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...

Celý popis

Uloženo v:

Podrobná bibliografie
Vydáno v:	Process safety and environmental protection Ročník 154; s. 467 - 479
Hlavní autoři:	Arunthavanathan, Rajeevan, Khan, Faisal, Ahmed, Salim, Imtiaz, Syed
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Rugby Elsevier B.V 01.10.2021 Elsevier Science Ltd
Témata:	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 přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

Buďte první, kdo okomentuje tento záznam!