结构优化深度网络的高压断路器机械故障诊断

Structural Optimization Deep Network for Mechanical Fault Diagnosis of High Voltage Circuit Breakers

姜楠 罗林 王乔 侯维

Nan Jiang Lin Luo Qiao Wang Wei Hou

辽宁石油化工大学 信息与控制工程学院，辽宁 抚顺 113001 中国石油抚顺石化公司 石油三厂，辽宁 抚顺 113001

School of Information and Control Engineering，Liaoning Petrochemical University，Fushun Liaoning 113001，China No. 3 Refinery of Fushun Petrochemical Company，PetroChina，Fushun Liaoning 113001，China

高压断路器操作过程中的振动信号反映断路器的机械状态。针对基于浅层的振动信号分析模型的特征提取及故障诊断精度等方面存在的不足,提出了一种基于遗传算法优化的卷积神经网络高压断路器故障诊断方法。利用遗传算法的全局寻优能力,通过遗传算法的选择、交叉和变异等操作获得最优初始网络结构参数及全连接层神经元数等，进而优化卷积神经网络,并将优化后的卷积神经网络应用于高压断路器的故障诊断。结果表明,所提方法的诊断性能优于未进行优化的卷积神经网络、动态支持向量机和多层感知机。

The vibration signal during the operation of high voltage circuit breaker can reflect the mechanical state of circuit breaker. Aiming at the shortcomings of feature extraction and fault diagnosis accuracy of shallow vibration signal analysis model, a fault diagnosis method of high voltage circuit breaker based on convolutional neural network optimized by genetic algorithm was proposed. Using the global optimization ability of genetic algorithm, the optimal initial network structure parameters and the number of neurons in the whole connection layer were obtained through the selection, crossover and mutation of genetic algorithm to optimize the convolutional neural network, and the optimized convolutional neural network is applied to the fault diagnosis of high voltage circuit breaker. The results show that the diagnosis performance of the proposed network model is better than that of convolution neural network, dynamic support vector machine and multilayer perceptron.

国家自然科学基金青年科学基金项目(61703191);辽宁省教育厅面上项目(LJKZ0423)

10.12422/j.issn.1672-6952.2023.03.015