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Research Papers

Separating Multiple Moving Sources by Microphone Array Signals for Wayside Acoustic Fault Diagnosis

[+] Author and Article Information
Wei Xiong

Department of Precision Machinery and Precision Instrumentation,
University of Science and Technology of China,
Hefei, Anhui 230026, China
e-mail: davidxw@mail.ustc.edu.cn

Qingbo He

State Key Laboratory of Mechanical System and Vibration,
Shanghai Jiao Tong University,
Shanghai 200240, China
e-mail: qbhe@sjtu.edu.cn

Zhike Peng

State Key Laboratory of Mechanical System and Vibration,
Shanghai Jiao Tong University,
Shanghai 200240, China
e-mail: z.peng@sjtu.edu.cn

1Corresponding author.

Contributed by the Noise Control and Acoustics Division of ASME for publication in the Journal of Vibration and Acoustics. Manuscript received October 6, 2018; final manuscript received April 2, 2019; published online May 22, 2019. Assoc. Editor: Zhongquan Charlie Zheng.

J. Vib. Acoust 141(5), 051004 (May 22, 2019) (12 pages) Paper No: VIB-18-1432; doi: 10.1115/1.4043508 History: Received October 06, 2018; Accepted April 04, 2019

Wayside acoustic defective bearing detector (ADBD) system is a potential technique in ensuring the safety of traveling vehicles. However, Doppler distortion and multiple moving sources aliasing in the acquired acoustic signals decrease the accuracy of defective bearing fault diagnosis. Currently, the method of constructing time-frequency (TF) masks for source separation was limited by an empirical threshold setting. To overcome this limitation, this study proposed a dynamic Doppler multisource separation model and constructed a time domain-separating matrix (TDSM) to realize multiple moving sources separation in the time domain. The TDSM was designed with two steps of (1) constructing separating curves and time domain remapping matrix (TDRM) and (2) remapping each element of separating curves to its corresponding time according to the TDRM. Both TDSM and TDRM were driven by geometrical and motion parameters, which would be estimated by Doppler feature matching pursuit (DFMP) algorithm. After gaining the source components from the observed signals, correlation operation was carried out to estimate source signals. Moreover, fault diagnosis could be carried out by envelope spectrum analysis. Compared with the method of constructing TF masks, the proposed strategy could avoid setting thresholds empirically. Finally, the effectiveness of the proposed technique was validated by simulation and experimental cases. Results indicated the potential of this method for improving the performance of the ADBD system.

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Figures

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Fig. 1

Geometrical model of a microphone array in the wayside acoustic defective bearing diagnosis system

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Fig. 2

Energy filter: (a) filter curve in time, (b) filter curve in frequency, (c) time-frequency energy filter, (d) TFD of a simulation signal, and (e) the filtered TFD

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Fig. 3

Non-contact health monitoring procedure of the proposed method

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Fig. 4

Train bearing test rig and wayside multisource acoustic signal acquiring experiment: (a) artificial cracks on the outer and inner raceways, (b) static experiment platform, (c) source layout, and (d) data acquiring system

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Fig. 10

Spectrum and envelope spectrum results: (a) spectra of x̂1(t), (b) spectra of x̂2(t), (c) spectra of x̂1(t) by the method of TF masks, and (d) spectra of x̂2(t) by the method of TF masks

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Fig. 8

Original observed signals with defects both on the outer raceways: (a) the resample signal sre1(t) with its spectra, (b) the resample signal sre2(t) with its spectra, (c) TFD of the resample signals sre1(t), and (d) TFD of the resample signals sre2(t)

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Fig. 7

Spectrum and envelope spectrum results: (a) spectra of x̂1(t), (b) spectra of x̂2(t), (c) spectra of x̂1(t) by method of TF masks, and (d) spectra of x̂2(t) by the method of TF masks

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Fig. 6

Separation results of inner-race and outer-race defective signal sources: (a) four separated signals x̄11(t), x̄21(t), x̄12(t), and x̄22(t), (b) the recovered source signals x̂1(t) and x̂2(t), (c) TFD of x̂1(t), and (d) TFD of x̂2(t)

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Fig. 5

Original observed signals with defects on the inner and outer raceways: (a) the resample signal sre1(t) with its spectra, (b) the resample signal sre2(t) with its spectra, (c) TFD of the resample signals sre1(t), and (d) TFD of the resample signals sre2(t)

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Fig. 9

Separation results of inner-race and outer-race defective signal sources: (a) four separated signals x̄11(t), x̄21(t), x̄12(t), and x̄22(t), (b) the recovered source signals x̂1(t) and x̂2(t), (c) TFD of x̂1(t), and (d) TFD of x̂2(t)

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