Classification of Chaotic Squeak and Rattle Vibrations by CNN Using Recurrence Pattern.

Jaehyeon Nam,Jaeyoung Kang

doi:10.3390/s21238054

Abstract

The chaotic squeak and rattle (S&R) vibrations in mechanical systems were classified by deep learning. The rattle, single-mode, and multi-mode squeak models were constructed to generate chaotic S&R signals. The repetition of nonlinear signals generated by them was visualized using an unthresholded recurrence plot and learned using a convolutional neural network (CNN). The results showed that even if the signal of the S&R model is chaos, it could be classified. The accuracy of the classification was verified by calculating the Lyapunov exponent of the vibration signal. The numerical experiment confirmed that the CNN classification using nonlinear vibration images as the proposed procedure has more than 90% accuracy. The chaotic status and each model can be classified into six classes.

Highlights

Chaotic squeak and rattle (S&R) vibrations are a significant factor for evaluating the quality of automotive parts
This study examined whether the rattle and squeak signals can be classified through a convolutional neural network (CNN), even if they are chaotic, by applying a signal visualization technique
This study examined whether the rattle and squeak signals can be classified through a CNTNhi,sesvtuendyifexthaemyinaered cwhhaeotthice,rbthyearpapttllyeinagndassqiugneaakl vsiigsunaliszcaatniobnetcelcahsnsifqiuede.tBherocauugshe aCCNNNNis, eavneinmiafgteh-ebyasaerde cclhasasoitfic,atbiyonaptepclhyniniqguae,saignnRaPl -vbiassueadlizdaattiaosnettewcahsnicqounes.trBuecc3taeoduf s2te0o

Summary

Introduction

Chaotic squeak and rattle (S&R) vibrations are a significant factor for evaluating the quality of automotive parts. The friction force of the single-mode squeak model is expressed as α, and h are the control parameters that determine the negative slope.

Results

Conclusion