Fault diagnosis of rotary machinery components using a stacked denoising autoencoder-based health state identification-Reference-Cited by-同舟云学术

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Fault diagnosis of rotary machinery components using a stacked denoising autoencoder-based health state identification

Published:2017-01 Issue: Volume:130 Page:377-388
ISSN:0165-1684
Container-title:Signal Processing
language:en
Short-container-title:Signal Processing

Author:

Lu Chen,Wang Zhen-Ya,Qin Wei-Li,Ma Jian

Funder

National Natural Science Foundation of China

Technology Foundation Program of National Defense

Publisher

Elsevier BV

Subject

Electrical and Electronic Engineering,Computer Vision and Pattern Recognition,Signal Processing,Software,Control and Systems Engineering

Reference53 articles.

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2. Multiwavelet transform and its applications in mechanical fault diagnosis - a review;Sun;Mech. Syst. Signal Process.,2014

3. A comparison study of basic data-driven fault diagnosis and process monitoring methods on the benchmark Tennessee Eastman process;Yin;J. Process. Contr.,2012

4. Adaptive signal decomposition based on wavelet ridge and its application;Qin;Signal Process.,2016

5. Development of an optimized condition-based maintenance system by data fusion and reliability-centered maintenance;Niu;Reliab. Eng. Syst. Safe.,2010

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