Vehicle Online Monitoring System Based on Fuzzy Classifier
Vehicle Online Monitoring System Based on Fuzzy Classifier
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发表时间：
2014
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通讯作者：
Diana María;Gómez Jaramillo;Claudia Victoria;Isaza Narváez
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作者：
Diana María;Gómez Jaramillo;Claudia Victoria;Isaza Narváez
— 在汽车领域，电子、机械和软件组件已经显着发展，导致车辆故障诊断的复杂性增加。模糊分类技术的使用已适用于复杂系统的在线诊断。特别是，多元数据分析学习算法 (LAMDA) 模糊分类器通过全局充分度 (GAD) 提供附加信息，允许执行早期预防措施并在决策过程中支持操作员。提出一种基于LAMDA模糊分类器的汽车故障诊断系统。该算法在车辆行驶时（在线监控）识别车辆的状态，即正常驾驶行为、攻击性驾驶（反映不耐烦或愤怒的驾驶员的驾驶行为）或机械故障。监控系统的实施是在一辆中型雷诺汽车上进行的。该算法以较低的计算成本实现了 92.52% 的功能状态识别正确率。
— In the automotive sector, electronic, mechanical, and software components have evolved significantly, resulting in increased complexity in vehicle fault diagnosis. The use of fuzzy classification techniques has been adapted for the online diagnosis of complex systems. In particular, Learning Algorithm for Multivariate Data Analysis (LAMDA) fuzzy classifier provides additional information through the Global Adequacy Degree (GAD) allowing to perform early preventive actions and supporting the operator in the decision-making process. This paper presents a car fault diagnosis system based on the LAMDA fuzzy classifier. The algorithm identifies, while the vehicle is in motion (online monitoring), the state of the vehicle, i.e., normal driving behavior, aggressive driving (driving behavior reflecting an impatient or angry driver) or mechanical failure. The implementation of the monitoring system implementation is performed in a midrange Renault vehicle. The algorithm achieves a 92.52% correct functional state identification with a low computational cost.