Analysis of Machine Learning Techniques in Fault Diagnosis of Vehicle Fleet Tracking Modules

Abstract

In this paper, four techniques of machine learning (ML) were applied and analyzed during the diagnosis of failures in vehicle fleet tracking modules. A comparison of the sampling methods was carried out considering the training and testing process using real data provided by DDMX, that acts in vehicle fleet tracking. A methodology was defined for pre-processing the collected data before the application of the ML techniques. Totally 16 models were created using the Random Forest (RF), Naive Bayes (NB), Support Vector Machine (SVM) and Multi Layer Perceptron (MLP) techniques. We have obtained promising results, where the techniques achieved a precision of 99.76% and 99.68% for detection and isolation of faults, respectively, on the provided dataset. These models can serve as prototypes to diagnose faults remotely and states that traditional ML techniques with manual feature extraction are still able to achieve high metrics.