Incipient bearing fault detection for three-phase Brushless DC motor drive using ANFIS

Incipient fault detection of electrical machine is a major task and requires intelligent diagnostic approach. Extensive research has been performed in the field of automation of fault diagnostic schemes. Among several causes of electrical machine failure the most frequent occurring fault is the mechanical bearing failure. Thus, this paper presents diagnostic technique for incipient bearing failure in a three-phase Brushless DC (BLDC) motor drive system. The Adaptive Neuro-Fuzzy Inference System is utilized for the diagnostic purpose. The proposed approach offers accurate estimate of the bearing conditions with minimal effort. The proposed technique is verified using simulation approach. The simulation is done using Matlab/Simulink and the complete model is presented in the paper.

[1]  Mohamed Benbouzid,et al.  A review of induction motors signature analysis as a medium for faults detection , 1998, IECON '98. Proceedings of the 24th Annual Conference of the IEEE Industrial Electronics Society (Cat. No.98CH36200).

[2]  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.

[3]  Emine Ayaz,et al.  Adaptive neuro-fuzzy inference system for bearing fault detection in induction motors using temperature, current, vibration data , 2009, IEEE EUROCON 2009.

[4]  J. Rosero,et al.  Fault detection of eccentricity and bearing damage in a PMSM by means of wavelet transforms decomposition of the stator current , 2008, 2008 Twenty-Third Annual IEEE Applied Power Electronics Conference and Exposition.

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

[6]  Gérard-André Capolino,et al.  Advances in Diagnostic Techniques for Induction Machines , 2008, IEEE Transactions on Industrial Electronics.

[7]  Mo-Yuen Chow.  Methodologies of Using Neural Network and Fuzzy Logic Technologies for Motor Incipient Fault Detection , 1998 .

[8]  Jyh-Shing Roger Jang,et al.  ANFIS: adaptive-network-based fuzzy inference system , 1993, IEEE Trans. Syst. Man Cybern..

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

[10]  Hamid A. Toliyat,et al.  Condition monitoring and fault diagnosis of electrical machines-a review , 1999, Conference Record of the 1999 IEEE Industry Applications Conference. Thirty-Forth IAS Annual Meeting (Cat. No.99CH36370).

[11]  T.G. Habetler,et al.  Dynamic Eccentricity and Demagnetized Rotor Magnet Detection in Trapezoidal Flux (Brushless DC) Motors Operating Under Different Load Conditions , 2007, IEEE Transactions on Power Electronics.

[12]  James L. Kirtley,et al.  Fault detection and diagnostics for non-intrusive monitoring using motor harmonics , 2010, 2010 Twenty-Fifth Annual IEEE Applied Power Electronics Conference and Exposition (APEC).

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

[14]  M.M. Morcos,et al.  Detection of insulation failure in BLDC motors using neuro-fuzzy systems , 2005, CEIDP '05. 2005 Annual Report Conference on Electrical Insulation and Dielectric Phenomena, 2005..

[15]  L. Romeral,et al.  Fault Detection in dynamic conditions by means of Discrete Wavelet Decomposition for PMSM running under Bearing Damage , 2009, 2009 Twenty-Fourth Annual IEEE Applied Power Electronics Conference and Exposition.

[16]  H. R. Martin.  Detection of Bearing Failure in Machines , 1993 .

[17]  Alireza Sadeghian,et al.  Mechanical fault diagnostics for induction motor with variable speed drives using Adaptive Neuro-fuzzy Inference System , 2006 .

[18]  Peter Vas,et al.  Artificial-Intelligence-Based Electrical Machines and Drives: Application of Fuzzy, Neural, Fuzzy-neural, and Genetic-Algorithm-based Techniques , 1999 .

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

[20]  K. Salahshoor,et al.  Complex dynamical system fault diagnosis based on multiple ANFIS using independent component , 2008, 2008 16th Mediterranean Conference on Control and Automation.

[21]  Mo-Yuen Chow,et al.  Using a neural/fuzzy system to extract heuristic knowledge of incipient faults in induction motors. Part I-Methodology , 1995, IEEE Trans. Ind. Electron..