FPGA Implementation of AI-Based Inverter IGBT Open Circuit Fault Diagnosis of Induction Motor Drives
In modern industrial manufacturing processes, induction motors are broadly utilized as industrial drives. Online condition monitoring and diagnosis of faults that occur inside and/or outside of the Induction Motor Drive (IMD) system make the motor highly reliable, helping to avoid unscheduled downti...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-04-01
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| Series: | Micromachines |
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| Online Access: | https://www.mdpi.com/2072-666X/13/5/663 |
| _version_ | 1797497755965325312 |
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| author | Nagalingam Rajeswaran Rajesh Thangaraj Lucian Mihet-Popa Kesava Vamsi Krishna Vajjala Özen Özer |
| author_facet | Nagalingam Rajeswaran Rajesh Thangaraj Lucian Mihet-Popa Kesava Vamsi Krishna Vajjala Özen Özer |
| author_sort | Nagalingam Rajeswaran |
| collection | DOAJ |
| description | In modern industrial manufacturing processes, induction motors are broadly utilized as industrial drives. Online condition monitoring and diagnosis of faults that occur inside and/or outside of the Induction Motor Drive (IMD) system make the motor highly reliable, helping to avoid unscheduled downtimes, which cause more revenue loss and disruption of production. This can be achieved only when the irregularities produced because of the faults are sensed at the moment they occur and diagnosed quickly so that suitable actions to protect the equipment can be taken. This requires intelligent control with a high-performance scheme. Hence, a Field Programmable Gate Array (FPGA) based on neuro-genetic implementation with a Back Propagation Neural network (BPN) is suggested in this article to diagnose the fault more efficiently and almost instantly. It is reported that the classification of the neural network will provide the output within 2 µs although the clone procedure with microcontroller requires 7 ms. This intelligent control with a high-performance technique is applied to the IMD fed by a Voltage Source Inverter (VSI) to diagnose the fault. The proposed approach was simulated and experimentally validated. |
| first_indexed | 2024-03-10T03:24:49Z |
| format | Article |
| id | doaj.art-2498ff960d4344d6a417c5d90b98daca |
| institution | Directory Open Access Journal |
| issn | 2072-666X |
| language | English |
| last_indexed | 2024-03-10T03:24:49Z |
| publishDate | 2022-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Micromachines |
| spelling | doaj.art-2498ff960d4344d6a417c5d90b98daca2023-11-23T12:11:14ZengMDPI AGMicromachines2072-666X2022-04-0113566310.3390/mi13050663FPGA Implementation of AI-Based Inverter IGBT Open Circuit Fault Diagnosis of Induction Motor DrivesNagalingam Rajeswaran0Rajesh Thangaraj1Lucian Mihet-Popa2Kesava Vamsi Krishna Vajjala3Özen Özer4Electrical and Electronics Engineering, Malla Reddy Engineering College, Secunderabad 500100, IndiaElectrical and Electronics Engineering, Malla Reddy Engineering College, Secunderabad 500100, IndiaFaculty of Information Technology, Engineering, and Economics, Oestfold University College, 1757 Halden, NorwayDepartment of Physics, Malla Reddy Engineering College, Secunderabad 500100, IndiaDepartment of Mathematics, Faculty of Science and Arts, Kırklareli University, Kırklareli 39100, TurkeyIn modern industrial manufacturing processes, induction motors are broadly utilized as industrial drives. Online condition monitoring and diagnosis of faults that occur inside and/or outside of the Induction Motor Drive (IMD) system make the motor highly reliable, helping to avoid unscheduled downtimes, which cause more revenue loss and disruption of production. This can be achieved only when the irregularities produced because of the faults are sensed at the moment they occur and diagnosed quickly so that suitable actions to protect the equipment can be taken. This requires intelligent control with a high-performance scheme. Hence, a Field Programmable Gate Array (FPGA) based on neuro-genetic implementation with a Back Propagation Neural network (BPN) is suggested in this article to diagnose the fault more efficiently and almost instantly. It is reported that the classification of the neural network will provide the output within 2 µs although the clone procedure with microcontroller requires 7 ms. This intelligent control with a high-performance technique is applied to the IMD fed by a Voltage Source Inverter (VSI) to diagnose the fault. The proposed approach was simulated and experimentally validated.https://www.mdpi.com/2072-666X/13/5/663condition monitoringInduction Motor Drivefault diagnosisFPGABack Propagation Neural NetworkDiscrete Wavelet Transforms |
| spellingShingle | Nagalingam Rajeswaran Rajesh Thangaraj Lucian Mihet-Popa Kesava Vamsi Krishna Vajjala Özen Özer FPGA Implementation of AI-Based Inverter IGBT Open Circuit Fault Diagnosis of Induction Motor Drives Micromachines condition monitoring Induction Motor Drive fault diagnosis FPGA Back Propagation Neural Network Discrete Wavelet Transforms |
| title | FPGA Implementation of AI-Based Inverter IGBT Open Circuit Fault Diagnosis of Induction Motor Drives |
| title_full | FPGA Implementation of AI-Based Inverter IGBT Open Circuit Fault Diagnosis of Induction Motor Drives |
| title_fullStr | FPGA Implementation of AI-Based Inverter IGBT Open Circuit Fault Diagnosis of Induction Motor Drives |
| title_full_unstemmed | FPGA Implementation of AI-Based Inverter IGBT Open Circuit Fault Diagnosis of Induction Motor Drives |
| title_short | FPGA Implementation of AI-Based Inverter IGBT Open Circuit Fault Diagnosis of Induction Motor Drives |
| title_sort | fpga implementation of ai based inverter igbt open circuit fault diagnosis of induction motor drives |
| topic | condition monitoring Induction Motor Drive fault diagnosis FPGA Back Propagation Neural Network Discrete Wavelet Transforms |
| url | https://www.mdpi.com/2072-666X/13/5/663 |
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