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Research Papers

A Comprehensive Study of Vibration Signals for a Thin Shell Structure Using Enhanced Independent Component Analysis and Experimental Validation

[+] Author and Article Information
Wei Cheng

State Key Laboratory for Manufacturing
Systems Engineering,
Xi’an Jiaotong University,
Xi’an, Shaanxi 710049, China
e-mail: chengw@mail.xjtu.edu.cn

Zhengjia He

State Key Laboratory for Manufacturing
Systems Engineering,
Xi’an Jiaotong University,
Xi’an, Shaanxi 710049, China
e-mail: hzj@mail.xjtu.edu.cn

Zhousuo Zhang

State Key Laboratory for Manufacturing
Systems Engineering,
Xi’an Jiaotong University,
Xi’an, Shaanxi 710049, China
e-mail: zzs@mail.xjtu.edu.cn

1Corresponding author.

Contributed by the Technical Committee on Vibration and Sound of ASME for publication in the JOURNAL OF VIBRATION AND ACOUSTICS. Manuscript received July 4, 2013; final manuscript received April 26, 2014; published online May 22, 2014. Assoc. Editor: Patrick S. Keogh.

J. Vib. Acoust 136(4), 041011 (May 22, 2014) (11 pages) Paper No: VIB-13-1233; doi: 10.1115/1.4027545 History: Received July 04, 2013; Revised April 26, 2014

Vibration source information (source number, source waveforms, and source contributions) of gears, bearings, motors, and shafts is very important for machinery condition monitoring, fault diagnosis, and especially vibration monitoring and control. However, it has been a challenging to effectively extract the source information from the measured mixed vibration signals without a priori knowledge of the mixing mode and sources. In this paper, we propose source number estimation, source separation, and source contribution evaluation methods based on an enhanced independent component analysis (EICA). The effects of nonlinear mixing mode and different source number on source separation are studied with typical vibration signals, and the effectiveness of the proposed methods is validated by numerical case studies and experimental studies on a thin shell test bed. The conclusions show that the proposed methods have a high accuracy for thin shell structures. This research benefits for application of independent component analysis (ICA) to solve the vibration monitoring and control problems for thin shell structures and provides important references for machinery condition monitoring and fault diagnosis.

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Figures

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Fig. 1

Source separation step of the EICA

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Fig. 2

Optimizing selection step of the EICA

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Fig. 5

Waveforms of the separated components: (a) case: n = 3, σ= 0.0001; (b) case: n = 4, σ= 0.0001; (c) case: n = 5, σ= 0.0003; and (d) case: n = 6, σ= 0.0005

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Fig. 4

R-index of source separations with different σ

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Fig. 3

Waveforms of the signals: (a) the source signals and (b) the mixed signals

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Fig. 9

Waveforms of the signals: (a) the mixed signals and (b) the source signals

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Fig. 6

The test bed and experiment equipments: (a) full view of the test bed; (b) motor 1; and (c) motor 2/3

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Fig. 7

The vibration monitoring and control system

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Fig. 8

The structure diagram of the test bed: (a) the test bed; (b) motor 1; and (c) motor 2/3

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Fig. 12

Spectra of the signals: (a) the source signals and (b) the separated signals

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Fig. 10

R-index of different source separation

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Fig. 11

Waveforms of the separated signals: (a) case: n = 3; (b) case: n = 4; and (c) case: n = 5

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