Optimized Deep Belief Network for Efficient Fault Detection in Induction Motor
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...
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| Format: | Article |
| Language: | English |
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Ediciones Universidad de Salamanca
2024-07-01
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| Series: | Advances in Distributed Computing and Artificial Intelligence Journal |
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| Online Access: | https://revistas.usal.es/cinco/index.php/2255-2863/article/view/31616 |
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| author | Pradeep Katta K. Karunanithi S. P. Raja S. Ramesh S. Vinoth John Prakash Deepthi Joseph |
| author_facet | Pradeep Katta K. Karunanithi S. P. Raja S. Ramesh S. Vinoth John Prakash Deepthi Joseph |
| author_sort | Pradeep Katta |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-218f304bf4224494b68014ba8b045252 |
| institution | Kabale University |
| issn | 2255-2863 |
| language | English |
| publishDate | 2024-07-01 |
| publisher | Ediciones Universidad de Salamanca |
| record_format | Article |
| series | Advances in Distributed Computing and Artificial Intelligence Journal |
| spelling | doaj-art-218f304bf4224494b68014ba8b0452522025-01-23T11:25:18ZengEdiciones Universidad de SalamancaAdvances in Distributed Computing and Artificial Intelligence Journal2255-28632024-07-0113e31616e3161610.14201/adcaij.3161637097Optimized Deep Belief Network for Efficient Fault Detection in Induction MotorPradeep Katta0K. Karunanithi1S. P. Raja2S. Ramesh3S. Vinoth John Prakash4Deepthi Joseph5School of Electrical and Communication Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai, Tamilnadu, IndiaSchool of Electrical and Communication Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai, Tamilnadu, IndiaSchool of Computer Science and Engineering, Vellore Institute of Technology, Vellore, Tamilnadu, IndiaSchool of Electrical and Communication Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai, Tamilnadu, IndiaSchool of Electrical and Communication Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai, Tamilnadu, IndiaDepartment of Electrical and Electronics Engineering, Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College, Chennai, Tamilnadu, IndiaNumerous 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.https://revistas.usal.es/cinco/index.php/2255-2863/article/view/31616induction motordeep learningdeep belief networks (dbn)restricted boltzmann machine (rbn)ant colony algorithm |
| spellingShingle | Pradeep Katta K. Karunanithi S. P. Raja S. Ramesh S. Vinoth John Prakash Deepthi Joseph Optimized Deep Belief Network for Efficient Fault Detection in Induction Motor Advances in Distributed Computing and Artificial Intelligence Journal induction motor deep learning deep belief networks (dbn) restricted boltzmann machine (rbn) ant colony algorithm |
| title | Optimized Deep Belief Network for Efficient Fault Detection in Induction Motor |
| title_full | Optimized Deep Belief Network for Efficient Fault Detection in Induction Motor |
| title_fullStr | Optimized Deep Belief Network for Efficient Fault Detection in Induction Motor |
| title_full_unstemmed | Optimized Deep Belief Network for Efficient Fault Detection in Induction Motor |
| title_short | Optimized Deep Belief Network for Efficient Fault Detection in Induction Motor |
| title_sort | optimized deep belief network for efficient fault detection in induction motor |
| topic | induction motor deep learning deep belief networks (dbn) restricted boltzmann machine (rbn) ant colony algorithm |
| url | https://revistas.usal.es/cinco/index.php/2255-2863/article/view/31616 |
| work_keys_str_mv | AT pradeepkatta optimizeddeepbeliefnetworkforefficientfaultdetectionininductionmotor AT kkarunanithi optimizeddeepbeliefnetworkforefficientfaultdetectionininductionmotor AT spraja optimizeddeepbeliefnetworkforefficientfaultdetectionininductionmotor AT sramesh optimizeddeepbeliefnetworkforefficientfaultdetectionininductionmotor AT svinothjohnprakash optimizeddeepbeliefnetworkforefficientfaultdetectionininductionmotor AT deepthijoseph optimizeddeepbeliefnetworkforefficientfaultdetectionininductionmotor |