articleTop 1% cited
A novel deep autoencoder feature learning method for rotating machinery fault diagnosis
Mechanical Systems and Signal Processing · 2017 · Vol. 95 · pp. 187–204
Haidong Shao(Northwestern Polytechnical University)Hongkai Jiang✉(Northwestern Polytechnical University)Huiwei Zhao(Northwestern Polytechnical University)Fuan Wang(Northwestern Polytechnical University)
Machine Fault Diagnosis TechniquesGear and Bearing Dynamics AnalysisFault Detection and Control SystemsAutoencoderArtificial intelligenceFault (geology)Feature (linguistics)Pattern recognition (psychology)Deep learningSIGNAL (programming language)VibrationComputer scienceEngineering
Funding
- National Natural Science Foundation of China
Citations
670
FWCI
46.43
field-weighted impact
References
36
Percentile
100%
vs. same field & year
Citations per year
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References
Wavelet transform based on inner product in fault diagnosis of rotating machinery: A review
Mechanical Systems and Signal Processing · 2015 · 506 citations
A review on empirical mode decomposition in fault diagnosis of rotating machinery
Mechanical Systems and Signal Processing · 2012 · 1,779 citations
Deep learning in neural networks: An overview
Neural Networks · 2014 · 17,774 citations
Reducing the Dimensionality of Data with Neural Networks
Science · 2006 · 20,627 citations
Correntropy: Properties and Applications in Non-Gaussian Signal Processing
IEEE Transactions on Signal Processing · 2007 · 1,644 citations
Representation Learning: A Review and New Perspectives
IEEE Transactions on Pattern Analysis and Machine Intelligence · 2013 · 12,724 citations
Deep neural networks: A promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data
Mechanical Systems and Signal Processing · 2015 · 1,655 citations
An Intelligent Fault Diagnosis Method Using Unsupervised Feature Learning Towards Mechanical Big Data
IEEE Transactions on Industrial Electronics · 2016 · 1,158 citations
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