基于改进VMD与特征选择的路灯故障检测方法

Streetlight fault detection method based on improved VMD and feature selection

路灯正常运行对于城市照明具有重要意义。当前路灯故障检测局限于初步的故障现象,无法辨识具体的故障类别。为实现路灯具体故障类别的检测,本文以路灯监控和数据采集系统的路灯运行数据为对象,提出了一种基于改进VMD与特征选择的路灯故障检测模型。首先,利用主成分分析法筛选路灯运行数据的主要变量参数,并用变分模态分解对筛选参数进行分解。同时,引入鲸鱼优化算法改进变分模态分解的自适应性。在特征选择方面,通过Pearson系数选择相关IMF分量结合样本熵构建故障特征向量。最后,结合广西崇左市2019~2022年路灯故障数据,建立基于XGBoost的故障诊断模型,从而辨别路灯的正常、电源故障、线路故障、保险故障4种状态。实验结果表明,该方法能有效实现路灯具体故障类别的诊断,故障辨识率为93.75%,为路灯故障检测研究提供了新途径。

As the core equipment of the urban lighting system,the regular operation of streetlights is of great significance to urban lighting.Currently,streetlight fault detection is limited to preliminary fault phenomenon.To detect the streetlight's specific fault category,this paper takes the streetlight operation data of the streetlight monitoring and data acquisition system as the object.A streetlight fault detection model based on improved VMD and feature selection is proposed combining the traditional method,the data-driven method and the signal processing method.First,principal component analysis filters the main variable parameters of streetlight operation data,and variational mode decomposition is used to decompose the screened parameters.Similarly,the whale optimization algorithm is introduced to improve the adaptability of the variational mode decomposition.Secondly,the Pearson coefficient selects the relevant IMF components,and the sample entropy is used to construct the fault feature vector.Through the experimental verification of the streetlight fault statistical data of the urban lighting monitoring system in Chongzuo City,Guangxi,the results show that the proposed fault diagnosis method can effectively extract the fault feature information of different fault states of streetlights.The correct rate of fault diagnosis is 93.75%,which provides a new way for streetlight fault diagnosis.