A novel deep autoencoder feature learning method for rotating machinery fault diagnosis-Reference-Cited by-同舟云学术

A novel deep autoencoder feature learning method for rotating machinery fault diagnosis

Published:2017-10 Issue: Volume:95 Page:187-204
ISSN:0888-3270
Container-title:Mechanical Systems and Signal Processing
language:en
Short-container-title:Mechanical Systems and Signal Processing

Author:

Shao Haidong,Jiang Hongkai,Zhao Huiwei,Wang Fuan

Funder

National Natural Science Foundation of China

Aviation Science Foundation of China

Shanghai Engineering Research Center of Civil Aircraft Health Monitoring Foundation of China

Innovation Foundation for Doctor Dissertation of Northwestern Polytechnical University

Publisher

Elsevier BV

Subject

Computer Science Applications,Mechanical Engineering,Aerospace Engineering,Civil and Structural Engineering,Signal Processing,Control and Systems Engineering

Reference34 articles.

1. An improved EEMD with multiwavelet packet for rotating machinery multi-fault diagnosis;Jiang;Mech. Syst. Signal Process.,2013

2. Wavelet transform based on inner product in fault diagnosis of rotating machinery: a review;Chen;Mech. Syst. Signal Process.,2016

3. A review on empirical mode decomposition in fault diagnosis of rotating machinery;Lei;Mech. Syst. Signal Process.,2013

4. Application of an intelligent classification method to mechanical fault diagnosis;Lei;Expert Syst. Appl.,2009

5. Bearing fault detection by a novel condition-monitoring scheme based on statistical-time features and neural networks;Prieto;IEEE Trans. Industr. Electron.,2013

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