基于RS和AHNs的轮毂电机故障模糊诊断法

A Fuzzy Diagnosis Method Based on RS and AHNs for Faults of In-Wheel Motor

为有效监测电动汽车轮毂电机在复杂工况下的运行状态,保证其运行安全,提出了一种基于粗糙集(rough set,简称RS)理论和人工碳氢网络(artificial hydrocarbon networks,简称AHNs)的轮毂电机故障模糊诊断方法。首先,以轮毂电机运行安全为目标,重点考虑转速和负载转矩对振动信号的影响程度,用特征参数表征轮毂电机运行状态,并基于RS理论提出一种特征参数的离散化方法,对输入层进行了模糊化处理;其次,基于分子间能量优化AHNs算法,建立初步诊断模型,并考虑不同输出状态类型的模糊性,利用模糊理论建立AHNs多输出的隶属度函数,构建轮毂电机故障模糊诊断模型,实现了对复杂工况下轮毂电机故障的诊断;最后,通过轮毂电机机械故障台架试验验证了该方法的有效性。

In order to effectively monitor some faults of in-wheel motor used in electric vehicle and ensure its operation safety,a fuzzy diagnosis method based on rough set(RS)theory and artificial hydrocarbon networks(AHNs)is proposed for detecting faults of in-wheel motor.Aiming at the operation safety of in-wheel motor,vibration signal was analyzed under the influence of the rotating speed and load torque to refine some symptom parameters(SPs)for characterizing the operation state of in-wheel motor.RS theory is used for proposing a discretization method of SPs with the fuzzy processing of the input layer.AHNs is employed to build a classifier,and its intermolecular energy is considered to optimize AHNs algorithm.Then,fuzzy theory is used for establishing the membership functions of the AHNs multi-outputs,so as to build a fuzzy diagnosis model for realizing the fault diagnosis of the in-wheel motor under complex working conditions.Finally,the effectiveness of the proposed method is verified by in-wheel motor bench test.