Optimized Deep Belief Network for Efficient Fault Detection in Induction Motor

• Katta, Pradeep ^[1] ; Karunanithi, K. ^[1] ; Raja, S. P. ^[2] ; Ramesh, S. ^[1] ; Prakash, S. Vinoth John ^[1] ; Joseph, Deepthi ^[3]
  1. [1] School of Electrical and Communication Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai, Tamilnadu, India
  2. [2] School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, Tamilnadu, India
  3. [3] Department of Electrical and Electronics Engineering, Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College, Chennai, Tamilnadu, India

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• Localización: ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, ISSN-e 2255-2863, Vol. 13, Nº. 1, 2024
• Idioma: inglés
• DOI: 10.14201/adcaij.31616
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• Resumen

  • Numerous industrial applications depend heavily on induction motors and their malfunction causes considerable financial losses. Induction motors in industrial processes have recently expanded dramatically in size, and complexity of defect identification and diagnostics for such systems has increased as well. As a result, research has concentrated on developing novel methods for the quick and accurate identification of induction motor problems.In response to these needs, this paper provides an optimised algorithm for analysing the performance of an induction motor. To analyse the operation of induction motors, an enhanced methodology on Deep Belief Networks (DBN) is introduced for recovering properties from the sensor identified vibration signals. Restricted Boltzmann Machine (RBM) is stacked utilizing multiple units of DBN model, which is then trained adopting Ant colony algorithm.An innovative method of feature extraction for autonomous fault analysis in manufacturing is provided by experimental investigations utilising vibration signals and overall accuracy of 99.8% is obtained, which therefore confirms the efficiency of DBN architecture for features extraction.

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