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Research Papers: Friction and Wear

Dynamic States Recognition of Friction Noise in the Wear Process Based on Moving Cut Data-Approximate Entropy

[+] Author and Article Information
Cong Ding

School of Mechatronic Engineering,
China University of Mining and Technology,
Xuzhou 221116, China
e-mail: dingcong@cumt.edu.cn

Hua Zhu

School of Mechatronic Engineering,
China University of Mining and Technology,
Xuzhou 221116, China
e-mail: zhuhua83591917@163.com

Guodong Sun

School of Mechatronic Engineering,
China University of Mining and Technology,
Xuzhou 221116, China
e-mail: guodongsun@cumt.edu.cn

Yu Jiang

School of Mechatronic Engineering,
China University of Mining and Technology,
Xuzhou 221116, China
e-mail: Jiangyu@cumt.edu.cn

Chunling Wei

School of Mechatronic Engineering,
China University of Mining and Technology,
Xuzhou 221116, China
e-mail: clweilove@126.com

1Corresponding author.

Contributed by the Tribology Division of ASME for publication in the JOURNAL OF TRIBOLOGY. Manuscript received November 26, 2017; final manuscript received February 7, 2018; published online April 5, 2018. Assoc. Editor: Daejong Kim.

J. Tribol 140(5), 051604 (Apr 05, 2018) (8 pages) Paper No: TRIB-17-1455; doi: 10.1115/1.4039525 History: Received November 26, 2017; Revised February 07, 2018

Wear experiments are performed to explore dynamic states changes of friction noise signals. A new characteristic parameter, moving cut data-approximate entropy (MC-ApEn), is adopted to quantitatively recognize dynamic states. Additionally, determinism (DET), one key parameter of recurrence quantification analysis, is applied to verify the reliability of recognition results of MC-ApEn. Results illustrate that MC-ApEn of friction noise has distinct changes in different wear processes, and it can accurately detect abrupt change points of dynamic states for friction noise. Furthermore, DET of friction noise rapidly declines first, then fluctuates around a small value, and finally increases sharply, which just corresponds to the evolution process of MC-ApEn. So, the reliability of wear state recognition on the basis of MC-ApEn can be confirmed. It makes it possible to accurately and reliably recognize wear states of friction pairs based on MC-ApEn.

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Figures

Grahic Jump Location
Fig. 1

Ring-on-disk tribometer

Grahic Jump Location
Fig. 2

Installation of (a) ring and (b) disk

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Fig. 3

Time series of friction force and noise signals: (a) test 1 (P = 480 N, n = 550 rpm), (b) test 2 (P = 480 N, n = 600 rpm), (c) test 3 (P = 580 N, n = 600 rpm), (d) test 4 (P = 680 N, n = 600 rpm), and (e) test 5 (P = 480 N, n = 550 rpm)

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Fig. 4

Time series Y and MC-ApEn evolution of Y: (a) theoretical nonlinear time series Y, (b) L = 100, (c) L = 50, (d) L = 20, and (e) L = 13

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Fig. 5

State recognition of friction noise in three tests based on MC-ApEn: (a) test 1 (P = 480 N, n = 550 rpm), (b) test 2 (P = 480 N, n = 600 rpm), (c) test 2 (P = 580 N, n = 600 rpm), (d) test 4 (P = 680 N, n = 600 rpm), and (e) test 5 (P = 480 N, n = 550 rpm)

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Fig. 6

Determinism evolution of friction noise in three tests: (a) test 1 (P = 480 N, n = 550 rpm), (b) test 2 (P = 480 N, n = 600 rpm), (c) test 3 (P = 580 N, n = 600 rpm), (d) test 4 (P = 680 N, n = 600 rpm), and (e) test 5 (P = 480 N, n = 550 rpm)

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