基于IPSO算法优化小波神经网络的转辙机故障诊断

Fault diagnosis of switch machine based on wavelet neural network optimized by IPSO algorithm

转辙机是铁路上实现道岔转换的重要的设备,其运营、维护工作耗费时间长、故障识别精度不高且存在误判、漏判等问题。针对上述问题,本文基于人工智能、深度学习等新型技术,提出一种新的S700K型转辙机故障识别方法。相较于较传统的Harr或Mexicanhat小波分解,本文首先将微机监测系统采样的转辙机动作功率曲线数据用一种具有紧支撑的正交小波Daubechies波分解与重构,提取8种常见类型故障的特征向量,归一化后作为改进后小波神经网络的输入量;然后采用分类学习粒子群算法优化网络内部的各项权值、阈值等参数,构建IPSO-WNN故障识别模型;最后选取车站监测机数据库中的动作功率曲线对故障识别模型进行网络训练和测试。本文提出的算法对8种常见的转辙机故障识别准确率超过95%,用时仅21 s左右,可以有效地运用于S700K型转辙机的故障识别并提高其精度与速度,为实现转辙机故障识别的预测提供理论支撑。

Switch machine is an important equipment to realize turnout conversion on the railway.Its operation and maintenance takes a long time,its fault identification accuracy is not high,and there are many problems such as misjudgment,omission and soon.To solve the above problems,this paper proposes a new fault recognition method for S700K switch machine based on artificial intelligence,deep learning and other new technologies.Compared with the traditional Harr or Mexicanhat wavelet decomposition,in this paper,the power curve data sampled by the microcomputer monitoring system is decomposed and composed by an orthogonal wavelet Daubechies wave with tight support,and the feature vectors of eight common types of faults are extracted,which are normalized as the input of the improved wavelet neural network.Then,the IPSO-WNN fault recognition model is constructed by using the classification learning particle swarm optimization algorithm to optimize the weights and thresholds in the network.Finally,the action power curve in the station monitor data base is selected for network training and testing of the fault identification model.The algorithm proposes in this paper has a fault identification accuracy of more than 95%and takes only about 21 seconds on the 8 common fault of switch machine.It can be effectively applied to the fault identification of S700K type switch machine and improve its accuracy and speed,providing theoretical support for the prediction of fault identification of switch machine.