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Method and Apparatus for Fault Diagnosis of Automobile Brake System Using Vibration Signals

机译:基于振动信号的汽车制动系统故障诊断方法及装置

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摘要

This paper presents method of vibration based continuous monitoring system and analysis using machine learning approach. The reliable and effective performance of a braking system is fundamental operation of most vehicles. This study provides insight of fault diagnosis of hydraulic braking system by vibration analysis. A hydraulic brake system test rig was fabricated. The vibration signals were acquired from a piezoelectric transducer for both good as well as faulty conditions of brakes. The statistical parameters are extracted and the good features that discriminate different faulty conditions were formed using decision tree. This study presents the data model (J48, C4.5 decision tree algorithm) for fault diagnosis through descriptive statistical features extracted from vibration signals of good and faulty conditions of hydraulic brakes. The classification results of decision tree algorithm for fault diagnosis of a hydraulic brake system are presented. The model built can be used for condition monitoring of hydraulic brake system. The classification accuracy for decision tree algorithm using statistical features is found to be 97.45%.
机译:本文提出了基于振动的连续监测系统方法,并使用机器学习方法进行了分析。制动系统的可靠和有效性能是大多数车辆的基本操作。该研究通过振动分析为液压制动系统的故障诊断提供了见识。制造了液压制动系统试验台。振动信号是从压电换能器获取的,用于判断制动器的状况是否良好。使用决策树提取统计参数,并形成区分不同故障条件的良好特征。本研究通过从液压制动器良好和故障状态的振动信号中提取描述性统计特征,提出了用于故障诊断的数据模型(J48,C4.5决策树算法)。给出了液压制动系统故障诊断的决策树算法分类结果。建立的模型可用于液压制动系统的状态监测。发现使用统计特征的决策树算法的分类精度为97.45%。

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