Temporal Convolutional Memory Networks for Remaining Useful Life Estimation of Industrial Machinery

Accurately estimating the remaining useful life (RUL) of industrial machinery is beneficial in many real-world applications. Estimation techniques have mainly utilized linear models or neural network based approaches with a focus on short term time dependencies. This paper, introduces a system model...

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Bibliographic Details
Main Authors: Jayasinghe, Lahiru, Samarasinghe, Tharaka, Chau Yuenv, Chen Ni Low, Jenny, Shuzhi Sam Ge
Format: Conference Proceeding
Language:English
Subjects:
convolutional neural networks
deep learning
Degradation
Estimation
Feature extraction
Hidden Markov models
long short-term memory
remaining useful life estimation
Systems architecture
Training
Trajectory
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Summary:Accurately estimating the remaining useful life (RUL) of industrial machinery is beneficial in many real-world applications. Estimation techniques have mainly utilized linear models or neural network based approaches with a focus on short term time dependencies. This paper, introduces a system model that incorporates temporal convolutions with both long term and short term time dependencies. The proposed network learns salient features and complex temporal variations in sensor values, and predicts the RUL. A data augmentation method is used for increased accuracy. The proposed method is compared with several state-of-the-art algorithms on publicly available datasets. It demonstrates promising results, with superior results for datasets obtained from complex environments.
ISSN:2643-2978
DOI:10.1109/ICIT.2019.8754956