A deep learning model for process fault prognosis

Early fault detection and fault prognosis are crucial functions to ensure safe process operations. Fault prognosis can detect and isolate early developing faults as well as predict fault propagation. To promptly detect potential faults in process systems, it is important to examine the fault symptom...

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Veröffentlicht in:	Process safety and environmental protection Jg. 154; S. 467 - 479
Hauptverfasser:	Arunthavanathan, Rajeevan, Khan, Faisal, Ahmed, Salim, Imtiaz, Syed
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Rugby Elsevier B.V 01.10.2021 Elsevier Science Ltd
Schlagworte:	Artificial neural networks Data-driven model Deep learning Fault detection Fault diagnosis Fault prognosis Life prediction Long short-term memory LSTM model Mathematical models Multivariate analysis Neural networks Process safety Prognosis Recurrent neural networks System effectiveness Time dependence Useful life Fault diagnosis Process safety Data-driven model LSTM model Fault prognosis
ISSN:	0957-5820, 1744-3598
Online-Zugang:	Volltext
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