Detection for Stator Winding Turn Fault by Using Fuzzy Neural Network

Xu Hong Wang; Yi Gang He

doi:10.4028/www.scientific.net/AMM.48-49.415

Paper Titles

Design of Strength of the Seats and Head Restraints Testing System
p.397

Design of Vertical Air Conditioning Control System
p.401

Design of Visibility Forecasting System on Freeways Based on the Internet of Things
p.406

Design Optimization of a Hydraulic Caterpillar Vehicle Frame
p.410

Detection for Stator Winding Turn Fault by Using Fuzzy Neural Network
p.415

Development and Implementation of a Bootloader Based on the Embedded Systems
p.419

Development and Optimization of a mAb-Based Indirect Competitive ELISA for Enrofloxacin Residue in Duck
p.423

Development of PZT Multi-Channel Power Supply Based on PA93
p.428

Discussion on the Pattern of Competitive Trade in the AGC Ancillary Service Market
p.434

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 48-49Detection for Stator Winding Turn Fault by Using...

Detection for Stator Winding Turn Fault by Using Fuzzy Neural Network

Abstract:

A fuzzy neural network based on-line turn fault detection approach for induction motors is proposed in this paper. B-spline membership fuzzy neural network is employed to detect turn fault and a genetic algorithm with an efficient search strategy is developed to optimize network parameters. Based on it, Experiments are carried out on a special rewound laboratory induction motor, the results show fuzzy neural network based diagnosis model determines the shorted turns exactly, and is more effective than the parameters estimation method under the condition of detecting a slowly developing turn fault.

You might also be interested in these eBooks

Measuring Technology and Mechatronics Automation

View Preview

Info:

Periodical:

Applied Mechanics and Materials (Volumes 48-49)

Pages:

415-418

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.48-49.415

Citation:

Cite this paper

Online since:

February 2011

Authors:

Xu Hong Wang, Yi Gang He

Keywords:

Fuzzy Neural Network (FNN), Genetic Algorithm (GA), Stator Winding, Turn Fault Detection

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] Bonnett AH, Soukup GC, in: Cause and analysis of stator and rotor failures in three-phase sqirrei-cage induction motors [J]. IEEE Transactions on Industry Applications, 1992, 28( 2): 921-937.

DOI: 10.1109/28.148460

Google Scholar

[2] Kliman GB, Premelani WJ, Koeg RA, in: A new approach to on-line turn fault detection in AC motors, IEEE-IAS Annual meeting, New York, 687-693, (1996).

DOI: 10.1109/ias.1996.557113

Google Scholar

[3] Yun, Jangho, Lee, Kwanghwan, Lee, Kwang-Woon etc, in: Detection and classification of stator turn faults and high-resistance electrical connections for induction machines[J]. IEEE Transactions on Industry Applications, 2009, 45( 2): 666-675.

DOI: 10.1109/tia.2009.2013557

Google Scholar

[4] P. Vas, in: Parameter estimation, condition monitoring, and diagnosis of electrical machines [M]. Oxford: Clarendon press, (2005).

Google Scholar

[5] K.F.C. Yiu, S. Wang, K.L. Teo etc, in: Nonlinear System Modeling via Knot-Optimizing B-Spline Networks [J]. IEEE Trans. Neural Networks, 2001, 12(5): 1013-1022.

DOI: 10.1109/72.950131

Google Scholar