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Remaining Useful Life Estimation for Railway Gearbox Bearings Using Machine Learning
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0001-5269-3900
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0003-0416-1787
2023 (English)In: Lecture Notes in Computer Science, Springer Science and Business Media Deutschland GmbH , 2023, p. 62-77Conference paper, Published paper (Refereed)
Abstract [en]

Gearbox bearing maintenance is one of the major overhaul cost items for railway electric propulsion systems. They are continuously exposed to challenging working conditions, which compromise their performance and reliability. Various maintenance strategies have been introduced over time to improve the operational efficiency of such components, while lowering the cost of their maintenance. One of these is predictive maintenance, which makes use of previous historical data to estimate a component’s remaining useful life (RUL). This paper introduces a machine learning-based method for calculating the RUL of railway gearbox bearings. The method uses unlabeled mechanical vibration signals from gearbox bearings to detect patterns of increased bearing wear and predict the component’s residual life span. We combined a data smoothing method, a change point algorithm to set thresholds, and regression models for prediction. The proposed method has been validated using real-world gearbox data provided by our industrial partner, Alstom Transport AB in Sweden. The results are promising, particularly with respect to the predicted failure time. Our model predicted the failure to occur on day 330, while the gearbox bearing’s actual lifespan was 337 days. The deviation of just 7 days is a significant result, since an earlier RUL prediction value is usually preferable to avoid unexpected failure during operations. Additionally, we plan to further enhance the prediction model by including more data representing failing bearing patterns.

Place, publisher, year, edition, pages
Springer Science and Business Media Deutschland GmbH , 2023. p. 62-77
Series
Lecture Notes in Computer Science, ISSN 0302-9743 ; 14198 LNCS
Keywords [en]
Gearbox bearing, Machine learning, Predictive maintenance, Railway, Remaining useful life, Bearings (machine parts), Forecasting, Gears, Maintenance, Railroads, Regression analysis, Vibrations (mechanical), Cost items, Electric propulsion systems, Life estimation, Lifespans, Machine-learning, Major overhauls, Remaining useful lives
National Category
Other Civil Engineering
Identifiers
URN: urn:nbn:se:mdh:diva-64606DOI: 10.1007/978-3-031-43366-5_4ISI: 001156323700004Scopus ID: 2-s2.0-85174442316ISBN: 9783031433658 (print)OAI: oai:DiVA.org:mdh-64606DiVA, id: diva2:1807638
Conference
5th International conference on Reliability, Safety and Security of Railway Systems: Modelling, Analysis, Verification and Certification, RSS Rail 2023, Berlin, Germany, 10 October - 12 October 2023
Available from: 2023-10-27 Created: 2023-10-27 Last updated: 2024-02-26Bibliographically approved

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Beqiri, LodianaBakhshi Valojerdi, ZeinabPunnekkat, SasikumarCicchetti, Antonio

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