基于深度学习的电气大数据故障分析模型研究

Research on Fault Analysis Model of Electrical Appliance Large Data Based on Deep Learning

汽车智能化技术的快速发展使整车功能日益复杂化、文件开发数激增,对传统车辆电气故障解析提出新挑战。为了提升电子电气故障分析与诊断能力,利用智能诊断技术,构建电气数据分析模型,辅助人工实现汽车电气系统故障的智能分析与定位。模型构建分为两步:一是电气测试数据的收集与预处理,包括报文解析、数据清洗及特征提取;二是利用深度学习算法构建故障分析模型,通过模型训练实现智能故障分析与定位。提出基于智能诊断技术的汽车电气数据分析模型,能够高效解析复杂电气测试数据,实现故障智能分析与定位,为电子电气故障排查提供有力支持。

The rapid development of automotive intelligent technology has led to increased complexity of vehicle functions and a surge in the number of document developments,posing new challenges for the analysis of traditional vehicle electrical faults.In order to enhance the capability of electronic and electrical fault analysis and diagnosis,an intelligent diagnostic technology is utilized to construct an electrical data analysis model,which aids in the intelligent analysis and localization of faults in the automotive electrical system.The model construction is divided into two steps:firstly,the collection and preprocessing of electrical test data,including message parsing,data cleaning,and feature extraction;secondly,the use of deep learning algorithms to build a fault analysis model,achieving intelligent fault analysis and localization through model training.The proposed automotive electrical data analysis model based on intelligent diagnostic technology is capable of efficiently parsing complex electrical test data,realizing intelligent fault analysis and localization,and providing strong support for the troubleshooting of electronic and electrical faults.