Comparison of autoencoder architectures for fault detection in industrial processes

Fault detection constitutes a fundamental task for predictive maintenance, requiring mathematical models that can be conveniently provided by data-driven techniques. Autoencoders are a particular type of unsupervised Artificial Neural Networks that can be suitable for fault detection applications. D...

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書誌詳細

出版年:	Digital Chemical Engineering
主要な著者:	Deris Eduardo Spina, Luiz Felipe de O. Campos, Wallthynay F. de Arruda, Afrânio Melo, Marcelo F. de S. Alves, Gildeir Lima Rabello, Thiago K. Anzai, José Carlos Pinto
フォーマット:	論文
言語:	英語
出版事項:	Elsevier 2024-09-01
主題:	Autoencoder Denoising Variational Fault detection
オンライン･アクセス:	http://www.sciencedirect.com/science/article/pii/S2772508124000243