Induction motor stator fault detection via fuzzy logic

The online monitoring of induction motors is becoming increasingly important. The main difficulty in this task is the lack of an accurate analytical model to describe a faulty motor. A fuzzy logic approach may help to diagnose induction motor faults. This work presents a reliable method for the detection of stator winding faults (which make up 38% of induction motor failures) based on monitoring the line/terminal current amplitudes. In this method, fuzzy logic is used to make decisions about the stator motor condition. In fact, fuzzy logic is reminiscent of human thinking processes and natural language enabling decisions to be made based on vague information. Therefore, this paper applies fuzzy logic to induction motors fault detection and diagnosis. The motor condition is described using linguistic variables. Fuzzy subsets and the corresponding membership functions describe stator current amplitudes. A knowledge base, comprising rule and data bases, is built to support the fuzzy inference. The induction motor condition is diagnosed using a compositional rule of fuzzy inference. Experimental results are presented in terms of accuracy in the detection motor faults and knowledge extraction feasibility. The preliminary results show that the proposed fuzzy approach can be used for accurate stator fault diagnosis.

[1]  A. Cardoso,et al.  Diagnosis of stator inter-turn short circuits in DTC induction motor drives , 2004, IEEE Transactions on Industry Applications.

[2]  B. Singh,et al.  A review of stator fault monitoring techniques of induction motors , 2005, IEEE Transactions on Energy Conversion.

[3]  Antero Arkkio,et al.  Detection of stator winding fault in induction motor using fuzzy logic , 2008, Appl. Soft Comput..

[4]  Lotfi A. Zadeh,et al.  Fuzzy Sets , 1996, Inf. Control..

[5]  Scott D. Sudhoff,et al.  Analysis of Electric Machinery and Drive Systems , 1995 .

[6]  Thomas G. Habetler,et al.  Transient model for induction machines with stator winding turn faults , 2000, Conference Record of the 2000 IEEE Industry Applications Conference. Thirty-Fifth IAS Annual Meeting and World Conference on Industrial Applications of Electrical Energy (Cat. No.00CH37129).

[7]  Gojko Joksimovic,et al.  The detection of inter-turn short circuits in the stator windings of operating motors , 2000, IEEE Trans. Ind. Electron..

[8]  Makarand Sudhakar Ballal,et al.  Adaptive Neural Fuzzy Inference System for the Detection of Inter-Turn Insulation and Bearing Wear Faults in Induction Motor , 2007, IEEE Transactions on Industrial Electronics.

[9]  J. Penman,et al.  Detection and location of interturn short circuits in the stator windings of operating motors , 1994 .

[10]  Rolf Isermann,et al.  Model-based fault-detection and diagnosis - status and applications , 2004, Annu. Rev. Control..

[11]  Ricardo H. C. Takahashi,et al.  Incipient fault detection in induction machine stator-winding using a fuzzy-Bayesian two change points detection approach , 2010, 2010 9th IEEE/IAS International Conference on Industry Applications - INDUSCON 2010.

[12]  T. G. Habetler,et al.  Neural network based on-line stator winding turn fault detection for induction motors , 2000, Conference Record of the 2000 IEEE Industry Applications Conference. Thirty-Fifth IAS Annual Meeting and World Conference on Industrial Applications of Electrical Energy (Cat. No.00CH37129).

[13]  H.A. Toliyat,et al.  Condition Monitoring and Fault Diagnosis of Electrical Motors—A Review , 2005, IEEE Transactions on Energy Conversion.

[14]  باقری,et al.  Stator Fault Detection in Induction Machines by Parameter Estimation Using Adaptive Kalman Filter , 2007 .

[15]  P. Cochat,et al.  Et al , 2008, Archives de pediatrie : organe officiel de la Societe francaise de pediatrie.