Fault Diagnosis Framework of Rolling Bearing Using Adaptive Sparse Contrative Auto-Encoder With Optimized Unsupervised Extreme Learning Machine

Fault Diagnosis Framework of Rolling Bearing Using Adaptive Sparse Contrative Auto-Encoder With Optimized Unsupervised Extreme Learning Machine

Nowadays, the intelligent fault diagnosis based on deep learning have achieved remarkable results in the fields of the industrial equipment health monitoring and management. To implement the adaptive feature extraction and fault isolation for key components of rotating machinery (rolling bearings, e...

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Bibliographic Details
Main Authors: Xiaoli Zhao, Minping Jia, Zheng Liu
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Access
Subjects:
Rolling bearings
fault diagnosis framework
homotopy regularization
Adaptive Sparse Contrative Auto-encoder (ASCAE)
Optimized Unsupervised extreme learning machine (OUSELM)
Cuckoo Search Algorithm (CSA)
Online Access:https://ieeexplore.ieee.org/document/8945331/

Internet

https://ieeexplore.ieee.org/document/8945331/

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