The Feature Extraction through Wavelet Coefficients of Metal Friction Noise for Adhesive and Abrasive Wear Monitoring

Abstract

Friction between metals is a physical phenomenon that occurs in manufacturing machine tools. This annoying noise implies unnecessary metal contact and deterioration of a mechanical system. In this study, for the monitoring of the friction between two metal surfaces, the acoustic signature was extracted by applying the wavelet transform method to the noise measured from the change in contact force for each state of adhesive and abrasive wear. Experiments were conducted with a constant relative speed between the contacting metal surfaces. For the adhesive wear, the peak signal-to-noise ratio (PSNR) calculated by the wavelet transformation increases with the increasing contact pressure. Opposite trends were observed for the abrasive wear. The proposed index formed a group within a specific range. This ratio exhibited a strong relationship with the wear characteristics and the surface condition. From the proposed index calculated by the wavelet coefficients, the continuous monitoring of the wear influence on the failure of the machine movement operations is achieved by the sound radiation from the contacting surfaces.

A Detecting Method for “Weak” Friction-Induced Vibration Based on Cross-Correlation Analysis between Vibration and Sound Signals

“…The "weak" FIV is highly dependent on the impacts of the asperities between the friction surfaces; one is known as surface or roughness sound [10]. As a result, some scholars believe that the "weak" FIV can help monitor the change in friction and wear state of friction pairs [11] and detect the failure of surface damage [12]. Therefore, studying the correspondence between "weak" FIV and the wear behaviors of friction pairs can be used to monitor the wear state of friction pairs online.…”