基于神经网络的电动机逆变驱动系统故障诊断

针对电动机变频调速系统中逆变器开关元件故障类型多，传统故障诊断方法难以实现故障分离等情况，本文提出了一种基于神经网络的电动机变频调速系统故障诊断方法。通过对逆变器输出信号的谱分析可以获得对故障敏感的故障特征量，将这些故障特征量输入神经网络后，由网络输出层的结点输出可以判断故障类型，从而实现故障分离。研究结果表明，该方法可有效实现开关元件断路、短路故障，为进一步实现逆变器容错驱动奠定了理论基础。

Fault diagnosis of inverter-fed motor drives based on neural network

Aiming at more switch faults occurred in the inverter and difficulties in isolating the faults using conventional diagnosis technique, this paper investigates the use of neural network for fault diagnosis in inverter-fed motor drive systems. The inverter voltage signal is acquired, and the relative spectral residue is obtained by analyzing the spectral of the inverter signal. All these residues can be viewed as fault features and sent to the network for training. Observing the outputs of the network, the faults can be isolated. The proposed technique can isolate the open-switch and short-switch faults occurred in the inverter. Simulation result shows that the method is effective and the diagnosis is accurate. The study lays a theoretical foundation for fault tolerant drive system.