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

Jaehyeon Nam1, Jaeyoung Kang2

  • 1Future Automotive Intelligent Electronics Core Technology Center, Kongju National University, Cheonan 31080, Korea.

Sensors (Basel, Switzerland)
|December 10, 2021
PubMed
Summary
This summary is machine-generated.

Deep learning accurately classifies chaotic squeak and rattle (S&R) vibrations in mechanical systems. This method uses nonlinear vibration images and achieves over 90% accuracy in identifying distinct S&R signal types.

Keywords:
Lyapunov exponentchaosconvolutional neural networkrattlerecurrence patternssqueak

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Area of Science:

  • Mechanical Engineering
  • Signal Processing
  • Artificial Intelligence

Background:

  • Squeak and rattle (S&R) vibrations are common in mechanical systems.
  • Classifying chaotic S&R signals is challenging due to their complex nonlinear dynamics.

Purpose of the Study:

  • To develop and validate a deep learning approach for classifying chaotic S&R vibrations.
  • To differentiate between rattle, single-mode squeak, and multi-mode squeak signals.

Main Methods:

  • Generated chaotic S&R signals using distinct mechanical models.
  • Visualized signal repetition with unthresholded recurrence plots.
  • Employed a convolutional neural network (CNN) for classification of vibration images.

Main Results:

  • The CNN successfully classified chaotic S&R signals with over 90% accuracy.
  • Classification accuracy was validated using the Lyapunov exponent.
  • The system could distinguish six distinct classes, including chaotic status and specific S&R models.

Conclusions:

  • Deep learning, particularly CNNs, is effective for classifying complex chaotic vibrations.
  • Nonlinear vibration images derived from recurrence plots are suitable inputs for AI-based analysis.
  • The proposed method offers a robust solution for identifying and categorizing S&R phenomena.