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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Vydáno v:IEEE transactions on instrumentation and measurement Ročník 70; s. 1 - 12
Hlavní autoři: Cheng, Yiwei, Wu, Jun, Zhu, Haiping, Or, Siu Wing, Shao, Xinyu
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
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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Popis
Shrnutí: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 this article, a novel ensemble long short-term memory neural network (ELSTMNN) model for RUL prediction is proposed to enhance the RUL prognosis accuracy and improve the adaptive and generalization abilities under different prognostic scenarios. The ELSTMNN contains a series of long short-term memory neural networks (LSTMNNs), each of which is trained on a unique set of historical data. A novel ensemble method is first proposed using Bayesian inference algorithm to integrate multiple predictions of the LSTMNNs for the optimal RUL estimation. The effectiveness of the ELSTMNN-based RUL prognosis method is validated using two characteristically different turbofan engine data sets. The experimental results show a competitive performance of the ELSTMNN in comparison with other prognostic methods.
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ISSN:0018-9456
1557-9662
DOI:10.1109/TIM.2020.3031113