Total views : 339

A Hybrid Monitoring Technique for Diagnosis of Mechanical Faults in Induction Motor


  • Quaid-e-Awam University College of Engineering, Science and Technology, Nawabshah, Pakistan
  • Mehran University of Engineering and Technology, Jamshoro, Pakistan
  • D1Quaid-e-Awam University College of Engineering, Science and Technology, Nawabshah, Pakistan


The paper aims to engineer an efficient technique for the condition monitoring of induction motor that not only provides the detection and identification of the faults but also assesses the operational condition of the motor. Induction motors possess one of the most important roles industrially and commercially. The developing faults in the motors can become catastrophic, if remain unanalyzed. The paper presents an effective novel solution to diagnose the major mechanical faults at the early possible stage by utilizing two efficient condition monitoring techniques to effectively deploy the strategies for the predictive maintenance. Primarily it employs the MCSA (Motor Current Signature Analysis), in which the faults are located by the spectral analysis of the particular harmonic components in the line current at specific characteristic frequencies generated by specific faults as the unique rotating flux. Fuzzy logic system has also been utilized, which assesses severity of the fault and operating condition of machine. The induction machine’s modular Simulink implementation has been presented that unlike other approaches provides the access to almost all parameters of the machine for analysis and control purposes. The mechanical faults specially bearing and eccentricity faults are simulated and successfully detected and localized in the results along with the severity assessment of the operational condition due to simulated faults.


Bearing faults, Condition Monitoring, Eccentricity, Fault Diagnosis, Fuzzy Logic, MCSA.

Full Text:

 |  (PDF views: 245)


  • Bouchikhi E, Choqueuse V, Benbouzid M. Induction machine faults detection using stator current parametric spectral estimation. Mechanical Systems and Signal Processing.2015; 52(53):447–64.
  • Choi S, Akin B, Rahimian MM, Toliyat HA. Implementation of a fault-diagnosis algorithm for induction machines based on advanced digital-signal-processing techniques.IEEE Trans Ind Electron. 2011; 58(3):937–48.
  • Janier JB, Zaharia MF. condition monitoring system for induction motor using fuzzy logic tool. Proc of first IEEE Int Conf Informatics and Computational Intelligence (ICI); Bandung, Indonesia. 2011. p. 3–7.
  • Thomson WT, Gilmore RJ. Motor current signature analysis to detect faults in induction motor drives-fundamentals, data interpretation, and industrial case histories. Proc of 32rd Turbomachinery Symposium; Texas, A&M University, USA. 2003. p. 45–156.
  • Frosini L, Bassi E. Stator current and motor efficiency as indicators for different types of bearing faults in induction motors. IEEE Trans Ind Electron. 2010; 57(1):44–251.
  • Huang S, Yu H. Intelligent fault monitoring and diagnosis in electrical machines. Measurement. 2013; 46(9):3640–6.
  • Szabo L, Biro KA, Fodor D, Kovasc E. Improved condition monitoring system for induction machines using a modelbased fault detection approach. Oradea University Annals, Electrotechnical Fascicle, Computer Science and Control Systems Session; Romania. 2006. p. 126–31.
  • Zhang P, Du Y, Habetler TG, Lu B. A survey of condition monitoring and protection methods for medium-voltage induction motors. IEEE Trans Ind Appl. 2011; 47(1):34–46.
  • Salah M, Bacha K, Chaari A. Comparative investigation of diagnosis media for induction machine mechanical unbalance fault. ISA Transactions. 2013; 52(6):888–99.
  • Bellini A, Filippetti F, Tassoni C. Advances in diagnostic techniques for induction machines. IEEE trans Ind Electron. 2008; 55(12):4109–26.
  • Jung JH, Lee JJ, Kwon BH. Online diagnosis of induction motors using MCSA. IEEE trans Ind Electron. 2006; 53(6):1842–52.
  • Zeraoulia M, Mamoune A, Mangel H, Benbouzid M. A simple fuzzy logic approach for induction motors condition monitoring. J Electrical Systems. 2005; 1(1):15–25.
  • Dash RN, Subudhi B. Stator inter-turn fault detection of an induction motor using neuro-fuzzy techniques. Archives of Control Sciences. 2010; 20(3):363–76.
  • Faiz J, Ebrahimi BM, Akin B, Toliyat HA. Comprehensive eccentricity fault diagnosis in induction motors using finite element method. IEEE Trans Magnetics. 2009; 45(3):1764– 7.
  • Ozpineci B, Tolbert LM. Simulink implementation of induction machine model - a modular approach. Proc IEEE Electric Machines and Drives Conf, IEMDC’03; 2003. p.728–34.
  • Krause PC. Analysis of electric machinery. New York: McGraw-Hill Book Company; 1986.
  • Blödt M, Granjon P, Raison B, Rostaing G. Models for bearing damage detection in induction motors using stator current monitoring. IEEE Trans Ind Electron. 2008; 55(4):1813–22.
  • Elbouchikhi E, Choqueuse V, Benbouzid M. Induction machine bearing faults detection based on a multi-dimensional MUSIC algorithm and maximum likelihood estimation.ISA Transactions. 2016; 63:413–24.
  • Rodríguez PVJ, Arkkio A. Detection of stator winding fault in induction motor using fuzzy logic. Applied Soft Computing.2008; 8(2):1112–20.
  • Akbari H. An analytical method for computation of induction machine inductances under rotor misalignment fault.Canadian J Pure and Applied Sciences. 2015; 9(1):3325–32.


  • There are currently no refbacks.

Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.