Regression Based Performance Analysis and Fault Detection in Induction Motors by Using Deep Learning Technique

  • Pradeep Katta
    School of Electrical and Communication Engineering pradeep.2048[at]gmail.com
  • Karunanithi Kandasamy
    School of Electrical and Communication Engineering
  • Raja Soosaimarian Peter Raj
    School of Electrical and Communication Engineering
  • Ramesh Subramanian
    School of Electrical and Communication Engineering
  • Chandrasekar Perumal
    School of Electrical and Communication Engineering

Abstract

The recent improvements related to the area of electric locomotive, power electronics, assembly processes and manufacturing of machines have increased the robustness and reliability of induction motors. Regardless of the increased availability, the application of induction motors in many fields alleges the need for operating state supervision and condition monitoring. In other words, fault identification at the initial stage helps make appropriate control decisions, influencing product quality as well as providing safety. Inspired by these demands, this work proposes a regression based modeling for the analysis of performance in induction motors. In this approach, the feature extraction process is combined with classification for efficient fault detection. Deep Belief Network (DBN) stacked with multiple Restricted Boltzmann Machine (RBM) is exploited for the robust diagnosis of faults with the adoption of training process. The influences of harmonics over induction motors are identified and the losses are mitigated. The simulation of the suggested approach and its comparison with traditional approaches are executed. An overall accuracy of 99.5% is obtained which in turn proves the efficiency of DBN in detecting faults.
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Katta, P., Kandasamy, K., Soosaimarian Peter Raj, R., Subramanian, R., & Perumal, C. (2023). Regression Based Performance Analysis and Fault Detection in Induction Motors by Using Deep Learning Technique. ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, 11(3), 349–365. https://doi.org/10.14201/adcaij.28435

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