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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Vydané v:	Process safety and environmental protection Ročník 154; s. 467 - 479
Hlavní autori:	Arunthavanathan, Rajeevan, Khan, Faisal, Ahmed, Salim, Imtiaz, Syed
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Rugby Elsevier B.V 01.10.2021 Elsevier Science Ltd
Predmet:	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
On-line prístup:	Získať plný text
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