Bayesian-based Component Lifetime Prediction Model Using Workshop and Telematics Data



Published Sep 4, 2023
Seungyoung Park Jihyun Lee


This paper presents a Bayesian approach to predicting brake pad and battery life based on field service data from a fleet management system(FMS). The data includes vehicle driving data collected via telematics and maintenance record data managed by the workshop. The proposed approach consists of three modules: component health diagnosis, workshop data analysis and driving pattern analysis. The health diagnosis module detects domain-based transformed feature, from the driving data, changes using KL divergence. The maintenance record data from workshop analysis module estimates the prior probability of maintenance cycles. The censored nature of workshop data is validated by updating the posterior probability using driving patterns from driving data. The driving pattern analysis module classifies driving patterns for lifetime prediction. This study develops a predictive maintenance model for brakes and batteries without additional sensors using the data required for fleet operation. The mileagebased cycle maintenance approach commonly used for fleet management is improved by this model. Future FMS systems are expected to make extensive use of this concept.

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lifetime prediction of brake pads and batteries, fleet management service telematics data, workshop data

Arena, F., Collotta, M., Luca, L., Ruggieri, M., & Termine, F. G. (2021, 12). Predictive maintenance in the automotive sector: A literature review. Mathematical and Computational Applications 2022, Vol. 27, Page 2, 27, 2. doi: 10.3390/MCA27010002

Gebraeel, N. Z., Lawley, M. A., Li, R., & Ryan, J. K. (2005, 6). Residual-life distributions from component degradation signals: A bayesian approach. IIE Transactions (Institute of Industrial Engineers), 37, 543-557. doi: 10.1080/07408170590929018

Leung, K.-M., Elashoff, R. M., & Afifi, A. A. (1997). Censoring issues in survival analysis. Public Health, 18, 83-104.

Prytz, R., Nowaczyk, S., Rognvaldsson, T., & Byttner, ¨ S. (2015, 5). Predicting the need for vehicle compressor repairs using maintenance records and logged vehicle data. Engineering Applications of Artificial Intelligence, 41, 139-150. doi: 10.1016/J.ENGAPPAI.2015.02.009

Vogl, G. W., Weiss, B. A., Helu, M., & moneerhelu, M. H. (2019). A review of diagnostic and prognostic capabilities and best practices for manufacturing. Journal of Intelligent Manufacturing, 30, 79-95. doi: 10.1007/s10845-016-1228-8

Voronov, S., Frisk, E., & Krysander, M. (2018, 6). Data-driven battery lifetime prediction and confidence estimation for heavy-duty trucks. IEEE Transactions on Reliability, 67, 623-629. doi: 10.1109/TR.2018.2803798

Yang, Z., Kanniainen, J., Krogerus, T., & Emmert-Streib, F. (2022, 5). Prognostic modeling of predictive maintenance with survival analysis for mobile work equipment. Scientific Reports 2022 12:1, 12, 1-20. doi: 10.1038/s41598-022-12572-z
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