基于双谱与双流卷积神经网络的断路器故障诊断  

Fault diagnosis of circuit breaker based on bispectrum and two-stream convolutional neural network

作　　者：林穿 徐启峰[1] Lin Chuan;Xu Qifeng(School of Electrical Engineering and Automation,Fuzhou University,Fuzhou 350108,China)

机构地区：[1]福州大学电气工程与自动化学院,福州350108

出　　处：《电子测量技术》2021年第23期165-172,共8页Electronic Measurement Technology

基　　金：国家自然科学基金(51977038)项目资助。

摘　　要：高压断路器操动机构的振动信号包含了断路器运行状态的重要信息,对操动机构工作状态的诊断辨识十分重要。针对振动信号随机、非平稳的复杂特性,提出了一种基于双谱分析和双通道流浅层卷积神经网络的断路器故障诊断方法。对振动信号进行双谱分析和小波分析,分别提取2D双谱矩阵以及1D小波频带能量作为双流卷积神经网络的双通道特征;对断路器模拟实验采集到的5种工况下的振动信号进行有监督训练。结果表明,双谱分析能够抑制高斯噪声、保留操动机构不同工况下主要峰值形态特征并融合小波频带能量特征,所提模型训练迭代5次即可达到98.33%的高识别精度,实现断路器操动机构的故障诊断辨识。High-voltage circuit breaker operating mechanism vibration signal contains important information about the status of the circuit breaker,which is of great significance for the diagnosis and identification of the operating status of the operating mechanism.Aiming at the complex characteristics of random and non-smooth vibration signals,a circuit breaker fault diagnosis method based on bispectrum analysis and a two-stream flow shallow convolutional neural network is proposed.Bispectral analysis and wavelet analysis are performed on the vibration signal.The 2 D bispectral matrix and 1 D wavelet band energy are extracted as the dual-channel features of the two-stream convolutional neural network,respectively,supervised model training of vibration signals collected from circuit breaker simulation experiments for five operating conditions.The results show that the bispectral analysis can suppress Gaussian noise,retain the main peak morphological features of the operating mechanism under different operating conditions and fuse wavelet band energy features,and the proposed model can achieve a high recognition accuracy of 98.33%in 5 training iterations to achieve fault diagnosis and identification of the circuit breaker operating mechanism.

关 键 词：振动信号 双谱分析 双流卷积神经网络 故障诊断 断路器 

分 类 号：TM56[电气工程—电器]