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Book Chapter
Diagnosis of Inverter Faults in PMSM DTC Drive Using Time-Series Data Mining Technique
Book Series
Lecture Notes in Computer Science
Publisher
Springer Berlin / Heidelberg
ISSN
0302-9743 (Print) 1611-3349 (Online)
Volume
Volume 4093/2006
Book
Advanced Data Mining and Applications
DOI
10.1007/11811305
Copyright
2006
ISBN
978-3-540-37025-3
Category
Sequential Data Mining and Time Series Mining
DOI
10.1007/11811305_81
Pages
741-748
Subject Collection
Computer Science
SpringerLink Date
Thursday, July 27, 2006
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Sequential Data Mining and Time Series Mining
Diagnosis of Inverter Faults in PMSM DTC Drive Using Time-Series Data Mining Technique
Dan Sun
1
, Jun Meng
1
and Zongyuan He
1
(1)
College of Electrical Engineering, Zhejiang University, Hangzhou, Zhejiang, 310027, China
Abstract
This paper investigates a Time-Series Data Mining (TSDM) Technique based fault diagnostic method for short-switch and open-phase faults in a standard 6-switch inverter fed permanent magnet synchronous motor (PMSM) direct torque control (DTC) drive system. For diagnosing the operating condition of an inverter, the reconstructed phase space (RPS) theory is applied to obtain the special feature consisting in the trajectories of phase currents for healthy and faulty operating conditions. The fuzzy C-mean (FCM) algorithm is used to build a fuzzy membership function, an FCM based ANFIS (FCM-ANFIS) is designed to classify different fault patterns. The proposed method has been studied by simulation using MATLAB; which proves that different operating conditions of PMSM DTC drive can be discovered clearly without background knowledge.
Dan
Sun
Email:
sundan@zju.edu.cn
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