Predictive Maintenance of Motor Using Machine Learning



EOI: 10.11242/viva-tech.01.05.001

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Citation

Siddhesh Darje, Premchandra Kumbhar, Nilesh Marchande, Dr. Deepak Sajnekar, "Predictive Maintenance of Motor Using Machine Learning", VIVA-IJRI Volume 1, Issue 6, Article 18, pp. 1-5, 2023. Published by Computer Engineering Department, VIVA Institute of Technology, Virar, India.

Abstract

As we all know that Condition monitoring together with predictive maintenance of electric motors and other equipment used by the industry avoids severe economic losses resulting from unexpected motor failures and greatly improves the system reliability. This paper describes a Machine Learning architecture for Predictive Maintenance, based on Machine Learning approach. The system was tested on a real industry example, by developing the data collection and data system analysis, applying the Machine Learning approach and comparing it to the simulation tool analysis. Data has been collected by various sensors. With the help of this paper, we want to monitor and increase the life span of Electric motor and other equipment's.

Keywords

Predictive maintenance, Fault Diagnosis, Anomaly Detection, Deep Learning.

References

  1. H. M. Hashemian and W. C. Bean, “State-of-theart predictive maintenance techniques,” IEEE Transactions on Instrumentation and measurement, vol. 60, no. 10, pp. 3480–3492, 2019.
  2. S.-j. Wu, N. Gebraeel, M. A. Lawley, and Y. Yih, “A neural network integrated decision support system for condition-based optimal predictive maintenance policy,” IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans, vol. 37, no. 2, pp. 226–236, 2017.
  3. E. Frontoni, R. Pollini, P. Russo, P. Zingaretti, and G. Cerri, “Hdomo: Smart sensor integration for an active and independent longevity of the elderly,” Sensors, vol. 17, no. 11, p. 2610, 2017.
  4. B. Lu, D. B. Durocher, and P. Stemper, “Predictive maintenance techniques,” IEEE Industry Applications Magazine, vol. 15, no. 6, 2019.
  5. B. Lu, T. G. Habetler, and R. G. Harley, “A survey of efficiencyestimation methods for inservice induction motors,” IEEE Transactions on Industry Applications, vol. 42, no. 4, pp. 924–933, 2020.
  6. W. T. Thomson and M. Fenger, “Current signature analysis to detect induction motor faults,” IEEE Industry Applications Magazine, vol. 7, no. 4, pp. 26–34, 2019.
  7. R. Yam, P. Tse, L. Li, and P. Tu, “Intelligent predictive decision support system for conditionbased maintenance,” The International Journal of Advanced Manufacturing Technology, vol. 17, no. 5, pp. 383–391, 2018.
  8. J.-H. Shin and H.-B. Jun, “On condition based maintenance policy,” Journal of Computational Design and Engineering, vol. 2, no. 2, pp. 119–127, 2021.
  9. G. A. Susto, A. Schirru, S. Pampuri, S. McLoone, and A. Beghi, “Machine learning for predictive maintenance: A multiple classifier approach,” IEEE Transactions on Industrial Informatics, vol. 11, no. 3, pp. 812–820, 2020.
  10. K. Wang, “Intelligent predictive maintenance (ipdm) system–industry 4.0 scenario,” WIT Transactions on Engineering Sciences, vol. 113, pp. 259–268, 2016.