Advanced Dual RNN Architecture for Electrical Motor Fault Classification
In recent years, there has been a remarkable increase in the usage of Deep Neural Networks (DNNs) for addressing and solving electrical field problems. This research primarily aims to present an advanced approach to classify different motor faults based on their time-series data by implementing a ne...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10365171/ |
| _version_ | 1797361431255973888 |
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| author | Yousef Alkhanafseh Tahir Cetin Akinci Emine Ayaz Alfredo A. Martinez-Morales |
| author_facet | Yousef Alkhanafseh Tahir Cetin Akinci Emine Ayaz Alfredo A. Martinez-Morales |
| author_sort | Yousef Alkhanafseh |
| collection | DOAJ |
| description | In recent years, there has been a remarkable increase in the usage of Deep Neural Networks (DNNs) for addressing and solving electrical field problems. This research primarily aims to present an advanced approach to classify different motor faults based on their time-series data by implementing a new Recurrent Neural Network (RNN) model that consists of mixed Long short-term memory (LSTM), Gated Recurrent Unit (GRU), and two Fully Connected (FC) layers. The main idea of this study centers on developing one comprehensive model capable of categorizing primary motor faults. The proposed model is supposed to classify 10 different classes, extracted from the Machinery Fault Database (MaFaulDa), which are normal (no-fault), vertical misalignment, horizontal misalignment, imbalance, overhang-ball, overhang-cage, overhang-outer race, underhang-ball, underhang-outer race, and underhang-cage. Classifying 10 different situations can be considered as a notable classification problem. Additionally, the learning period did not include any data augmentation, which reflects the model’s power in training over the available data. Significantly, the accuracy of the model is enhanced by setting precise values for hyperparameters, including network structure (number of layers and neurons), learning rate, regularization, optimizer type, number of epochs, and more. The obtained train-validation-test accuracies from the proposed model are 99.87%, 99.599%, and 99.48%, respectively. The accuracy of the model represents the highest accuracy among other publications. This advanced approach offers numerous advantages, including early-stage fault detection, improved robustness in industrial maintenance, and generating fast and intelligent alerts, thereby reducing the possible damage to electrical instruments. |
| first_indexed | 2024-03-08T15:54:36Z |
| format | Article |
| id | doaj.art-5b11911216064d6286ac1d3d5f14686b |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-08T15:54:36Z |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-5b11911216064d6286ac1d3d5f14686b2024-01-09T00:04:31ZengIEEEIEEE Access2169-35362024-01-01122965297610.1109/ACCESS.2023.334467610365171Advanced Dual RNN Architecture for Electrical Motor Fault ClassificationYousef Alkhanafseh0https://orcid.org/0000-0002-6229-6703Tahir Cetin Akinci1https://orcid.org/0000-0002-4657-6617Emine Ayaz2Alfredo A. Martinez-Morales3https://orcid.org/0000-0003-4204-2228Electrical Engineering Department, Istanbul Technical University, Istanbul, TurkeyElectrical Engineering Department, Istanbul Technical University, Istanbul, TurkeyElectrical Engineering Department, Istanbul Technical University, Istanbul, TurkeyBourns College of Engineering, Center for Environmental Research and Technology, University of California, Riverside, Riverside, CA, USAIn recent years, there has been a remarkable increase in the usage of Deep Neural Networks (DNNs) for addressing and solving electrical field problems. This research primarily aims to present an advanced approach to classify different motor faults based on their time-series data by implementing a new Recurrent Neural Network (RNN) model that consists of mixed Long short-term memory (LSTM), Gated Recurrent Unit (GRU), and two Fully Connected (FC) layers. The main idea of this study centers on developing one comprehensive model capable of categorizing primary motor faults. The proposed model is supposed to classify 10 different classes, extracted from the Machinery Fault Database (MaFaulDa), which are normal (no-fault), vertical misalignment, horizontal misalignment, imbalance, overhang-ball, overhang-cage, overhang-outer race, underhang-ball, underhang-outer race, and underhang-cage. Classifying 10 different situations can be considered as a notable classification problem. Additionally, the learning period did not include any data augmentation, which reflects the model’s power in training over the available data. Significantly, the accuracy of the model is enhanced by setting precise values for hyperparameters, including network structure (number of layers and neurons), learning rate, regularization, optimizer type, number of epochs, and more. The obtained train-validation-test accuracies from the proposed model are 99.87%, 99.599%, and 99.48%, respectively. The accuracy of the model represents the highest accuracy among other publications. This advanced approach offers numerous advantages, including early-stage fault detection, improved robustness in industrial maintenance, and generating fast and intelligent alerts, thereby reducing the possible damage to electrical instruments.https://ieeexplore.ieee.org/document/10365171/Condition monitoringGRULSTMmotor faults classificationrecurrent neural networks |
| spellingShingle | Yousef Alkhanafseh Tahir Cetin Akinci Emine Ayaz Alfredo A. Martinez-Morales Advanced Dual RNN Architecture for Electrical Motor Fault Classification IEEE Access Condition monitoring GRU LSTM motor faults classification recurrent neural networks |
| title | Advanced Dual RNN Architecture for Electrical Motor Fault Classification |
| title_full | Advanced Dual RNN Architecture for Electrical Motor Fault Classification |
| title_fullStr | Advanced Dual RNN Architecture for Electrical Motor Fault Classification |
| title_full_unstemmed | Advanced Dual RNN Architecture for Electrical Motor Fault Classification |
| title_short | Advanced Dual RNN Architecture for Electrical Motor Fault Classification |
| title_sort | advanced dual rnn architecture for electrical motor fault classification |
| topic | Condition monitoring GRU LSTM motor faults classification recurrent neural networks |
| url | https://ieeexplore.ieee.org/document/10365171/ |
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