摘要
提出了一种基于大数据挖掘技术的、用于汽车运行工况数据采集设备的故障诊断方法。该方法依托中国汽车测试工况(CATC)项目和内蒙古道路运输企业节能降耗监控技术研究(NJ-2017-8),运用了k-means故障模式聚类算法、诊断状态参数Apriori关联规则、及相关性分析算法。并进行了案例实证分析。结果表明:采用k-mean算法能有效分析数据采集设备的故障模式,Apriori算法挖掘出的数据采集设备特征参数之间的关联规则与相关性能,有效地找出数据采集设备的故障环节和原因;与设备实际维修试验结果相符。因此,该方法可为数据采集设备的运行维护与管控提供参考。
A fault diagnosis method of vehicles driving cycle data acquisition devices were proposed based on a project sponsored by the China Automotive Test Cycle(CATC) and the Research on Monitoring Technology of Energy Saving and Consumption Reduction for Road Transportation Enterprises in Inner Mongolia(NJ-2017-8) by using large data mining technology, a k-means clustering algorithm, an Apriori algorithm association rules, a correlation analysis algorithm, and made an empirical study with a case. The results show that the fault modes of data acquisition devices can be effectively analyzed by k-means algorithm, and the fault diagnosis and causes of data acquisition devices can effectively find out by Apriori algorithm of association rules and correlation between the characteristic parameters of data acquisition devices, which are consistent with the actual maintenance test results. Therefore, this method provides a reference for the operation, maintenance and control of data acquisition devices.
作者
张宏
ZHANG Hong(Transportation Institute of Inner Mongolia University,Hohhot 010070,China;Inner Mongolia Engineering Research Centre for Urban Transportation Data Science and Applications,Hohhot 010070,China)
出处
《汽车安全与节能学报》
CAS
CSCD
2019年第1期54-59,共6页
Journal of Automotive Safety and Energy
基金
工业和信息化部项目(中国新能源汽车产品检测工况研究和开发CATC)
内蒙古自治区交通运输厅科技项目(NJ-2017-8)
中央高校基本科研业务费专项资金项目(310822171134)
中国国家留学基金资助(201806815002)
