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2024 | OriginalPaper | Chapter

Implementing Deep Learning Models for Imminent Component X Failures Prediction in Heavy-Duty Scania Trucks

Authors : Jie Zhong, Zhenkan Wang

Published in: Advances in Intelligent Data Analysis XXII

Publisher: Springer Nature Switzerland

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Abstract

This paper explores the application of predictive maintenance (PdM) in vehicle management, focusing on improving performance and reliability of critical truck components. By leveraging a newly acquired, comprehensive real-world dataset, the study aims to develop machine learning models for accurately predicting component failures. The dataset, sourced from the Symposium on Intelligent Data Analysis (IDA 2024), includes multivariate time series data from an anonymized engine component of a fleet of trucks, featuring operational data, repair records, and specifications. The research employs advanced deep learning techniques like Convolutional and Recurrent Neural Networks, including Long Short-Term Memory (LSTM) networks, to identify patterns indicative of potential failures. This initiative aims to optimize maintenance interventions, resource allocation, and fleet management by predicting the time or class of potential failures, thereby reducing downtime and maintenance costs.

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Title: Implementing Deep Learning Models for Imminent Component X Failures Prediction in Heavy-Duty Scania Trucks
Authors: Jie Zhong
Zhenkan Wang
Publisher: Springer Nature Switzerland
Book: Advances in Intelligent Data Analysis XXII
Print ISBN: 978-3-031-58555-5

Electronic ISBN: 978-3-031-58553-1

Copyright Year: 2024
DOI: https://doi.org/10.1007/978-3-031-58553-1_22

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