基于参数自适应VMD和SA-ELM的有载分接开关故障诊断

机械振动信号能反映有载分接开关的运行状态。为提高有载分接开关机械故障的诊断准确率,提出了一种基于参数自适应变分模态分解(VMD)和模拟退火优化极限学习机(SA-ELM)的故障诊断方法。首先对振动信号进行VMD分解,根据能量准则自适应确定模态数的取值,得到一组窄带、区分度较好的模态分量。然后求取各模态的能量值,形成特征向量组,不同故障状态的模态特征区分明显。最后将特征向量组输入SA-ELM,实现振动信号的识别和诊断。在模拟试验平台上进行试验并对采集的信号进行分析,结果表明文中故障诊断方法可有效提高有载分接开关机械故障的诊断准确率。

Fault diagnosis of on-load tap-changer based on the parameter-adaptive VMD and SA-ELM

In order to realize effective feature extraction and fault diagnosis for non-stationary vibration signals of on-load tap-changer, a fault diagnosis method based on the parameter-adapted variational mode decomposition (VMD) and modal energy feature is proposed. Firstly, the signal is decomposed by VMD method, and the number of modals is selected based on energy criterion. A group of modal components with narrow band and great discrimination is obtained. Then the energy features of each modal component are calculated, which form the feature vector group. Finally, the feature vector group is input to the extreme learning machine (ELM) optimized by simulated annealing algorithm to realize the recognition and fault diagnosis of the vibration signals. An experiment is carried out on the simulation experiment platform and the collected signals are processed. The results show that the proposed fault diagnosis method has higher identification accuracy.