A Fault Diagnosis Methodology Based on Non-Stationary Monitoring Signals by Extracting Features With Unknown Probability Distribution

A Fault Diagnosis Methodology Based on Non-Stationary Monitoring Signals by Extracting Features With Unknown Probability Distribution

This paper studies features with the characteristic of unknown probability distribution, and its application on fault diagnosis based on non-stationary monitoring signals, which mainly consider the uncertainty as the main factor in masking fault diagnosis of practical industrial system. Generally, t...

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Bibliographic Details
Main Authors: Huang Lei, Wang Yiming, Qu Jianfeng, Ren Hao
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Access
Subjects:
Feature extraction
restricted Boltzmann machine
deep belief network
non-stationary signals
fault diagnosis
Online Access:https://ieeexplore.ieee.org/document/9025196/

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