Liu Qinzhe, Wang Xiaolong, Guo Zhaojing, Li Jian, Xu Wei, Dai Xiaowen, Liu Chenlei, Zhao Tong
School of Electrical Engineering, Shangdong University, Jinan 250100, China.
Taikai Automation Co., Ltd., Taian 271000, China.
Sensors (Basel). 2023 Dec 26;24(1):124. doi: 10.3390/s24010124.
In response to the lack of generality in feature extraction using modal decomposition methods and the susceptibility of diagnostic performance to parameter selection in traditional mechanical fault diagnosis of high-voltage circuit breaker operating mechanisms, this paper proposes a Global-Local feature extraction method based on Generalized S-Transform (S-Translate) combined with Gray Level Co-Occurrence Matrix (GLCM) and complemented by Maximum Relevance and Minimum Redundancy (mRMR) feature selection. The GL (Global-Local)-mRMR-KELM fault diagnosis model is proposed, which employs the Kernel Extreme Learning Machine (KELM). In this model, the original time-frequency domain features and the time-frequency features of the Generalized S-Transform matrix of vibration signals under different states of the circuit breaker are first extracted as global features. Then, the GLCM is obtained to extract texture features as local features. Finally, the mRMR and KELM are comprehensively applied to perform feature selection and classification on the dataset, thereby accomplishing the fault diagnosis of the circuit breaker's operating mechanism. In this study, the 72.5 kV SF circuit breaker operating mechanism is taken as the research object, and three types of mechanical faults are simulated to obtain a vibration signal. Experimental results verify the effectiveness of the proposed GL-mRMR-KELM model, achieving a diagnostic accuracy of 96%. This research provides a feasible approach for the fault diagnosis of circuit breaker operating mechanisms.
针对高压断路器操动机构传统机械故障诊断中模态分解方法特征提取缺乏通用性以及诊断性能易受参数选择影响的问题，本文提出一种基于广义S变换（S-Translate）结合灰度共生矩阵（GLCM）并辅以最大相关最小冗余（mRMR）特征选择的全局-局部特征提取方法。提出了GL（全局-局部）-mRMR-KELM故障诊断模型，该模型采用核极限学习机（KELM）。在该模型中，首先提取断路器不同状态下振动信号的原始时频域特征和广义S变换矩阵的时频特征作为全局特征。然后，通过获取GLCM提取纹理特征作为局部特征。最后，综合应用mRMR和KELM对数据集进行特征选择和分类，从而完成断路器操动机构的故障诊断。本研究以72.5kV SF断路器操动机构为研究对象，模拟三种机械故障获取振动信号。实验结果验证了所提GL-mRMR-KELM模型的有效性，诊断准确率达到96%。该研究为断路器操动机构的故障诊断提供了一种可行的方法。
