A Decision-Making Method for Machinery Abnormalities Based on Neural Network Prediction and Bayesian Hypothesis Testing

A Decision-Making Method for Machinery Abnormalities Based on Neural Network Prediction and Bayesian Hypothesis Testing

For anomaly identification of predicted data in machinery condition monitoring, traditional threshold methods have problems during residual testing. It is difficult to make decisions when the residuals are close to the threshold and fluctuate. This paper proposes a Bayesian dynamic thresholding meth...

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Bibliographic Details
Main Authors:	Gaojun Liu, Shan Yang, Gaixia Wang, Fenglei Li, Dongdong You
Format:	Article
Language:	English
Published:	MDPI AG 2021-07-01
Series:	Electronics
Subjects:	Bayesian hypothesis testing abnormality identification long short-term memory nuclear power turbine
Online Access:	https://www.mdpi.com/2079-9292/10/14/1610

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