Detection and Diagnosis of Faults in Induction Motor Using an Improved Artificial Ant Clustering Technique

The presence of electrical and mechanical faults in the induction motors (IMs) can be detected by analysis of the stator current spectrum. However, when an IM is fed by a frequency converter, the spectral analysis of stator current signal becomes difficult. For this reason, the monitoring must depend on multiple signatures in order to reduce the effect of harmonic disturbance on the motor-phase current. The aim of this paper is the description of a new approach for fault detection and diagnosis of IMs using signal-based method. It is based on signal processing and an unsupervised classification technique called the artificial ant clustering. The proposed approach is tested on a squirrel-cage IM of 5.5 kW in order to detect broken rotor bars and bearing failure at different load levels. The experimental results prove the efficiency of our approach compared with supervised classification methods in condition monitoring of electrical machines.

[1]  Osman Bilgin,et al.  Rotor Bar Fault Diagnosis by Using Power Factor , .

[2]  H. Douglas,et al.  The detection of interturn stator faults in doubly-fed induction generators , 2005, Fourtieth IAS Annual Meeting. Conference Record of the 2005 Industry Applications Conference, 2005..

[3]  P. Garcia,et al.  Broken Rotor Bar Detection in Line-Fed Induction Machines Using Complex Wavelet Analysis of Startup Transients , 2007, 2007 IEEE Industry Applications Annual Meeting.

[4]  Raphaël Romary,et al.  Stator-Interlaminar-Fault Detection Using an External-Flux-Density Sensor , 2010, IEEE Transactions on Industrial Electronics.

[5]  Izzet Yilmaz,et al.  Induction Motor Bearing Failure Detection and Diagnosis: Park and Concordia Transform Approaches Comparative Study , 2008 .

[6]  Guy Clerc,et al.  Feature Selection by Evolutionary Computing: Application on Diagnosis by Pattern Recognition Approach , 2005, CAINE.

[7]  Jose A. Antonino-Daviu,et al.  Diagnosis of Induction Motor Faults in Time-Varying Conditions Using the Polynomial-Phase Transform of the Current , 2011, IEEE Transactions on Industrial Electronics.

[8]  H. Ermert,et al.  Comparison of different Neuro-Fuzzy classification systems for the detection of prostate cancer in ultrasonic images , 1997, 1997 IEEE Ultrasonics Symposium Proceedings. An International Symposium (Cat. No.97CH36118).

[9]  Chi-Ho Tsang,et al.  Multi-agent intrusion detection system in industrial network using ant colony clustering approach and unsupervised feature extraction , 2005, 2005 IEEE International Conference on Industrial Technology.

[10]  Stephen L. Chiu,et al.  Fuzzy Model Identification Based on Cluster Estimation , 1994, J. Intell. Fuzzy Syst..

[11]  Selin Aviyente,et al.  Time–Frequency Analysis for Efficient Fault Diagnosis and Failure Prognosis for Interior Permanent-Magnet AC Motors , 2008, IEEE Transactions on Industrial Electronics.

[12]  O. Ondel,et al.  Detection of induction motor faults by an improved artificial ant clustering , 2011, IECON 2011 - 37th Annual Conference of the IEEE Industrial Electronics Society.

[13]  Janos Gertler,et al.  Fault detection and diagnosis in engineering systems , 1998 .

[14]  O. Ondel,et al.  Use of data standardization to improve inverter - induction machine fault detection , 2006, IECON 2006 - 32nd Annual Conference on IEEE Industrial Electronics.

[15]  G. Grellet,et al.  Asynchronous motor cage fault detection through electromagnetic torque measurement , 2007 .

[16]  Peter Tavner,et al.  Condition Monitoring of Rotating Electrical Machines , 2008 .

[17]  Chee Peng Lim,et al.  Fault Detection and Diagnosis of Induction Motors Using Motor Current Signature Analysis and a Hybrid FMM–CART Model , 2012, IEEE Transactions on Neural Networks and Learning Systems.

[18]  Hongyuan Gao,et al.  Multiuser Detection Using Immune Ant Colony Optimization , 2009, 2009 International Conference on Artificial Intelligence and Computational Intelligence.

[19]  Vicente Climente-Alarcon,et al.  Induction Motor Diagnosis Based on a Transient Current Analytic Wavelet Transform via Frequency B-Splines , 2011, IEEE Transactions on Industrial Electronics.

[20]  E. S. Saraiva,et al.  Computer aided detection of airgap eccentricity in operating three-phase induction motors, by Park's vector approach , 1991, Conference Record of the 1991 IEEE Industry Applications Society Annual Meeting.

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

[22]  C. Tassoni,et al.  Vibrations, currents and stray flux signals to asses induction motors rotor conditions , 2006, IECON 2006 - 32nd Annual Conference on IEEE Industrial Electronics.

[23]  Lanlan Kang,et al.  An Improved Genetic & Ant Colony Optimization Algorithm for Travelling Salesman Problem , 2010, 2010 Third International Symposium on Information Science and Engineering.

