基于AdaBoost算法的新能源汽车电机异常故障检测
Abnormal Fault Detection of New Energy Vehicle Motor Based on AdaBoost Algorithm
倪龙飞 1白倩 1张治斌2
作者信息
- 1. 黄河交通学院智能工程学院,河南 焦作 454950
- 2. 河南理工大学,河南 焦作 454950
- 折叠
摘要
新能源汽车的电机系统包含许多复杂的部件和子系统,部件之间的相互作用使得异常故障的检测变得复杂,而电机异常故障检测主要采用人工检测方式,即通过耳朵听声音,用眼睛观察,用手触摸找出故障位置,导致故障检测精度较低.因此,提出AdaBoost算法下新能源汽车电机异常故障检测方法.通过传感器采集电机信号,采用距离相似度、模糊隶属度函数提取信号特征,借助遗传算法的编码操作、交叉操作及其变异操作获取关键信号特征,运用自适应增强(Adaptive Boosting,AdaBoost)算法将信号特征分成正常信号和异常故障,以此实现对新能源汽车电机异常故障检测.实验结果表明,所提算法电机异常故障检测精度高,且耗时短.
Abstract
The motor system of new energy vehicles contains many complex components and subsystems.The in-teraction between these components makes the detection of abnormal faults complex.At present,the detection of motor abnormal faults mainly adopts manual detection methods,so the detection accuracy is relatively low.This paper pres-ented a method of detecting abnormal faults of new energy vehicle motors based on AdaBoost algorithm.Firstly,we collected motor signals through sensors.Then,we extracted signal features by distance similarity and fuzzy membership functions.Meanwhile,we used encoding operation,crossover operation,and mutation operation of the ge-netic algorithm to obtain key signal features.Finally,we used the Adaptive Boosting(AdaBoost)algorithm to divide signal features into normal signals and abnormal faults.Thus,we realized the detection of abnormal faults in new ener-gy vehicle motors.Experimental results prove that the proposed algorithm has high accuracy in detecting motor abnor-mal faults and takes less time.
关键词
弱分类器/强分类器/遗传算法/新能源汽车/电机异常故障检测Key words
AdaBoost algorithm/Weak classifier/Strong classifier/Genetic algorithm/New energy vehicles/Motor abnormal fault detection引用本文复制引用
基金项目
河南省科技攻关计划(232102241028)
河南省工程技术研究中心项目(266)
出版年
2024
NETL
NSTL
万方数据