Abstract
Rolling-element bearing vibrations are random cyclostationary. This paper addresses the problem of noise reduction with simultaneous components extraction in vibration signals for faults diagnosis of bearing. The observed vibration signal is modeled as a summation of two components contaminated by noise, and each component composes of repetitive transients. To extract the two components simultaneously, an approach by solving an optimization problem is proposed in this paper. The problem adopts convex sparsity-based regularization scheme for decomposition, and non-convex regularization is used to further promote the sparsity but preserving the global convexity. A synthetic example is presented to illustrate the performance of the proposed approach for repetitive feature extraction. The performance and effectiveness of the proposed method are further demonstrated by applying to compound faults and single fault diagnosis of a locomotive bearing. The results show the proposed approach can effectively extract the features of outer and inner race defects.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 227-244 |
| Number of pages | 18 |
| Journal | Mechanical Systems and Signal Processing |
| Volume | 84 |
| DOIs | |
| State | Published - Feb 1 2017 |
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Keywords
- Bearing Fault diagnosis
- Compound fault diagnosis
- Convex optimization
- Cyclostationary signals
- Feature extraction
ASJC Scopus subject areas
- Control and Systems Engineering
- Signal Processing
- Civil and Structural Engineering
- Aerospace Engineering
- Mechanical Engineering
- Computer Science Applications
Cite this
Repetitive transients extraction algorithm for detecting bearing faults. / He, Wangpeng; Ding, Yin; Zi, Yanyang; Selesnick, Ivan.
In: Mechanical Systems and Signal Processing, Vol. 84, 01.02.2017, p. 227-244.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Repetitive transients extraction algorithm for detecting bearing faults
AU - He, Wangpeng
AU - Ding, Yin
AU - Zi, Yanyang
AU - Selesnick, Ivan
PY - 2017/2/1
Y1 - 2017/2/1
N2 - Rolling-element bearing vibrations are random cyclostationary. This paper addresses the problem of noise reduction with simultaneous components extraction in vibration signals for faults diagnosis of bearing. The observed vibration signal is modeled as a summation of two components contaminated by noise, and each component composes of repetitive transients. To extract the two components simultaneously, an approach by solving an optimization problem is proposed in this paper. The problem adopts convex sparsity-based regularization scheme for decomposition, and non-convex regularization is used to further promote the sparsity but preserving the global convexity. A synthetic example is presented to illustrate the performance of the proposed approach for repetitive feature extraction. The performance and effectiveness of the proposed method are further demonstrated by applying to compound faults and single fault diagnosis of a locomotive bearing. The results show the proposed approach can effectively extract the features of outer and inner race defects.
AB - Rolling-element bearing vibrations are random cyclostationary. This paper addresses the problem of noise reduction with simultaneous components extraction in vibration signals for faults diagnosis of bearing. The observed vibration signal is modeled as a summation of two components contaminated by noise, and each component composes of repetitive transients. To extract the two components simultaneously, an approach by solving an optimization problem is proposed in this paper. The problem adopts convex sparsity-based regularization scheme for decomposition, and non-convex regularization is used to further promote the sparsity but preserving the global convexity. A synthetic example is presented to illustrate the performance of the proposed approach for repetitive feature extraction. The performance and effectiveness of the proposed method are further demonstrated by applying to compound faults and single fault diagnosis of a locomotive bearing. The results show the proposed approach can effectively extract the features of outer and inner race defects.
KW - Bearing Fault diagnosis
KW - Compound fault diagnosis
KW - Convex optimization
KW - Cyclostationary signals
KW - Feature extraction
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U2 - 10.1016/j.ymssp.2016.06.035
DO - 10.1016/j.ymssp.2016.06.035
M3 - Article
VL - 84
SP - 227
EP - 244
JO - Mechanical Systems and Signal Processing
JF - Mechanical Systems and Signal Processing
SN - 0888-3270
ER -