基于卷积神经网络的逆变器故障诊断方法

doi:10.19562/j.chinasae.qcgc.2022.01.017

Abstract

Abstract:

For the risk of inverter failure in the permanent magnet synchronous motor （PMSM） drive system of the electric vehicles during long-term operation， a fault diagnosis method based on convolutional neural network （CNN） is proposed in this paper. Firstly， three-phase stator current data is normalized and filtered to an electric cycle current data， reducing the impact of variable torque and variable speed conditions of the motor drive system on the fault diagnosis effect. Then， the CNN， for the advantage of fault feature extraction and noise immunity， is used for inverter fault diagnosis. In MATLAB/Simulink， the PMSM drive system model of the electric vehicle is built. The fault injection simulation and experimental data is used to construct the dataset for the CNN， and the effectiveness of the proposed fault diagnosis method is verified. In addition， the applicability of the method is explored in the case of training sample dataset sparsification and motor model differentiation.The simulation results show that the fault diagnosis method proposed in this paper is robust and universal under the conditions of noise data and sparse data.

Key words: inverter, three-phase stator current, convolutional neural network, fault diagnosis

Hai Yu,Junjun Deng,Zhenpo Wang,Fengchun Sun. Inverter Fault Diagnosis Method Based on Convolutional Neural Network[J].Automotive Engineering, 2022, 44(1): 142-152.

Figures/Tables 16

参量	符号	数值
参量	符号	模型1	模型2	平台1
极对数	$p$	8	4	4
永磁磁链	$ψ m$ /Wb	0.043 7	0.016 54	0.483 33
交轴电感	$L q$ /H	2.975 8×10^-4	5×10^-5	0.002
直轴电感	$L d$ /H	2.436 8×10^-4	5×10^-5	0.002
定子电阻	$R s$ /Ω	0.010 1	0.007 9	0.31
电源电压	$U d c$ /V	325	60	400
采样频率	$f s$ /kHz	10	20	10
IGBT开关频率	$f s w$ /kHz	10	20	10

References 18

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故障情况	故障位置
正常	无（N）
半导体功率开关管开路故障	IGBT1（K1）	IGBT2（K2）	IGBT3（K3）	IGBT4（K4）	IGBT5（K5）	IGBT6（K6）
相电路开路故障	A相电路（D1）		B相电路（D2）		C相电路（D3）
电流传感器信号丢失故障	A相电流传感器（C1）		B相电流传感器（C2）		C相电流传感器（C3）

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Inverter Fault Diagnosis Method Based on Convolutional Neural Network

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