Investigation of Transfer Learning Method for Motor Fault Detection
Industry 4.0 is propelling modern industries forward due to its reliability, stability, and performance. Electric motors (EMs) are utilized in multiple industries for their efficiency, precise speed and torque control, and robustness. Detecting faults in motors at an early stage is crucial to ensure...
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MDPI AG
2025-04-01
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| Online Access: | https://www.mdpi.com/2075-1702/13/4/329 |
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| author | Prashant Kumar Saurabh Singh Doug Young Song |
| author_facet | Prashant Kumar Saurabh Singh Doug Young Song |
| author_sort | Prashant Kumar |
| collection | DOAJ |
| description | Industry 4.0 is propelling modern industries forward due to its reliability, stability, and performance. Electric motors (EMs) are utilized in multiple industries for their efficiency, precise speed and torque control, and robustness. Detecting faults in motors at an early stage is crucial to ensure maximum productivity. Recently, DL has been implemented as a data-driven approach for detecting faults in motors. However, due to the limited availability of labeled fault data, the performance of the DL model is constrained. This issue is addressed by leveraging transfer learning (TL), which uses knowledge from a larger source domain to improve performance in a smaller target domain. In this paper, a multiple fault detection (FD) model for EMs is proposed by combining the ideas of deep convolutional neural networks (CNNs) and TL. A one-dimensional signal-to-image conversion technique is suggested for converting the vibration signal to images, and an Inception-ResNet-v2-inspired FD model is proposed for detecting bearing faults in the motor. The proposed method achieved a mean accuracy of more than 99%. |
| format | Article |
| id | doaj-art-bea403ae07dd4e968cfb3ac636ff9fa6 |
| institution | DOAJ |
| issn | 2075-1702 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Machines |
| spelling | doaj-art-bea403ae07dd4e968cfb3ac636ff9fa62025-08-20T03:13:32ZengMDPI AGMachines2075-17022025-04-0113432910.3390/machines13040329Investigation of Transfer Learning Method for Motor Fault DetectionPrashant Kumar0Saurabh Singh1Doug Young Song2Department of AI and Big Data, Woosong University, Daejeon 34606, Republic of KoreaDepartment of AI and Big Data, Woosong University, Daejeon 34606, Republic of KoreaDepartment of AI and Big Data, Woosong University, Daejeon 34606, Republic of KoreaIndustry 4.0 is propelling modern industries forward due to its reliability, stability, and performance. Electric motors (EMs) are utilized in multiple industries for their efficiency, precise speed and torque control, and robustness. Detecting faults in motors at an early stage is crucial to ensure maximum productivity. Recently, DL has been implemented as a data-driven approach for detecting faults in motors. However, due to the limited availability of labeled fault data, the performance of the DL model is constrained. This issue is addressed by leveraging transfer learning (TL), which uses knowledge from a larger source domain to improve performance in a smaller target domain. In this paper, a multiple fault detection (FD) model for EMs is proposed by combining the ideas of deep convolutional neural networks (CNNs) and TL. A one-dimensional signal-to-image conversion technique is suggested for converting the vibration signal to images, and an Inception-ResNet-v2-inspired FD model is proposed for detecting bearing faults in the motor. The proposed method achieved a mean accuracy of more than 99%.https://www.mdpi.com/2075-1702/13/4/329transfer learning (TL)convolutional neural network (CNN)bearing faultselectric motor |
| spellingShingle | Prashant Kumar Saurabh Singh Doug Young Song Investigation of Transfer Learning Method for Motor Fault Detection Machines transfer learning (TL) convolutional neural network (CNN) bearing faults electric motor |
| title | Investigation of Transfer Learning Method for Motor Fault Detection |
| title_full | Investigation of Transfer Learning Method for Motor Fault Detection |
| title_fullStr | Investigation of Transfer Learning Method for Motor Fault Detection |
| title_full_unstemmed | Investigation of Transfer Learning Method for Motor Fault Detection |
| title_short | Investigation of Transfer Learning Method for Motor Fault Detection |
| title_sort | investigation of transfer learning method for motor fault detection |
| topic | transfer learning (TL) convolutional neural network (CNN) bearing faults electric motor |
| url | https://www.mdpi.com/2075-1702/13/4/329 |
| work_keys_str_mv | AT prashantkumar investigationoftransferlearningmethodformotorfaultdetection AT saurabhsingh investigationoftransferlearningmethodformotorfaultdetection AT dougyoungsong investigationoftransferlearningmethodformotorfaultdetection |