Detection and diagnosis of bearing faults using shift-invariant dictionary learning and hidden Markov model
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- 作者：Haitao Zhoua ; zhouht1988@163.com" class="auth_mail" title="E-mail the corresponding author ; Jin Chena ; Guangming Donga ; Ran Wangb
- 关键词：Shift-invariant dictionary learning ; Sparse representation ; Adaptive feature extraction ; Rolling element bearing ; Hidden Markov model ; Double impulses
- 刊名：Mechanical Systems and Signal Processing
- 出版年：2016
- 出版时间：May 2016
- 年：2016
- 卷：72-73
- 期：Complete
- 页码：65-79
- 全文大小：1005 K
文摘
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Shift-invariant dictionary learning method is used to extract double-impulse structure in bearing fault.
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A new feature extraction method is proposed by computing the energy distribution on each basis atom.
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A new fault diagnosis model for rolling element bearing based on shift-invariant dictionary learning and hidden Markov model is proposed.
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The advantages of proposed method over other methods, in terms of feature extraction or classifiers, are verified.