基于CEEMDAN⁃LMD⁃KFCM的断路器操动机构机械故障诊断方法

Mechanical Fault Diagnosis Method for Circuit Breaker Operating Mechanism Based on CEEMDAN⁃LMD⁃KFCM

断路器操动机构中高频振动信号蕴含丰富的机械信息,为有效提取振动中高频分量进行诊断分析,提出了一种新的断路器操动机构机械故障诊断方法。此方法对采集到的振动信号时域波形通过CEEMDAN分解,并根据熵权法对其高频分量进行重构以得到去噪信号,将去噪后的信号进行LMD分解,对分解得到的PF分量求取多尺度排列熵作为KFCM识别算法的输入特征分量。经KFCM识别结果表明,此机械故障诊断方法对断路器操动机构转轴卡涩、底座松动、拒动等典型故障具有较高的识别率。

The high frequency vibration signal in the operating mechanism of the circuit breaker contains rich me⁃chanical information.In order to effectively extract the high frequency component of the vibration for diagnosis and analysis,a new mechanical fault diagnosis method of the operating mechanism of the circuit breaker is proposed.This method decomposes the collected vibration signal in time domain through CEEMDAN,and reconstructs its high frequency components according to entropy weight method to get the denoised signal,decomposes the denoised sig⁃nal with LMD,and calculates the multiscale permutation entropy of the decomposed pf component as the input char⁃acteristic component of KFCM recognition algorithm.The results of KFCM identification show that this method has a high identification rate for typical faults such as shaft jam,base looseness,and misoperation.