## Journal of Central South University

 第50卷    第11期    总第303期    2019年11月

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(1. 兰州交通大学 自动化与电气工程学院，甘肃 兰州，730070；
2. 兰州交通大学 光电技术与智能控制教育部重点实验室，甘肃 兰州，730070
)

Fault diagnosis of S700K switch machine based on EEMD multiscale sample entropy

1. School of Automation & Electrical Engineering, Lanzhou Jiaotong University, Lanzhou 730070；
2. Key Laboratory of Opto-technology and Intelligent Control of Ministry of Education,
Lanzhou Jiaotong University, Lanzhou 730070, China

Abstract:Aiming at the fault diagnosis of S700K switch machine in operation, a signal analysis and fault diagnosis method based on ensemble empirical mode decomposition (EEMD) and multiscale sample entropy was proposed. Firstly, the power curve of S700K switch machine was decomposed by EEMD, and the intrinsic mode function (IMF) of different time scales were obtained. The sample entropy of each IMF component was extracted. Because the sample entropy can effectively distinguish the complexity of different signals, the characteristic parameters of different states of the switch machine were obtained. Finally, the characteristic pattern matrix was constructed by using the characteristic parameters of the switch machine in different operation states, and the fuzzy equivalent matrix of the matrix was obtained by using the fuzzy clustering analysis algorithm. In the fuzzy equivalent matrix, when λ (variable threshold) changes within [0,1], the fuzzy equivalent matrix was transformed into the equivalent Boolean matrix. The dynamic clustering graph was obtained from the Boolean matrix and the classification result was obtained to realize fault diagnosis . The results show that the algorithm can accurately extract fault features and support multiple fault detection simultaneously. The accuracy and efficiency of fault diagnosis of S700K switch machine is effectively improved.

Key words: EEMD; multiscale sample entropy; intrinsic mode function (IMF); fuzzy clustering; equivalent matrix

 中南大学学报（自然科学版） ISSN 1672-7207 CN 43-1426/NZDXZAC 中南大学学报（英文版） ISSN 2095-2899 CN 43-1516/TBJCSTFT