A one-class generative adversarial detection framework for multifunctional fault diagnoses

In this article, fault diagnosis is of great significance for system health maintenance. For real applications, diagnosis accuracy suffers from unbalanced data patterns, where normal data are usually abundant than anomaly ones, leading to tremendous diagnosis obstacles. Therefore, it is challenging...

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Bibliographic Details
Main Author: Pu, Ziqiang (author)
Other Authors: Cabrera, Diego (author), Bai, Yun (author), Li, Chuan (author)
Format: article
Language:eng
Published: 2022
Subjects:
Fault diagnosis
Anomaly detection
Generators
Generative adversarial networks
Data models
Task analysis
Online Access:http://hdl.handle.net/10400.1/17847
Country:Portugal
Oai:oai:sapientia.ualg.pt:10400.1/17847

Internet

http://hdl.handle.net/10400.1/17847

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