Reconstruction and information entropy analysis of frictional vibration signals in running-in progress

Abstract

Purpose This study aims to reconstruct the frictional vibration signal from noise and characterize the running-in process by frictional vibration. Design/methodology/approach There is a strong correlation between tangential frictional vibration and normal frictional vibration. On this basis, a new frictional vibration reconstruction method combining cross-correlation analysis with ensemble empirical mode decomposition (EEMD) was proposed. Moreover, the concept of information entropy of friction vibration is introduced to characterize the running-in process. Findings Compared with the wavelet packet method, the tangential friction vibration and the normal friction vibration reconstructed by the method presented in this paper have a stronger correlation. More importantly, during the running-in process, the information entropy of friction vibration gradually decreases until the equilibrium point is reached, which is the same as the changing trend of friction coefficient, indicating that the information entropy of friction vibration can be used to characterize the running-in process. Practical implications The study reveals that the application EEMD method is an appropriate approach to reconstruct frictional vibration and the information entropy of friction vibration represents the running-in process. Based on these results, a condition monitoring system can be established to automatically evaluate the running-in state of mechanical parts. Originality/value The EEMD method was applied to reconstruct the frictional vibration. Furthermore, the information entropy of friction vibration was used to analysis the running-in process.

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

“…Unlike the vibration signals, the low-frequency band dominated the spectrum of the sound pressure signals and caused a periodic variation in the waveform, as shown in Figure 3c,d. Meanwhile, the Pearson correlation coefficient was used to describe the correlation between two kinds of signals [21,22]. The Pearson coefficients were −0.03, −0.06, −0.05 and −0.07, respectively, which demonstrated that the original vibration and sound pressure signals were less correlated at each time.…”

“…Thus, the "weak" FIV components in vibration and sound are correlated, which makes it possible to identify "weak" FIV by cross-correlation analysis (CCA) without the help of the friction coefficient. Of course, the discussion of detecting "weak" FIV signals by CCA is in no sense new, for example, see [21,22]. However, most of the present work focused on the CCA of the vibration signals in the tangential and normal directions rather than the vibration and sound signals.…”

Analysis of Vibration Characteristics of Bridge Structures under Seismic Excitation

“…Studies have shown that the information entropy of mechanical friction vibration signals is closely related to the change of friction coefficient. This relationship can be used to monitor and evaluate the running-in process and wear state of machinery [ 26 , 27 ]. Ying et al proposed a rolling bearing fault diagnosis algorithm that utilizes ensemble entropy, the Holder coefficient theory, and the grey relation algorithm.…”