Remaining useful life estimation using a bidirectional recurrent neural network based autoencoder scheme. Yu, W., Kim, I. Y., & Mechefske, C. Mechanical Systems and Signal Processing, 129:764–780, August, 2019. ![Remaining useful life estimation using a bidirectional recurrent neural network based autoencoder scheme [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex System remaining useful life (RUL) estimation is one of the major prognostic activities in industrial applications. In this paper, we propose a sensor-based data-driven scheme using a deep learning tool and the similarity-based curve matching technique to estimate the RUL of a system. The whole procedure consists of two steps: in the first step, a bidirectional recurrent neural network based autoencoder is trained in an unsupervised way to convert the multi-sensor (high-dimensional) readings collected from historical run-to-failure instances (i.e. multiple units of the same system) to low-dimensional embeddings, which are used to construct the one-dimensional health index (HI) values to reflect various health degradation patterns of the instances. In the second step, the test HI curve obtained from sensor readings collected from an on-line instance is compared with the degradation patterns built in the offline phase using the similarity-based curve matching technique, from which the RUL of the test unit can be estimated at an early stage. The proposed scheme was tested on two publicly available run-to-failure datasets: the turbofan engine datasets (simulation datasets) and the milling datasets (experimental datasets). The prognostic performance of the proposed procedure was directly compared with the existing state-of-art prognostic models in terms of various prognostic metrics on the two datasets respectively. The comparison results demonstrate the competitiveness of the proposed method used for RUL estimation of systems.
@article{yu_remaining_2019,
title = {Remaining useful life estimation using a bidirectional recurrent neural network based autoencoder scheme},
volume = {129},
issn = {0888-3270},
url = {https://www.sciencedirect.com/science/article/pii/S0888327019303061},
doi = {10.1016/j.ymssp.2019.05.005},
abstract = {System remaining useful life (RUL) estimation is one of the major prognostic activities in industrial applications. In this paper, we propose a sensor-based data-driven scheme using a deep learning tool and the similarity-based curve matching technique to estimate the RUL of a system. The whole procedure consists of two steps: in the first step, a bidirectional recurrent neural network based autoencoder is trained in an unsupervised way to convert the multi-sensor (high-dimensional) readings collected from historical run-to-failure instances (i.e. multiple units of the same system) to low-dimensional embeddings, which are used to construct the one-dimensional health index (HI) values to reflect various health degradation patterns of the instances. In the second step, the test HI curve obtained from sensor readings collected from an on-line instance is compared with the degradation patterns built in the offline phase using the similarity-based curve matching technique, from which the RUL of the test unit can be estimated at an early stage. The proposed scheme was tested on two publicly available run-to-failure datasets: the turbofan engine datasets (simulation datasets) and the milling datasets (experimental datasets). The prognostic performance of the proposed procedure was directly compared with the existing state-of-art prognostic models in terms of various prognostic metrics on the two datasets respectively. The comparison results demonstrate the competitiveness of the proposed method used for RUL estimation of systems.},
language = {en},
urldate = {2021-09-28},
journal = {Mechanical Systems and Signal Processing},
author = {Yu, Wennian and Kim, II Yong and Mechefske, Chris},
month = aug,
year = {2019},
keywords = {Autoencoder, Bidirectional recurrent neural network, Health index, Remaining useful life},
pages = {764--780},
}
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The whole procedure consists of two steps: in the first step, a bidirectional recurrent neural network based autoencoder is trained in an unsupervised way to convert the multi-sensor (high-dimensional) readings collected from historical run-to-failure instances (i.e. multiple units of the same system) to low-dimensional embeddings, which are used to construct the one-dimensional health index (HI) values to reflect various health degradation patterns of the instances. In the second step, the test HI curve obtained from sensor readings collected from an on-line instance is compared with the degradation patterns built in the offline phase using the similarity-based curve matching technique, from which the RUL of the test unit can be estimated at an early stage. The proposed scheme was tested on two publicly available run-to-failure datasets: the turbofan engine datasets (simulation datasets) and the milling datasets (experimental datasets). The prognostic performance of the proposed procedure was directly compared with the existing state-of-art prognostic models in terms of various prognostic metrics on the two datasets respectively. 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The proposed scheme was tested on two publicly available run-to-failure datasets: the turbofan engine datasets (simulation datasets) and the milling datasets (experimental datasets). The prognostic performance of the proposed procedure was directly compared with the existing state-of-art prognostic models in terms of various prognostic metrics on the two datasets respectively. The comparison results demonstrate the competitiveness of the proposed method used for RUL estimation of systems.},\n\tlanguage = {en},\n\turldate = {2021-09-28},\n\tjournal = {Mechanical Systems and Signal Processing},\n\tauthor = {Yu, Wennian and Kim, II Yong and Mechefske, Chris},\n\tmonth = aug,\n\tyear = {2019},\n\tkeywords = {Autoencoder, Bidirectional recurrent neural network, Health index, Remaining useful life},\n\tpages = {764--780},\n}\n\n\n\n","author_short":["Yu, W.","Kim, I. 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