Multiple degradation mode analysis via gated recurrent unit mode recognizer and life predictors for complex equipment. Luo, Q., Chang, Y., Chen, J., Jing, H., Lv, H., & Pan, T. Computers in Industry, 123:103332, December, 2020. ![Multiple degradation mode analysis via gated recurrent unit mode recognizer and life predictors for complex equipment [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex In order to ensure the reliability and safety of industrial complex equipment, it is necessary to predict and manage the health status of the equipment. Remaining useful life (RUL) prediction is the decision basis of condition-based maintenance (CBM) and one of the main tasks in prognostics and health management (PHM). Complex systems tend to have multiple degradation modes, while similar degradation features may have significantly different RUL labels in different degradation modes, which can be called feature multi-label problem. To solve the problem, a novel RUL prediction method was proposed, which first analyzed the degradation mode and then utilized the predictor for RUL prediction under the specific mode. In particular, a modified de-noising auto-encoder (DAE) was proposed for nonlinear feature extraction and noise reduction. Mode recognizer and life predictors based on gated recurrent unit (GRU) and fuzzy k-means were proposed as the core modules. Case studies of commercial modular aero-propulsion system simulation data and the life cycle data of bearing were conducted to verify the effectiveness of the proposed method. Results show that the proposed method achieved much higher prediction accuracy than other methods.
@article{luo_multiple_2020,
title = {Multiple degradation mode analysis via gated recurrent unit mode recognizer and life predictors for complex equipment},
volume = {123},
issn = {0166-3615},
url = {http://www.sciencedirect.com/science/article/pii/S0166361520305662},
doi = {10.1016/j.compind.2020.103332},
abstract = {In order to ensure the reliability and safety of industrial complex equipment, it is necessary to predict and manage the health status of the equipment. Remaining useful life (RUL) prediction is the decision basis of condition-based maintenance (CBM) and one of the main tasks in prognostics and health management (PHM). Complex systems tend to have multiple degradation modes, while similar degradation features may have significantly different RUL labels in different degradation modes, which can be called feature multi-label problem. To solve the problem, a novel RUL prediction method was proposed, which first analyzed the degradation mode and then utilized the predictor for RUL prediction under the specific mode. In particular, a modified de-noising auto-encoder (DAE) was proposed for nonlinear feature extraction and noise reduction. Mode recognizer and life predictors based on gated recurrent unit (GRU) and fuzzy k-means were proposed as the core modules. Case studies of commercial modular aero-propulsion system simulation data and the life cycle data of bearing were conducted to verify the effectiveness of the proposed method. Results show that the proposed method achieved much higher prediction accuracy than other methods.},
language = {en},
urldate = {2020-10-26},
journal = {Computers in Industry},
author = {Luo, Qinyuan and Chang, Yuanhong and Chen, Jinglong and Jing, Hongjie and Lv, Haixin and Pan, Tongyang},
month = dec,
year = {2020},
keywords = {Complex equipment, Multiple degradation mode, Recurrent neural network, Remaining useful life},
pages = {103332},
}
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Complex systems tend to have multiple degradation modes, while similar degradation features may have significantly different RUL labels in different degradation modes, which can be called feature multi-label problem. To solve the problem, a novel RUL prediction method was proposed, which first analyzed the degradation mode and then utilized the predictor for RUL prediction under the specific mode. In particular, a modified de-noising auto-encoder (DAE) was proposed for nonlinear feature extraction and noise reduction. Mode recognizer and life predictors based on gated recurrent unit (GRU) and fuzzy k-means were proposed as the core modules. Case studies of commercial modular aero-propulsion system simulation data and the life cycle data of bearing were conducted to verify the effectiveness of the proposed method. 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