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

Abstract

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.