基于卷积深度网络的高压真空断路器机械故障诊断方法

Fault diagnosis of high-voltage vacuum circuit breaker with a convolutional deep network

高压断路器执行机构所产生的振动信号蕴含着丰富的机械状态信息。针对传统基于浅层的振动信号分析法存在特征提取和泛化能力等方面的不足,提出一种基于卷积神经网络和长短期记忆网络的混合深度网络。该网络利用卷积层对原始振动数据进行特征转换,结合门控循环单元的局部时域特征表示能力,对故障敏感特征进行提取。通过对10 kV真空断路器振动信号的分析实验表明,所提出的混合网络模型在ROC曲线和PR曲线上的诊断性能要优于广泛应用的支持向量机诊断法。这种端到端的故障诊断策略通过振动信号特征的深度映射能够有效提高机械状态故障识别精度。

Information on mechanical conditions is contained in the vibration signals generated from the actuator control system of a high-voltage vacuum circuit breaker.To overcome the limitation on feature representation in the traditional fault detection methods,this paper proposes a hybrid network model combining a convolutional neural network and a long-short term memory network.The convolution layer and gate recurrent unit in the network are respectively used for the feature conversion and the extraction of local time-domain characteristics of the vibration signals.The feature extracted by the network layers is sensitive to the fault.Experiments on a 10 kV high voltage vacuum circuit breaker show that the proposed method is able to detect various types of mechanical faults.The ROC curve and PR curve show that the diagnostic effect of the proposed model is better than a SVM model.This end-to-end fault diagnosis strategy improves the diagnostic accuracy of the mechanical state of a high voltage circuit breaker by deepening feature mapping.