An improved deep network for intelligent diagnosis of machinery faults with similar features
Published 2019 View Full Article
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Title
An improved deep network for intelligent diagnosis of machinery faults with similar features
Authors
Keywords
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Journal
IEEJ Transactions on Electrical and Electronic Engineering
Volume 14, Issue 12, Pages 1851-1864
Publisher
Wiley
Online
2019-10-24
DOI
10.1002/tee.23012
References
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Related references
Note: Only part of the references are listed.- Artificial intelligence for fault diagnosis of rotating machinery: A review
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- (2016) Hussein Al-Bugharbee et al. JOURNAL OF SOUND AND VIBRATION
- Deep neural networks: A promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data
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- A feature selection approach based on term distributions
- (2016) Hongfang Zhou et al. SpringerPlus
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- (2015) Jianli Yang et al. BIO-MEDICAL MATERIALS AND ENGINEERING
- Detection of weak transient signals based on wavelet packet transform and manifold learning for rolling element bearing fault diagnosis
- (2015) Yi Wang et al. MECHANICAL SYSTEMS AND SIGNAL PROCESSING
- Deep learning
- (2015) Yann LeCun et al. NATURE
- Weak fault diagnosis of rotating machinery based on feature reduction with Supervised Orthogonal Local Fisher Discriminant Analysis
- (2015) Feng Li et al. NEUROCOMPUTING
- A novel adaptive wavelet stripping algorithm for extracting the transients caused by bearing localized faults
- (2013) Dong Wang et al. JOURNAL OF SOUND AND VIBRATION
- LMD Method and Multi-Class RWSVM of Fault Diagnosis for Rotating Machinery Using Condition Monitoring Information
- (2013) Zhiwen Liu et al. SENSORS
- Improved Hilbert–Huang transform based weak signal detection methodology and its application on incipient fault diagnosis and ECG signal analysis
- (2013) Jihong Yan et al. SIGNAL PROCESSING
- Early fault diagnosis of rotating machinery based on wavelet packets—Empirical mode decomposition feature extraction and neural network
- (2011) G.F. Bin et al. MECHANICAL SYSTEMS AND SIGNAL PROCESSING
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