Intelligent Fault Diagnosis Research for Permanent Magnet Linear Synchronous Motor

被引：2

作者：

Wang Fuzhong ^{[1
,2
]}

Yuan Shiying ^{[2
]}

机构：

[1] China Univ Mining & Technol, Sch Mech Elect & Informat Engineer, Beijing 100083, Peoples R China

[2] Henan Polytech Univ, Sch Elect Engn & Automat, Henan Sheng 454003, Peoples R China

来源：

2008 7TH WORLD CONGRESS ON INTELLIGENT CONTROL AND AUTOMATION, VOLS 1-23 | 2008年

关键词：

permanent magnet linear synchronous motor; fault diagnosis; fuzzy wavelet neural network; a hybrid learning algorithm;

D O I：

10.1109/WCICA.2008.4593223

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

On basis of fault characteristics analysis of the permanent magnet linear synchronous motor (PMLSM), a fuzzy wavelet neural network model was established to achieve the PMLSM intelligent fault diagnosis, which used wavelet function as a fuzzy membership function and integrated fuzzy logic with BP neural network. Meanwhile a mixed learning algorithm based on self-organizing and instructors-guide-learning was proposed to train translation factor, flexing factor of wavelet function, and fuzzy neural network weights to make network parameters and structure achieve optimal approximation. The test results show that the method can realize fault diagnosis effectively, improve the efficiency and accuracy of diagnosis, and provide an effective way for the protection of PMLSM safe operation.

引用

页码：1951 / +

页数：2