Fault diagnosis of induction motors broken rotor bars by pattern recognition based on noise cancelation

Current signal monitoring (CSM) can be used as an effective tool for diagnosing broken rotor bars fault in induction motors. In this paper, fault diagnosis and classification based on artificial neural networks (ANNs) is done in two stages. In the first stage, a filter is designed to remove irrelevant fault components (such as noise) of current signals. The coefficients of the filter are obtained by least square (LS) algorithm. Then by extracting suitable time domain features from filter's output, a neural network is trained for fault classification. The output vector of this network is represented in one of four categories that includes healthy mode, a 5 mm crack on a bar, one broken bar, and two broken bar modes. An optimum structure of the neural network is obtained via particle swarm optimization (PSO) algorithm.

[1]  Jawad Faiz,et al.  TIME STEPPING FINITE ELEMENT ANALYSIS OF BROKEN BARS FAULT IN A THREE-PHASE SQUIRREL-CAGE INDUCTION MOTOR , 2007 .

[2]  M. Haji,et al.  Pattern Recognition-A Technique for Induction Machines Rotor Broken Bar Detection , 2001, IEEE Power Engineering Review.

[3]  Bo-Suk Yang,et al.  Intelligent fault diagnosis system of induction motor based on transient current signal , 2009 .

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

[5]  Report of Large Motor Reliability Survey of Industrial and Commercial Installations, Part I , 1985, IEEE Transactions on Industry Applications.

[6]  H.A. Toliyat,et al.  A novel approach for broken rotor bar detection in cage induction motors , 1998, Conference Record of 1998 IEEE Industry Applications Conference. Thirty-Third IAS Annual Meeting (Cat. No.98CH36242).

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

[8]  Yaguo Lei,et al.  Application of an intelligent classification method to mechanical fault diagnosis , 2009, Expert Syst. Appl..

[9]  Chris K. Mechefske,et al.  Induction Motor Fault Detection and Diagnosis Using Artifical Neural Networks , 2005 .

[10]  Kolla,et al.  Identifying three-phase induction motor faults using artificial neural networks , 2000, ISA transactions.

[11]  Bo-Suk Yang,et al.  Combination of independent component analysis and support vector machines for intelligent faults diagnosis of induction motors , 2007, Expert Syst. Appl..

[12]  Hamid Reza Karimi,et al.  Vibration analysis for bearing fault detection and classification using an intelligent filter , 2014 .

[13]  N. Tandon,et al.  A review of vibration and acoustic measurement methods for the detection of defects in rolling element bearings , 1999 .

[14]  Mo-Yuen Chow,et al.  Methodology for on-line incipient fault detection in single-phase squirrel-cage induction motors using artificial neural networks , 1991 .

[15]  Stavros J. Perantonis,et al.  Levenberg-Marquardt algorithm with adaptive momentum for the efficient training of feedforward networks , 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium.

[16]  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).

[17]  Hamid A. Toliyat,et al.  Study of three phase induction motors with incipient rotor cage faults under different supply conditions , 1999, Conference Record of the 1999 IEEE Industry Applications Conference. Thirty-Forth IAS Annual Meeting (Cat. No.99CH36370).

[18]  Wenying Huang,et al.  A novel detection method of motor broken rotor bars based on wavelet ridge , 2003 .

[19]  Chanan Singh,et al.  Report of Large Motor Reliability Survey of Industrial and Commercial Installations, Part II , 1985, IEEE Transactions on Industry Applications.

[20]  Jafar Zarei,et al.  Induction motors bearing fault detection using pattern recognition techniques , 2012, Expert Syst. Appl..

[21]  J. Dron,et al.  Improvement of the sensitivity of the scalar indicators (crest factor, kurtosis) using a de-noising method by spectral subtraction: application to the detection of defects in ball bearings , 2004 .

[22]  D. Sutanto,et al.  High-impedance fault detection using discrete wavelet transform and frequency range and RMS conversion , 2005, IEEE Transactions on Power Delivery.

[23]  A. J. Marques Cardoso,et al.  Rotor Cage Fault Diagnosis in Three-Phase Induction Motors by Extended Park's Vector Approach , 2000 .