Remaining useful life estimation of engineered systems using vanilla LSTM neural networks. Wu, Y., Yuan, M., Dong, S., Lin, L., & Liu, Y. Neurocomputing, 275:167–179, January, 2018. ![Remaining useful life estimation of engineered systems using vanilla LSTM neural networks [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex Long Short-Term Memory (LSTM) networks are a significant branch of Recurrent Neural Networks (RNN), capable of learning long-term dependencies. In recent years, vanilla LSTM (a variation of original LSTM above) has become the state-of-the-art model for a variety of machine learning problems, especially Natural Language Processing (NLP). However, in industry, this powerful Deep Neural Network (DNN) has not aroused wide concern. In research focusing on Prognostics and Health Management (PHM) technology for complex engineered systems, Remaining Useful Life (RUL) estimation is one of the most challenging problems, which can lead to appropriate maintenance actions to be scheduled proactively to avoid catastrophic failures and minimize economic losses of the systems. Following that, this paper aims to propose utilizing vanilla LSTM neural networks to get good RUL prediction accuracy which makes the most of long short-term memory ability, in the cases of complicated operations, working conditions, model degradations and strong noises. In addition, to promote cognition ability about model degradation processes, a dynamic differential technology was proposed to extract inter-frame information. The whole proposition is illustrated and discussed by performing tests on a case of the health monitoring of aircraft turbofan engines which have four different issues. Performances of vanilla LSTM are benchmarked with standard RNN and Gated Recurrent Unit (GRU) LSTM. Results show the significance of performance improvement achieved by vanilla LSTM.
@article{wu_remaining_2018,
title = {Remaining useful life estimation of engineered systems using vanilla {LSTM} neural networks},
volume = {275},
issn = {0925-2312},
url = {https://www.sciencedirect.com/science/article/pii/S0925231217309505},
doi = {10.1016/j.neucom.2017.05.063},
abstract = {Long Short-Term Memory (LSTM) networks are a significant branch of Recurrent Neural Networks (RNN), capable of learning long-term dependencies. In recent years, vanilla LSTM (a variation of original LSTM above) has become the state-of-the-art model for a variety of machine learning problems, especially Natural Language Processing (NLP). However, in industry, this powerful Deep Neural Network (DNN) has not aroused wide concern. In research focusing on Prognostics and Health Management (PHM) technology for complex engineered systems, Remaining Useful Life (RUL) estimation is one of the most challenging problems, which can lead to appropriate maintenance actions to be scheduled proactively to avoid catastrophic failures and minimize economic losses of the systems. Following that, this paper aims to propose utilizing vanilla LSTM neural networks to get good RUL prediction accuracy which makes the most of long short-term memory ability, in the cases of complicated operations, working conditions, model degradations and strong noises. In addition, to promote cognition ability about model degradation processes, a dynamic differential technology was proposed to extract inter-frame information. The whole proposition is illustrated and discussed by performing tests on a case of the health monitoring of aircraft turbofan engines which have four different issues. Performances of vanilla LSTM are benchmarked with standard RNN and Gated Recurrent Unit (GRU) LSTM. Results show the significance of performance improvement achieved by vanilla LSTM.},
language = {en},
urldate = {2021-09-28},
journal = {Neurocomputing},
author = {Wu, Yuting and Yuan, Mei and Dong, Shaopeng and Lin, Li and Liu, Yingqi},
month = jan,
year = {2018},
keywords = {Dynamic differential feature, Long short-term memory neural network, Prognostics and health management, Remaining useful life estimation},
pages = {167--179},
}
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