Abstract
In this paper, the novel wavelet spectral kurtosis (WSK) technique is applied for the early diagnosis of gear tooth faults. Two variants of the wavelet spectral kurtosis technique, called variable resolution WSK and constant resolution WSK, are considered for the diagnosis of pitting gear faults. The gear residual signal, obtained by filtering the gear mesh frequencies, is used as the input to the SK algorithm. The advantages of using the wavelet-based SK techniques when compared to classical Fourier transform (FT)-based SK is confirmed by estimating the toothwise Fisher's criterion of diagnostic features. The final diagnosis decision is made by a three-stage decision-making technique based on the weighted majority rule. The probability of the correct diagnosis is estimated for each SK technique for comparison. An experimental study is presented in detail to test the performance of the wavelet spectral kurtosis techniques and the decision-making technique.
| Original language | English |
|---|---|
| Pages (from-to) | 409-416 |
| Number of pages | 8 |
| Journal | Insight: Non-Destructive Testing and Condition Monitoring |
| Volume | 58 |
| Issue number | 8 |
| DOIs | |
| Publication status | Published - 1 Aug 2016 |
| Externally published | Yes |
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Novel spectral kurtosis technology for adaptive vibration condition monitoring of multi-stage gearboxes. / Gelman, L.; Harish Chandra, N.; Kurosz, R.; Pellicano, F.; Barbieri, M.; Zippo, A.
In: Insight: Non-Destructive Testing and Condition Monitoring, Vol. 58, No. 8, 01.08.2016, p. 409-416.Research output: Contribution to journal › Article
TY - JOUR
T1 - Novel spectral kurtosis technology for adaptive vibration condition monitoring of multi-stage gearboxes
AU - Gelman, L.
AU - Harish Chandra, N.
AU - Kurosz, R.
AU - Pellicano, F.
AU - Barbieri, M.
AU - Zippo, A.
PY - 2016/8/1
Y1 - 2016/8/1
N2 - In this paper, the novel wavelet spectral kurtosis (WSK) technique is applied for the early diagnosis of gear tooth faults. Two variants of the wavelet spectral kurtosis technique, called variable resolution WSK and constant resolution WSK, are considered for the diagnosis of pitting gear faults. The gear residual signal, obtained by filtering the gear mesh frequencies, is used as the input to the SK algorithm. The advantages of using the wavelet-based SK techniques when compared to classical Fourier transform (FT)-based SK is confirmed by estimating the toothwise Fisher's criterion of diagnostic features. The final diagnosis decision is made by a three-stage decision-making technique based on the weighted majority rule. The probability of the correct diagnosis is estimated for each SK technique for comparison. An experimental study is presented in detail to test the performance of the wavelet spectral kurtosis techniques and the decision-making technique.
AB - In this paper, the novel wavelet spectral kurtosis (WSK) technique is applied for the early diagnosis of gear tooth faults. Two variants of the wavelet spectral kurtosis technique, called variable resolution WSK and constant resolution WSK, are considered for the diagnosis of pitting gear faults. The gear residual signal, obtained by filtering the gear mesh frequencies, is used as the input to the SK algorithm. The advantages of using the wavelet-based SK techniques when compared to classical Fourier transform (FT)-based SK is confirmed by estimating the toothwise Fisher's criterion of diagnostic features. The final diagnosis decision is made by a three-stage decision-making technique based on the weighted majority rule. The probability of the correct diagnosis is estimated for each SK technique for comparison. An experimental study is presented in detail to test the performance of the wavelet spectral kurtosis techniques and the decision-making technique.
KW - Condition monitoring
KW - Decision making
KW - Diagnosis
KW - Gear teeth
KW - Higher order statistics
KW - Wavelet analysis
UR - http://www.scopus.com/inward/record.url?scp=84982189625&partnerID=8YFLogxK
U2 - 10.1784/insi.2016.58.8.409
DO - 10.1784/insi.2016.58.8.409
M3 - Article
VL - 58
SP - 409
EP - 416
JO - Insight: Non-Destructive Testing and Condition Monitoring
JF - Insight: Non-Destructive Testing and Condition Monitoring
SN - 1354-2575
IS - 8
ER -