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Research Papers

Improved Ensemble Superwavelet Transform for Vibration-Based Machinery Fault Diagnosis

[+] Author and Article Information
Wangpeng He

State Key Laboratory for Manufacturing and
Systems Engineering,
Xi'an Jiaotong University,
Xi'an 710049, China;
Tandon School of Engineering,
New York University,
Brooklyn, NY 11201
e-mail: wangp.he@gmail.com

Yanyang Zi

State Key Laboratory for Manufacturing and
Systems Engineering,
Xi'an Jiaotong University,
Xi'an 710049, China
e-mail: ziyy@mail.xjtu.edu.cn

Zhiguo Wan

State Key Laboratory for Manufacturing and
Systems Engineering,
Xi'an Jiaotong University,
Xi'an 710049, China
e-mail: wanzhiguo@stu.xjtu.edu.cn

Binqiang Chen

School of Aeronautics and Astronautics,
Xiamen University,
Xiamen 361005, China
e-mail: Cbq@xmu.edu.cn

1Corresponding author.

Manuscript received July 11, 2015; final manuscript received December 30, 2015; published online March 15, 2016. Assoc. Editor: Laine Mears.

J. Manuf. Sci. Eng 138(7), 071012 (Mar 15, 2016) (9 pages) Paper No: MANU-15-1344; doi: 10.1115/1.4032568 History: Received July 11, 2015; Revised December 30, 2015

In the previous work of authors, the authors have presented an automatic fault feature extraction method, called ensemble superwavelet transform (ESW), based on the combination of tunable Q-factor wavelet transform (TQWT) and Hilbert transform. However, the nonstationary fault feature ratio which defined to guide the optimal wavelet basis selection does not take the interferences of high-frequency components into consideration. In addition, the original ESW utilizes one optimal subband to reconstruct the signal, which may result in the leakage of useful fault features. The present paper improves the ESW to address these problems. Specifically, the authors modify the definition of fault feature ratio by eliminating the high-frequency components when calculating total amplitudes of Hilbert envelope spectrum. Moreover, for the purpose of preserving more useful fault features and recovering the signal more accurately, a novel approach to reconstruct the processed result by incorporating two optimal subbands is proposed in this paper. The comprehensive comparisons by processing two simulation signals are provided to verify the effectiveness and utility of the improved ESW. Moreover, the improved ESW is applied to a range of engineering applications, and the obtained results demonstrate that the improved ESW can act as an effective technique in extracting weak fault features.

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References

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Figures

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Fig. 1

Block diagram of filter banks for the implementation of the TQWT: (a) analysis filter bank and (b) synthesis filter bank

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Fig. 2

TQWT wavelets (at level 6) for two Q-factors: (a) Q = 1.0 and (b) Q = 4.0

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Fig. 3

Useful fault features leakage into two different subbands (shown shaded): (a) leakage into adjacent subbands and (b) leakage into two nonadjacent subbands

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Fig. 4

Flowchart of the improved ESW for extracting machinery fault features

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Fig. 5

The simulated signal: (a) noise-free signal with periodic impulses and (b) signal added with noise (SNR = −6.5 dB)

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Fig. 6

Original ESW: (a) single branch reconstruction of the optimal subband and (b) its Hilbert envelope spectrum

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Fig. 7

(a) The extracted transients by incorporating two optimal subbands and (b) its Hilbert envelope spectrum

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Fig. 10

(a) The extracted transients by incorporating two optimal subbands and (b) its Hilbert envelope spectrum

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Fig. 8

The simulated signal: (a) noise-free signal with periodic impulses and (b) signal added with noise (SNR = −5.5 dB)

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Fig. 9

Original ESW: (a) single branch reconstruction of the optimal subband and (b) its Hilbert envelope spectrum

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Fig. 11

The measured vibration signal of the motor housing

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Fig. 12

Original ESW: (a) single branch reconstruction of the optimal subband and (b) its Hilbert envelope spectrum

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Fig. 13

(a) The reconstructed signal by incorporating two optimal subbands and (b) its Hilbert envelope spectrum

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Fig. 14

The measured vibration signal

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Fig. 15

Original ESW: (a) single branch reconstruction of the optimal subband and (b) its Hilbert envelope spectrum

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Fig. 16

(a) The reconstructed signal by incorporating two optimal subbands and (b) its Hilbert envelope spectrum

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Fig. 17

Schematic sketch of the experimental setup

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Fig. 18

Measured noisy signal added with additive white Gaussian noise

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Fig. 19

Fault feature ratio displayed along the subbands corresponding to different Q-factors (original ESW)

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Fig. 20

(a) The reconstructed signal by utilizing one optimal subband and (b) its Hilbert envelope spectrum (original ESW)

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Fig. 21

Modified fault feature ratio displayed along the subbands corresponding to different Q-factors (improved ESW)

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Fig. 22

(a) The reconstructed signal by incorporating two optimal subbands and (b) its Hilbert envelope spectrum (improved ESW)

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