Remaining Useful Life Prognosis Based on Ensemble Long Short-Term Memory Neural Network

Remaining useful life (RUL) prognosis is of great significance to improve the reliability, availability, and maintenance cost of an industrial equipment. Traditional machine learning method is not fit for dealing with time series signals and has low generalization and stability in prognostic. In thi...

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Vydané v:	IEEE transactions on instrumentation and measurement Ročník 70; s. 1 - 12
Hlavní autori:	Cheng, Yiwei, Wu, Jun, Zhu, Haiping, Or, Siu Wing, Shao, Xinyu
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York IEEE 2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Adaptation models Algorithms Bayesian analysis Bayesian inference algorithm (BIA) ensemble learning (EL) ensemble long short-term memory neural network (ELSTMNN) Equipment costs Machine learning Maintenance costs Monitoring Neural networks Predictive models Prognosis Prognostics and health management remaining useful life (RUL) prognosis Sensors Short term Statistical inference Training Turbofan engines Useful life
ISSN:	0018-9456, 1557-9662
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