this study through combining phase space reconstruction (PSR), continuous wavelet transform (CWT), and manifold learning. For the TSM generation, an optimal scale band is selected to eliminate the influence of unconcerned scale components, and the noise in the selected band is suppressed by manifold learning to highlight the inherent non-linear structure of faulty impacts. The TSM reserves the non-stationary information and reveals the non-linear structure of the fault pattern, with the merits of noise suppression and resolution improvement. The TSM ridge is further extracted by seeking the ridge with energy concentration lying on the TSM signature. It inherits the advantages of both the TSM and ridge analysis, and hence is beneficial to demodulation of the fault information. Through analyzing the instantaneous amplitude (IA) of the TSM ridge, in which the noise is nearly not contained, the fault characteristic frequency can be exactly identified. The whole process of the proposed fault diagnosis scheme is automatic, and its effectiveness has been verified by means of typical faulty vibration/acoustic signals from a gearbox and bearings. A reliable performance of the new method is validated in comparison with traditional enveloping methods for rotating machine fault diagnosis.