采用时频矩阵奇异值分解的配电开关振动信号特征量提取方法

A Vibration Signal Feature Extraction Method for Distribution Switches Based on Singular Value Decomposition of Time-frequency Matrix

配电开关振动信号具有非线性非平稳特性,蕴含有机械状态信息。提出一种采用时频矩阵奇异值分解的配电开关振动信号特征量提取方法,对振动信号做希尔伯特-黄变换以进行带通滤波,构造其时频矩阵,对该矩阵进行奇异值分解,可将振动信号的特征信息分解到不同的时频子空间,以得到的时频矩阵奇异值作为振动信号的特征量,用于表征配电开关的机械状态。对配电开关在正常及卸掉A相触头绝缘拉杆、机械结构卡涩、底座螺丝松动等3种典型故障情况下实测振动信号的时频矩阵奇异值做模糊c均值聚类,结果表明该特征量能够准确、有效地表征配电开关的机械状态。

Vibration signals of distribution switches that contain mechanical information are characterized by nonlinearity and nonstationarity. Therefore, based on the singular value decomposition of time-frequency matrix, a vibration signal feature extraction method for distribution switches was proposed. Hilbert-Huang transform （HHT） band-pass filter was used to construct the time-frequency matrix for the vibration signal. After the singular value decomposition （SVD） of the time-frequency matrix, the characteristic information of the vibration signal would be decomposed into different time-frequency subspaces ,so we can get time-frequency matrix singular values as the feature quantities, the feature quantities would represent the mechanical state of distribution switches. Fuzzy c-mean （FCM） clustering was applied to these time-frequency matrix singular values of vibration signals which was monitored in normal states and three kinds of typical malfunctions including relieved insulated pull rod of phase A contacts, mechanical structure clamping stagnation states and screw loosing states. The result shows that the feature quantity can represent the mechanical state of distribution switches accurately and effectively.