With industry 4.0, data-based approaches are in vogue. However, extracting the essential features is not a trivial task and greatly influences the final 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 significant, 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 classifiers 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 and Multi Layer Perceptron; on the other hand, the approach ‘Without Knowledge’ performs an automatic feature extraction, through a convolutional neural network. 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.

中文翻译：

---

车队跟踪模块故障诊断机器学习技术的性能评估

随着工业 4.0，基于数据的方法正在流行。然而，提取基本特征并非易事，对最终结果有很大影响。还需要专门的系统知识来监控环境和诊断故障。在这种情况下，故障诊断非常重要，例如在车队监控系统中，因为甚至可以在客户意识到故障之前诊断故障，从而最大限度地降低模块的维护成本。在本文中，在车队跟踪模块的故障诊断过程中应用和分析了几个使用机器学习 (ML) 技术的模型。提出了两种方法：“有知识”和“没有知识”，使用 ML 技术探索数据集以生成有助于故障诊断过程的分类器。“With Knowledge”方法使用 ML 技术手动执行特征提取：随机森林、朴素贝叶斯、支持向量机和多层感知器；另一方面，“无知识”方法通过卷积神经网络执行自动特征提取。结果表明，所提出的方法是有前途的。在提供的数据集中，具有手动特征提取的最佳模型分别获得了 99.76% 和 99.68% 的检测、检测和故障隔离精度。执行自动特征提取的最佳模型分别获得了 88.43% 和 54.98% 用于检测和检测和隔离故障。支持向量机和多层感知器；另一方面，“无知识”方法通过卷积神经网络执行自动特征提取。结果表明，所提出的方法是有前途的。在提供的数据集中，具有手动特征提取的最佳模型分别获得了 99.76% 和 99.68% 的检测、检测和故障隔离精度。执行自动特征提取的最佳模型分别获得了 88.43% 和 54.98% 用于检测和检测和隔离故障。支持向量机和多层感知器；另一方面，“无知识”方法通过卷积神经网络执行自动特征提取。结果表明，所提出的方法是有前途的。在提供的数据集中，具有手动特征提取的最佳模型分别获得了 99.76% 和 99.68% 的检测、检测和故障隔离精度。执行自动特征提取的最佳模型分别获得了 88.43% 和 54.98% 用于检测和检测和隔离故障。在提供的数据集中，具有手动特征提取的最佳模型分别获得了 99.76% 和 99.68% 的检测、检测和故障隔离精度。执行自动特征提取的最佳模型分别获得了 88.43% 和 54.98% 用于检测和检测和隔离故障。在提供的数据集中，具有手动特征提取的最佳模型分别获得了 99.76% 和 99.68% 的检测、检测和故障隔离精度。执行自动特征提取的最佳模型分别获得了 88.43% 和 54.98% 用于检测和检测和隔离故障。