[24]  H. Henao,et al.  Diagnosis of Broken Bar Fault in Induction Machines Using Discrete Wavelet Transform without Slip Estimation , 2007, 2007 IEEE Industry Applications Annual Meeting.

[25]  Guillermo R. Bossio,et al.  Separating Broken Rotor Bars and Load Oscillations on IM Fault Diagnosis Through the Instantaneous Active and Reactive Currents , 2009, IEEE Transactions on Industrial Electronics.

[26]  Jawad Faiz,et al.  Mixed-fault diagnosis in induction motors considering varying load and broken bars location , 2010 .

[27]  Ezio Bassi,et al.  Stator Current and Motor Efficiency as Indicators for Different Types of Bearing Faults in Induction Motors , 2010, IEEE Transactions on Industrial Electronics.

[28]  Jose A. Antonino-Daviu,et al.  A General Approach for the Transient Detection of Slip-Dependent Fault Components Based on the Discrete Wavelet Transform , 2008, IEEE Transactions on Industrial Electronics.

[29]  M. Moalem,et al.  Intelligent Diagnosis of Broken Bars in Induction Motors Based on New Features in Vibration Spectrum , 2007, 2007 IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics and Drives.

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

[31]  Jie Chen,et al.  Robust Model-Based Fault Diagnosis for Dynamic Systems , 1998, The International Series on Asian Studies in Computer and Information Science.

[32]  Rui Xu,et al.  Survey of clustering algorithms , 2005, IEEE Transactions on Neural Networks.

[33]  H. Razik,et al.  Fault detection and diagnosis of induction motors based on hidden Markov model , 2012, 2012 XXth International Conference on Electrical Machines.

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

[35]  S. Azizi-Ghannad,et al.  An acoustic diagnostic technique for use with electric machine insulation , 1994 .

[36]  Cheng-Hong Chang A fast method for determining electrical and mechanical qualities of induction motors using a PC-aided detector , 1994 .

[37]  Arturo Garcia-Perez,et al.  Automatic Online Diagnosis Algorithm for Broken-Bar Detection on Induction Motors Based on Discrete Wavelet Transform for FPGA Implementation , 2008, IEEE Transactions on Industrial Electronics.

[38]  O. Ondel,et al.  A method to detect broken bars in induction machine using pattern recognition techniques , 2006, IEEE Transactions on Industry Applications.

[39]  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).

[40]  Ian T. Jolliffe,et al.  Principal Component Analysis , 2002, International Encyclopedia of Statistical Science.

[41]  Hubert Razik,et al.  Induction Motor Diagnosis Using Line Neutral Voltage Signatures , 2009, IEEE Transactions on Industrial Electronics.

[42]  C. H. Rojas,et al.  Study of an induction motor working under stator winding inter-turn short circuit condition , 2003, 4th IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics and Drives, 2003. SDEMPED 2003..

[43]  F. Fnaiech,et al.  Wound-rotor induction generator short-circuit fault classification using a new neural network based on digital data , 2011, 8th IEEE Symposium on Diagnostics for Electrical Machines, Power Electronics & Drives.

[44]  C. Mechefske,et al.  Detection of Induction Motor Faults: A Comparison of Stator Current, Vibration and Acoustic Methods , 2006 .

[45]  A. Benchaib,et al.  Detection of broken bars in induction motors using an extended Kalman filter for rotor resistance sensorless estimation , 2000 .

[46]  Hubert Razik,et al.  On the Use of Slot Harmonics as a Potential Indicator of Rotor Bar Breakage in the Induction Machine , 2009, IEEE Transactions on Industrial Electronics.

[47]  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.

[48]  Mohamed Benbouzid,et al.  Monitoring and diagnosis of induction motors electrical faults using a current Park's vector pattern learning approach , 2000 .

[49]  Daniel Svozil,et al.  Introduction to multi-layer feed-forward neural networks , 1997 .

[50]  G. A. Capolino,et al.  A neural approach for the fault diagnostics in induction machines , 2002, IEEE 2002 28th Annual Conference of the Industrial Electronics Society. IECON 02.

[51]  T.G. Habetler,et al.  Detecting Rotor Faults in Low Power Permanent Magnet Synchronous Machines , 2007, IEEE Transactions on Power Electronics.

[52]  E. R. Filho,et al.  Automatic three-phase squirrel-cage induction motor test assembly for motor thermal behaviour studies , 1994, Proceedings of 1994 IEEE International Symposium on Industrial Electronics (ISIE'94).

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

[54]  R. Fisher THE USE OF MULTIPLE MEASUREMENTS IN TAXONOMIC PROBLEMS , 1936 .

[55]  Raphael Romary,et al.  Induction machine fault diagnosis using an external radial flux sensor , 2005 .

[56]  Jose A. Antonino-Daviu,et al.  Instantaneous Frequency of the Left Sideband Harmonic During the Start-Up Transient: A New Method for Diagnosis of Broken Bars , 2009, IEEE Transactions on Industrial Electronics.

[57]  Xuemei Li,et al.  The Fault Diagnosis of Garbage Crusher Based on Ant Colony Algorithm and Neural Network , 2009, 2009 Third International Conference on Genetic and Evolutionary Computing.