Stick-slip vibrations of a self-excited SD oscillator with Coulomb friction

Li, Zhixin; Cao, Qingjie; Nie, Zairan

doi:10.1007/s11071-020-06009-3

“…The non-smooth transition between these two states makes the system exhibits rich nonlinear behaviours, including bifurcation and chaos [1,5,6]. As a result, frictioninduced stick-slip vibrations have been a continuously active topic for theoretical analysis [7,8,9,10,11,12,13,14] and industrial applications [15,16,17].…”

Section: Introductionmentioning

confidence: 99%

Friction-induced vibration of a stick–slip oscillator with random field friction modelling

Hu

¹

,

Batou

²

,

Ouyang

³

2023

Mechanical Systems and Signal Processing

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“…In addition, the friction force is expressed as a function of the velocity in the dynamic instability of the friction-induced model. Higher-order nonlinear problems, such as chaos, were described because the model includes extreme nonlinearity in the creep section [ 12 , 13 , 14 , 15 , 16 ]. Kang [ 15 ] used a two-degree-of-freedom friction model to show that the chaotic phenomenon is generated by self-excited vibrations and investigated the parameters that create chaos.…”

Section: Introductionmentioning

confidence: 99%

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

Nam

¹

,

Kang

²

2021

Sensors

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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.

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“…In addition, the friction force is expressed as a function of the velocity in the dynamic instability of the friction-induced model. Higher-order nonlinear problems, such as chaos, were described because the model includes extreme nonlinearity in the creep section [12][13][14][15][16]. Kang [15] used a two-degree-of-freedom friction model to show the chaotic phenomenon is generated by selfexcited vibrations and investigated the parameters that create chaos.…”

Section: Introductionmentioning

confidence: 99%

Classification of Chaotic Squeak And Rattle Vibrations By CNN Using Recurrence Pattern

Nam¹,

Kang²

2021

Preprint

3

0

View full text Add to dashboard Cite

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.

show abstract

Stick-slip vibrations of a self-excited SD oscillator with Coulomb friction

Cited by 24 publications

References 34 publications

Friction-induced vibration of a stick–slip oscillator with random field friction modelling

Friction-induced vibration of a stick–slip oscillator with random field friction modelling

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

Classification of Chaotic Squeak And Rattle Vibrations By CNN Using Recurrence Pattern

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