Stator inter-turn fault detection of an induction motor using neuro-fuzzy techniques

Motivated by the superior performances of neural networks and neuro-fuzzy approaches to fault detection of a single phase induction motor, this paper studies the applicability these two approaches for detection of stator inter-turn faults in a three phase induction motor. Firstly, the paper develops an adaptive neural fuzzy inference system (ANFIS) detection strategy and then compares its performance with that of using a multi layer perceptron neural network (MLP NN) applied to stator inter-turn fault detection of a three phase induction motor. The fault location process is based on the monitoring the three phase shifts between the line current and the phase voltage of the induction machine.

[1]  Fiorenzo Filippetti,et al.  Recent developments of induction motor drives fault diagnosis using AI techniques , 2000, IEEE Trans. Ind. Electron..

[2]  Khaled Jelassi,et al.  An Effective Neural Approach for the Automatic Location of Stator Interturn Faults in Induction Motor , 2008, IEEE Transactions on Industrial Electronics.

[3]  F. Filippetti,et al.  AI techniques in induction machines diagnosis including the speed ripple effect , 1996 .

[4]  Richard J. Povinelli,et al.  Induction Machine Broken Bar and Stator Short-Circuit Fault Diagnostics Based on Three-Phase Stator Current Envelopes , 2008, IEEE Transactions on Industrial Electronics.

[5]  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.

[6]  Muslum Arkan,et al.  Modelling and simulation of induction motors with inter-turn faults for diagnostics , 2005 .

[7]  Gary G. Yen,et al.  Wavelet packet feature extraction for vibration monitoring , 2000, IEEE Trans. Ind. Electron..

[8]  Christine W. Chan,et al.  Artificial intelligence for monitoring and supervisory control of process systems , 2007, Eng. Appl. Artif. Intell..

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

[10]  W ChanChristine,et al.  Artificial intelligence for monitoring and supervisory control of process systems , 2007 .

[11]  Slim Tnani,et al.  Diagnosis by parameter estimation of stator and rotor faults occurring in induction machines , 2006, IEEE Transactions on Industrial Electronics.

[12]  M. M. Morcos,et al.  Application of AI tools in fault diagnosis of electrical machines and drives-an overview , 2003 .

[13]  M. Riera-Guasp,et al.  The Use of the Wavelet Approximation Signal as a Tool for the Diagnosis of Rotor Bar Failures , 2005, IEEE Transactions on Industry Applications.

[14]  W. T. Thomson,et al.  Current signature analysis to detect induction motor faults , 2001 .

[15]  Tommy W. S. Chow,et al.  Induction machine fault diagnostic analysis with wavelet technique , 2004, IEEE Transactions on Industrial Electronics.

[16]  Gary S. May,et al.  Neural-network-based sensor fusion of optical emission and mass spectroscopy data for real-time fault detection in reactive ion etching , 2005, IEEE Transactions on Industrial Electronics.

[17]  Hamid A. Toliyat,et al.  Novel frequency domain based technique to detect incipient stator inter-turn faults in induction machines , 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).

[18]  M. Riera-Guasp,et al.  The use of the wavelet approximation signal as a tool for the diagnosis of rotor bar failures , 2005, 2005 5th IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics and Drives.

[19]  F. Filippetti,et al.  AI techniques in induction machines diagnosis including the speed ripple effect , 1996, IAS '96. Conference Record of the 1996 IEEE Industry Applications Conference Thirty-First IAS Annual Meeting.

[20]  Jim Penman,et al.  Induction machine condition monitoring with higher order spectra , 2000, IEEE Trans. Ind. Electron..

[21]  Mohamed Benbouzid,et al.  Induction motors' faults detection and localization using stator current advanced signal processing techniques , 1999 .

[22]  H. Joel Trussell,et al.  Fuzzy inference systems implemented on neural architectures for motor fault detection and diagnosis , 1999, IEEE Trans. Ind. Electron..