Degradation-Aware Remaining Useful Life Prediction With LSTM Autoencoder

The remaining useful life (RUL) prediction plays a pivotal role in the predictive maintenance of industrial manufacturing systems. However, one major problem with the existing RUL estimation algorithms is the assumption of a single health degradation trend for different machine health stages. To imp...

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Vydáno v:IEEE transactions on instrumentation and measurement Ročník 70; s. 1 - 10
Hlavní autoři: Wu, Ji-Yan, Wu, Min, Chen, Zhenghua, Li, Xiao-Li, Yan, Ruqiang
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Accuracy
Algorithms
Data models
Degradation
Hilbert transformation
Industrial internet-of-things (IIoT)
Life prediction
Long short term memory
long short-term memory (LSTM) autoencoder (AE)
Mathematical model
Performance evaluation
Prediction algorithms
Predictive maintenance
Predictive models
prognostic technique
remaining useful life (RUL)
Useful life
ISSN:0018-9456, 1557-9662
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Shrnutí:The remaining useful life (RUL) prediction plays a pivotal role in the predictive maintenance of industrial manufacturing systems. However, one major problem with the existing RUL estimation algorithms is the assumption of a single health degradation trend for different machine health stages. To improve the RUL prediction accuracy with various degradation trends, this article proposes an algorithm dubbed degradation-aware long short-term memory (LSTM) autoencoder (AE) (DELTA). First, the Hilbert transform is adopted to evaluate the degradation stage and factor with the real-time sensory signal. Second, we adopt LSTM AE to predict RUL based on multisensor time-series data and the degradation factor. Distinct from the existing studies, the proposed framework is able to dynamically model the degradation factor and explore latent variables to improve RUL prediction accuracy. The performance of DELTA is evaluated with the open-source FEMTO bearing data set. Compared with the existing algorithms, DELTA achieves appreciable improvements in the RUL prediction accuracy.
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ISSN:0018-9456
1557-9662
DOI:10.1109/TIM.2021.3055788