A dedicated Application of artificial ants for the condition monitoring of induction motors

In the last decade, the field of diagnosis has attracted the attention of many researchers, especially for the detection of faults in induction motors. The condition monitoring of induction motors is generally based on the analysis of signals coming from one or several sensors. This analysis is performed by the motor current signature analysis (MCSA) which is considered as the most popular fault detection technique. This approach considers that a failed component generates a frequency in the motor current spectrum and measuring the amplitude of this frequency can help us to identify and quantify the fault severity. So, the frequency amplitude of the faulty component has to be known. This paper suggests the use of a heuristic technique inspired by the behavior of a colony of ants to track these frequencies. This technique is very easy to implement and converge quickly to a solution. The proposed technique is described and the experimental results illustrate this novel technique.

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

[2]  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.

[3]  M. Sahraoui,et al.  Broken bar fault diagnosis of induction motors using MCSA and neural network , 2011, 8th IEEE Symposium on Diagnostics for Electrical Machines, Power Electronics & Drives.

[4]  Wei Zhou,et al.  Stator Current-Based Bearing Fault Detection Techniques: A General Review , 2007, 2007 IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics and Drives.

[5]  Kai Zhang,et al.  An improved ant colony optimization for communication network routing problem , 2009, 2009 Fourth International on Conference on Bio-Inspired Computing.

[6]  Bong-Hwan Kwon,et al.  Online Diagnosis of Induction Motors Using MCSA , 2006, IEEE Transactions on Industrial Electronics.

[7]  Luca Maria Gambardella,et al.  Ant Algorithms for Discrete Optimization , 1999, Artificial Life.

[8]  Luca Maria Gambardella,et al.  Ant colony system: a cooperative learning approach to the traveling salesman problem , 1997, IEEE Trans. Evol. Comput..

[9]  G. Clerc,et al.  Faults classification of induction machine using an improved ant clustering technique , 2011, 8th IEEE Symposium on Diagnostics for Electrical Machines, Power Electronics & Drives.

[10]  Hamid A. Toliyat,et al.  Phase-Sensitive Detection of Motor Fault Signatures in the Presence of Noise , 2008, IEEE Transactions on Industrial Electronics.

[11]  Kai Zhang,et al.  An improved ant colony optimization for communication network routing problem , 2009 .

[12]  M.F. Cabanas,et al.  Application of a Dynamic Model based on a Network of Magnetically Coupled Reluctances to Rotor Fault Diagnosis in Induction Motors , 2007, 2007 IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics and Drives.

[13]  E. R. C. da Silva,et al.  The tracking of induction motor's faulty lines through particle swarm optimization using chaos , 2010, 2010 IEEE International Conference on Industrial Technology.

[14]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[15]  Mohammad Majid al-Rifaie,et al.  An investigation into the merger of stochastic diffusion search and particle swarm optimisation , 2011, GECCO '11.

[16]  Guy Clerc,et al.  Fault diagnosis in an induction motor by pattern recognition methods , 2003, 4th IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics and Drives, 2003. SDEMPED 2003..

[17]  Hubert Razik,et al.  A Novel Monitoring of Load Level and Broken Bar Fault Severity Applied to Squirrel-Cage Induction Motors Using a Genetic Algorithm , 2009, IEEE Transactions on Industrial Electronics.

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

[19]  R. Eberhart,et al.  Particle Swarm Optimization-Neural Networks, 1995. Proceedings., IEEE International Conference on , 2004 .

[20]  K. Sankar,et al.  Ant Colony algorithm for routing problem using rule-mining , 2010, 2010 IEEE International Conference on Computational Intelligence and Computing Research.

[21]  E. Blanco,et al.  Fault Detection and Diagnosis in a Set “Inverter–Induction Machine” Through Multidimensional Membership Function and Pattern Recognition , 2009, IEEE Transactions on Energy Conversion.

[22]  Seungdeog Choi,et al.  Implementation of a Fault-Diagnosis Algorithm for Induction Machines Based on Advanced Digital-Signal-Processing Techniques , 2011, IEEE Transactions on Industrial Electronics.