基于数据流的电控汽车故障智能检测方法研究

Research on Intelligent Fault Detection Method of Electronic Control Vehicle based on Data Flow

为了提高电控汽车故障智能检测能力,提出基于数据流的电控汽车故障智能检测方法。采用大数据传感信息感知方法进行电控汽车故障原始样本信息采样,对采集的电控汽车故障特征序列进行信息重构和非线性结构重组,建立电控汽车故障信息特征分布式信息拟合模型,采用谱特征分析方法进行电控汽车故障样本序列的特征提取和数据融合,采用模糊空间序列融合的方法实现对电控汽车故障数据流的分类识别和聚类处理,对故障样本数据进行多元特征重构和信息拟合,结合数据流分析和故障信息融合聚类分析方法,实现对电控汽车故障的优化检测。仿真结果表明,采用该方法进行电控汽车故障检测的准确性较高,检测性能较好,具有很好的故障诊断和实时分析能力。

In ord to improve that intelligent detection capability of the electric control automobile,an intelligent detection method of the electric control automobile fault based on the data flow is proposed.by adopting a large-data sensing information sensing method to carry out information sampling of an electric control automobile fault original sample,carrying out information reconstruction and a non-linear structure recombination on the collected electric control automobile fault characteristic sequence,and establishing a distributed information fitting model of an electric control automobile fault information characteristic,the characteristic extraction and the data fusion of the fault sample sequence of the electric control automobile are carried out by adopting a spectral characteristic analysis method,the classification identification and the clustering processing of the fault data flow of the electric control automobile are realized by adopting a method of fusion of the fuzzy space sequence,the fault sample data is subjected to the multi-component characteristic reconstruction and the information fitting,By combining the data flow analysis and the fault information fusion cluster analysis method,the optimal detection of the electric control car fault is realized.The simulation results show that the method is high in accuracy,good in detection performance and good in fault diagnosis and real-time analysis.