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Research Papers

Periodic Impulsive Fault Feature Extraction of Rotating Machinery Using Dual-Tree Rational Dilation Complex Wavelet Transform

[+] Author and Article Information
ChunLin Zhang

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

Bing Li

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

BinQiang Chen

School of Physics and Mechanical &
Electrical Engineering,
Xiamen University,
Xiamen 361005, China
e-mail: cbq@xmu.edu.cn

HongRui Cao

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

YanYang Zi

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

ZhengJia He

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

1Corresponding author.

Contributed by the Manufacturing Engineering Division of ASME for publication in the JOURNAL OF MANUFACTURING SCIENCE AND ENGINEERING. Manuscript received October 9, 2013; final manuscript received June 1, 2014; published online August 6, 2014. Assoc. Editor: Robert Gao.

J. Manuf. Sci. Eng 136(5), 051011 (Aug 06, 2014) (16 pages) Paper No: MANU-13-1366; doi: 10.1115/1.4027839 History: Received October 09, 2013; Revised June 01, 2014

Fault diagnosis of rotating machinery is very important to guarantee the safety of manufacturing. Periodic impulsive fault features commonly appear in vibration measurements when local defects occur in the key components like rolling bearings and gearboxes. To extract the periodic impulses embedded in strong background noise, wavelet transform (WT) is suitable and has been widely used in analyzing these nonstationary signals. However, a few limitations like shift-variance and fixed frequency partition manner of the dyadic WT would weaken its effectiveness in engineering application. Compared with dyadic WT, the dual-tree rational dilation complex wavelet transform (DT-RADWT) enjoys attractive properties of better shift-invariance, flexible time-frequency (TF) partition manner, and tunable oscillatory nature of the bases. In this article, an impulsive fault features extraction technique based on the DT-RADWT is proposed. In the routine of the proposed method, the optimal DT-RADWT basis is constructed dynamically and adaptively based on the input signal. Additionally, the sensitive wavelet subband is chosen using kurtosis maximization principle to reveal the potential weak fault features. The proposed method is applied on engineering applications for defects detection of the rolling bearing and gearbox. The results show that the proposed method performs better in extracting the fault features than dyadic WT and empirical mode decomposition (EMD), especially when the incipient fault features are embedded in the frequency transition bands of the dyadic WT.

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Figures

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Fig. 1

Iterated FB implementation and frequency decomposition of dyadic DWT: (a) Two-level Mallat's pyramid algorithm of DWT; (b) frequency partition manner of dyadic DWT; and (c) frequency partition manner of WPT

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Fig. 2

Iterated FBs for implementation of DTCWT

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Fig. 3

TF atoms of dyadic WTs and their frequency response: (a) a typical wavelet basis of DTCWT; (b) frequency decomposition of DTCWT; (c) wavelet basis (Db 6 basis); and (d) frequency response of DWT (Db 6 basis)

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Fig. 5

The analysis and synthesis FBs of DT-RADWT

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Fig. 6

The first FB H(w) and G(w)

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Fig. 7

Equivalent FB to the first FB and its properties: (a) an equivalent FB to the first FB and (b) properties of H1(w) and G1(w)

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Fig. 11

The flow chart of the proposed method

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Fig. 12

The test setup in case 1

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Fig. 13

The vibration signal of the tested rolling bearing

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Fig. 14

The log-amplitude FFT spectrum of the signal

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Fig. 15

The characteristic kurtosis values of the candidate DT-RADWT bases: (a) q = p + 1; (b) q = p + 2; (c) q = p + 3; and (d) q = p + 4

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Fig. 16

Waveforms of the two selected optimal DT-RADWT bases: (a) B6,7,4 and (b) B9,10,3

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Fig. 17

The sensitive subband selection of the two optimal DT-RADWT bases: (a) B6,7,4 and (b) B9,10,3

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Fig. 18

Reconstructed sensitive subband signals and their envelope spectra: (a) the D7 subband signal of the basis B6,7,4; (b) the D5 subband signal of the basis B9,10,3; (c) the envelope spectrum of the subband signal in (a); and (d) the envelope spectrum of the subband signal in (b)

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Fig. 19

Local surface spalling fault on the outer race of the tested bearing: (a) a view of the disassembled axle box and (b) the defect on the outer race

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Fig. 20

The IMFs using EMD

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Fig. 21

Wavelet subband signals by using dyadic WTs: (a) DWT (Db 6 basis) and (b) SGWT

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Fig. 22

The results by using SK: (a) the Kurtogram and (b) the purified signal

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Fig. 23

The schematic sketch of the F3 finishing mill stand

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Fig. 24

The vibration signal of the reduction gearbox

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Fig. 25

The log-amplitude FFT spectrum of the vibration signal

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Fig. 26

The characteristic kurtosis values of the candidate DT-RADWT bases: (a) s = 1; (b) s = 2; (c) s = 3; (d) s = 4; (e) s = 5; and (f) s = 6

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Fig. 27

Waveform of the optimal basis B3,4,2

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Fig. 28

Kurtosis values of subbands of B3,4,2

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Fig. 29

The reconstructed D4 wavelet subband signal

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Fig. 30

Wavelet subbands signals by using dyadic WTs: (a) DWT (Db 6 basis) and (b) SGWT

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Fig. 31

The decomposition IMFs using EMD of Fig. 24

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Fig. 32

Results by using SK of Fig. 24: (a) the Kurtogram and (b) the purified signal

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Fig. 33

Local defects on the pinion gear in the reduction gearbox: (a) location of I1 and (b) location of I2

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