Performance evaluation of machine learning techniques for fault diagnosis in vehicle fleet tracking modules

Journal article


Sepulevene, Luis, Drummond, Isabela, Kuehne, Bruno Tardiole, Frinhani, Rafael, Filho, Dionisio Leite, Peixoto, Maycon, Reiff-Marganiec, Stephan and Batista, Bruno 2021. Performance evaluation of machine learning techniques for fault diagnosis in vehicle fleet tracking modules. The Computer Journal. https://doi.org/10.1093/comjnl/bxab047
AuthorsSepulevene, Luis, Drummond, Isabela, Kuehne, Bruno Tardiole, Frinhani, Rafael, Filho, Dionisio Leite, Peixoto, Maycon, Reiff-Marganiec, Stephan and Batista, Bruno
Abstract

With industry 4.0, data-based approaches are in vogue. However, extracting the essential features is not a trivial task and greatly influences the fi nal result. There is also a need for specialized system knowledge to monitor the environment and diagnose faults. In this context, the diagnosis of faults is signi cant, for example, in a vehicle fleet monitoring system, since it is possible to diagnose faults even before the customer is aware of the fault, minimizing the maintenance costs of the modules. In this paper, several models using Machine Learning (ML) techniques were applied and analyzed during the fault diagnosis process in vehicle fleet tracking modules. Two approaches were proposed, "With Knowledge" and "Without Knowledge", to explore the dataset using ML techniques to generate classi fiers that can assist in the fault diagnosis process. The approach "With Knowledge" performs the feature extraction manually, using the ML techniques: Random Forest, Naive Bayes, Support Vector Machine (SVM) and
Multi Layer Perceptron (MLP); on the other hand, the approach "Without Knowledge" performs an automatic feature extraction, through a Convolutional Neural Network (CNN). The results showed that the proposed approaches are promising. The best models with manual feature extraction obtained a precision of 99.76% and 99.68% for detection and detection and isolation of faults, respectively, in the provided dataset. The best models performing an automatic feature extraction obtained respectively 88.43% and 54.98% for detection and detection and isolation of failures.

KeywordsMachine Learning; Fault Diagnosis; Feature Extraction; Convolutional Neural Networks
Year2021
JournalThe Computer Journal
PublisherOxford University Press
ISSN1460-2067
Digital Object Identifier (DOI)https://doi.org/10.1093/comjnl/bxab047
Web address (URL)https://academic.oup.com/comjnl/advance-article-abstract/doi/10.1093/comjnl/bxab047/6275474?redirectedFrom=fulltext
hdl:10545/625766
Output statusPublished
Publication dates14 May 2021
Publication process dates
Deposited17 May 2021, 11:24
Accepted31 Mar 2021
ContributorsFederal University of Itajubá, Itajubá, Brazil, Federal University of Mato Grosso do Sul, Ponta Porã, Brazil, Federal University of Bahia, Salvador, Brazil and University of Derby
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