TY - JOUR
T1 - Achieving Predictive and Proactive Maintenance for High-Speed Railway Power Equipment with LSTM-RNN
AU - Wang, Q.
AU - Bu, S.
AU - He, Z.
N1 - Funding Information:
Manuscript received September 14, 2019; revised November 11, 2019; accepted December 21, 2019. Date of publication January 13, 2020; date of current version June 22, 2020. This work was supported in part by the National Natural Science Foundation of China under Grant 51807171, in part by the Sichuan Science and Technology Program under Grant 2018RZ0075, in part by the Hong Kong Research Grant Council for the Research Project under Grant 25203917, Grant 15200418, and Grant 15219619, in part by Hong Kong Polytechnic University for the Start-up Fund Research Project under Grant 1-ZE68, and in part by the Open Project of National Rail Transit Electrification and Automation Engineering Technique Research Center in China under Grant NEEC-2019-B01. Paper no. TII-19-4230. (Corresponding author: Siqi Bu.) Q. Wang and S. Bu are with the Department of Electrical Engineering, The Hong Kong Polytechnic University, Hong Kong (e-mail: [email protected]; [email protected]).
Publisher Copyright:
© 2005-2012 IEEE.
PY - 2020/10
Y1 - 2020/10
N2 - Current maintenance mode for high-speed railway (HSR) power equipment is so outdated that can hardly adapt to the high-standard modern HSR. Therefore, a new possibility is proposed in this article to update the obsoleting maintenance mode of the HSR power equipment by adopting both predictive maintenance and proactive maintenance. With the combination of data-driven (predictive) and model-based (proactive) approaches, two principal constituents-the sample generator and the maintenance predictor-are designed. The maintenance predictor which is powered by the long short-term memory recurrent neural network is developed to realize the goal of predictive maintenance. The sample generator which is formulated by the physical degradation and failure model of HSR power equipment is proposed toward the goal of proactive maintenance. Test results on a gas-insulated switchgear have shown the powerful collaboration between the generator and the predictor, to not only accurately predict future maintenance timing of the switchgear based on historical sample data, but also enrich the data supply proactively to deal with potential data deficiency problems.
AB - Current maintenance mode for high-speed railway (HSR) power equipment is so outdated that can hardly adapt to the high-standard modern HSR. Therefore, a new possibility is proposed in this article to update the obsoleting maintenance mode of the HSR power equipment by adopting both predictive maintenance and proactive maintenance. With the combination of data-driven (predictive) and model-based (proactive) approaches, two principal constituents-the sample generator and the maintenance predictor-are designed. The maintenance predictor which is powered by the long short-term memory recurrent neural network is developed to realize the goal of predictive maintenance. The sample generator which is formulated by the physical degradation and failure model of HSR power equipment is proposed toward the goal of proactive maintenance. Test results on a gas-insulated switchgear have shown the powerful collaboration between the generator and the predictor, to not only accurately predict future maintenance timing of the switchgear based on historical sample data, but also enrich the data supply proactively to deal with potential data deficiency problems.
KW - Artificial intelligence (AI)
KW - deep learning
KW - high-speed railway (HSR)
KW - long short-term memory (LSTM) network
KW - power equipment
KW - predictive maintenance
KW - proactive maintenance
KW - recurrent neural network (RNN)
UR - http://www.scopus.com/inward/record.url?scp=85087829148&partnerID=8YFLogxK
U2 - 10.1109/TII.2020.2966033
DO - 10.1109/TII.2020.2966033
M3 - Journal article
SN - 1551-3203
VL - 16
SP - 6509
EP - 6517
JO - IEEE Transactions on Industrial Informatics
JF - IEEE Transactions on Industrial Informatics
IS - 10
M1 - 8957109
ER -