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Performance Analysis of Expert Systems Incorporating Neural Network for Fault Detection of an Electric Motor

Authors: M. Khatami Rad, N. Jamali, M. Torabizadeh, A. Noshadi


In this paper, an artificial neural network simulator is employed to carry out diagnosis and prognosis on electric motor as rotating machinery based on predictive maintenance. Vibration data of the primary failed motor including unbalance, misalignment and bearing fault were collected for training the neural network. Neural network training was performed for a variety of inputs and the motor condition was used as the expert training information. The main purpose of applying the neural network as an expert system was to detect the type of failure and applying preventive maintenance. The advantage of this study is for machinery Industries by providing appropriate maintenance that has an essential activity to keep the production process going at all processes in the machinery industry. Proper maintenance is pivotal in order to prevent the possible failures in operating system and increase the availability and effectiveness of a system by analyzing vibration monitoring and developing expert system.

Keywords: Condition based monitoring, expert system, neural network, fault detection, vibration monitoring.

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[1] V. Narayana, Effective maintenance management.Risk and reliability strategies for optimizing performance. Industrial Press, New York, 2004.
[2] J. Lee, R. Abujamra, A. K. S. Jardine, D. Lin, and D. Banjevic, "An integrated platform for diagnostics, prognostics and maintenance optimization", The IMS -2004 International Conf. on Adv. Maintenance , Modeling, Simulation, Intelligent Monitoring of Degradations, Arles, France, 2004.
[3] M. Y. Chow and S. O. Yee, "Methodology for On-line Incipient Fault Detection in Single-phase Squirrel - Induction motors using Artificial Neural Networks", IEEE Trans, on Energy Conversion, vol. 6,no.3, September 1991.
[4] J. T. Renwick and P. E. Babson, "Vibration analysis-a proven technique as a predictive maintenance tool". IEEE Trans. Industrial Applications, vol. 21, no. 2,pp. 324-332, 1985.
[5] I. E. Alguindigue, L. A. Buczak, and R. E. Uhrig, "Monitoring and diagnosis of rolling element bearings using artificial neural networks." IEEE Transaction of Industrial Electronics, vol.40, no. 2, pp. 209-217, 1993.
[6] M. AbdKadir, S. Sharifah, and H. Takashi, "Predicting remaining useful life of rotating machinery based artificial neural network, "Computers and Mathematics with Applications, vol. 60, pp. 1078-1087,2010.
[7] J. H. Williams, A. Davies, and P. R. Drake, Condition-Based Maintenance and Machine Diagnostics, Chapman & Hall, London, 1994.
[8] H. Austerlitz, Data Acquisition Techniques using PCs. Academic Press, San Diego, CA, 2003.
[9] P. R. Lippman, "An Introduction to Computing with Neural Nets", IEEE Transaction on neural networks, vol. 1, no. 1, pp. 4-27, 1990.