Abnormal Fault Detection of New Energy Vehicle Motor Based on AdaBoost Algorithm
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.
AdaBoost algorithmWeak classifierStrong classifierGenetic algorithmNew energy vehiclesMotor abnormal fault detection
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