Regression Based Performance Analysis and Fault Detection in Induction Motors by Using Deep Learning Technique
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 alle...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Ediciones Universidad de Salamanca
2023-01-01
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| Series: | Advances in Distributed Computing and Artificial Intelligence Journal |
| Subjects: | |
| Online Access: | https://revistas.usal.es/cinco/index.php/2255-2863/article/view/28435 |
| _version_ | 1797943640532713472 |
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| author | Pradeep Katta Karunanithi Kandasamy Raja Soosaimarian Peter Raj Ramesh Subramanian Chandrasekar Perumal |
| author_facet | Pradeep Katta Karunanithi Kandasamy Raja Soosaimarian Peter Raj Ramesh Subramanian Chandrasekar Perumal |
| author_sort | Pradeep Katta |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-10T20:27:23Z |
| format | Article |
| id | doaj.art-56a7a56c2df24364a1a4da4a527f0d04 |
| institution | Directory Open Access Journal |
| issn | 2255-2863 |
| language | English |
| last_indexed | 2024-04-10T20:27:23Z |
| publishDate | 2023-01-01 |
| publisher | Ediciones Universidad de Salamanca |
| record_format | Article |
| series | Advances in Distributed Computing and Artificial Intelligence Journal |
| spelling | doaj.art-56a7a56c2df24364a1a4da4a527f0d042023-01-25T08:48:07ZengEdiciones Universidad de SalamancaAdvances in Distributed Computing and Artificial Intelligence Journal2255-28632023-01-0111334936510.14201/adcaij.2843533896Regression Based Performance Analysis and Fault Detection in Induction Motors by Using Deep Learning TechniquePradeep Katta0Karunanithi Kandasamy1Raja Soosaimarian Peter Raj2Ramesh Subramanian3Chandrasekar Perumal4School of Electrical and Communication EngineeringSchool of Electrical and Communication EngineeringSchool of Electrical and Communication EngineeringSchool of Electrical and Communication EngineeringSchool of Electrical and Communication EngineeringThe 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.https://revistas.usal.es/cinco/index.php/2255-2863/article/view/28435induction motordbnrbmfast fourier transform (fft)regression modeling |
| spellingShingle | Pradeep Katta Karunanithi Kandasamy Raja Soosaimarian Peter Raj Ramesh Subramanian Chandrasekar Perumal Regression Based Performance Analysis and Fault Detection in Induction Motors by Using Deep Learning Technique Advances in Distributed Computing and Artificial Intelligence Journal induction motor dbn rbm fast fourier transform (fft) regression modeling |
| title | Regression Based Performance Analysis and Fault Detection in Induction Motors by Using Deep Learning Technique |
| title_full | Regression Based Performance Analysis and Fault Detection in Induction Motors by Using Deep Learning Technique |
| title_fullStr | Regression Based Performance Analysis and Fault Detection in Induction Motors by Using Deep Learning Technique |
| title_full_unstemmed | Regression Based Performance Analysis and Fault Detection in Induction Motors by Using Deep Learning Technique |
| title_short | Regression Based Performance Analysis and Fault Detection in Induction Motors by Using Deep Learning Technique |
| title_sort | regression based performance analysis and fault detection in induction motors by using deep learning technique |
| topic | induction motor dbn rbm fast fourier transform (fft) regression modeling |
| url | https://revistas.usal.es/cinco/index.php/2255-2863/article/view/28435 |
| work_keys_str_mv | AT pradeepkatta regressionbasedperformanceanalysisandfaultdetectionininductionmotorsbyusingdeeplearningtechnique AT karunanithikandasamy regressionbasedperformanceanalysisandfaultdetectionininductionmotorsbyusingdeeplearningtechnique AT rajasoosaimarianpeterraj regressionbasedperformanceanalysisandfaultdetectionininductionmotorsbyusingdeeplearningtechnique AT rameshsubramanian regressionbasedperformanceanalysisandfaultdetectionininductionmotorsbyusingdeeplearningtechnique AT chandrasekarperumal regressionbasedperformanceanalysisandfaultdetectionininductionmotorsbyusingdeeplearningtechnique |