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

Jaehyeon Nam; Jaeyoung Kang

doi:10.3390/s21238054

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

Sensors (Basel). 2021 Dec 2;21(23):8054. doi: 10.3390/s21238054.

Authors

Jaehyeon Nam¹, Jaeyoung Kang²

Affiliations

¹ Future Automotive Intelligent Electronics Core Technology Center, Kongju National University, Cheonan 31080, Korea.
² Department of Mechanical Engineering, Inha University, Incheon 22212, Korea.

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.

Keywords: Lyapunov exponent; chaos; convolutional neural network; rattle; recurrence patterns; squeak.

MeSH terms

Neural Networks, Computer*
Vibration*