Fault-Attention Generative Probabilistic Adversarial Autoencoder for Machine Anomaly Detection

Anomaly detection is one of the most fundamental and indispensable components in predictive maintenance. In this article, anomaly detection is modeled as a one-class classification problem. Based on the scenario that the training data only include healthy state data, a fault-attention generative pro...

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Vydané v:	IEEE transactions on industrial informatics Ročník 16; číslo 12; s. 7479 - 7488
Hlavní autori:	Wu, Jingyao, Zhao, Zhibin, Sun, Chuang, Yan, Ruqiang, Chen, Xuefeng
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Piscataway IEEE 01.12.2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Anomalies Anomaly detection artificial neural networks Classification condition monitoring Datasets Decoding Failure Feature extraction Generators Manifolds one-class classification Predictive maintenance Probabilistic logic Statistical analysis
ISSN:	1551-3203, 1941-0050
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