基于CNN的三相逆变器开路故障诊断及其样本条件分析

Open-circuit fault diagnosis for three-phase inverters based on CNN and analysis of its sample conditions

为了合理选择样本条件以实现高效的智能化诊断,以及克服智能化方法中传统反向传播(back propagation, BP)网络权值较多、局部信息提取能力不足的问题,对基于卷积神经网络(convolutional neural network, CNN)的开路故障诊断方法进行研究,并以典型的三相两电平逆变器为具体对象,着重分析样本时长、样本数量变化时,CNN方法相较于BP网络方法在网络权值数量、训练稳定性、诊断准确率上的量化优势。结果表明,基于CNN的方法可在权值数量远少于BP网络方法的情况下构建深度更深的诊断模型,并在更短样本时长、更少训练样本数量下实现高效、准确的开路故障诊断。

In order to reasonably choose a sample condition which supports efficient intelligent diagnosis, and to overcome the problems of too many weights and weak local information extraction capability of intelligent traditional BP(back propagation) network, an open-circuit faults diagnosis method based on CNN(convolutional neural network) was studied. Moreover, by taking the typical three-phase two-level inverter as the specific object, the advantages of the CNN method on network weights number, network training stability and diagnosis effects under different conditions of sample durations and training sample numbers over the BP network method were analyzed quantitatively. Results show that the CNN method can build a deeper network model with much less weights than the BP network method, and it can achieve efficient and accurate model training and diagnosis with shorter and less samples.