Signal Spectrum-Based Machine Learning Approach for Fault Prediction and Maintenance of Electrical Machines
Industrial revolution 4.0 has enabled the advent of new technological advancements, including the introduction of information technology with physical devices. The implementation of information technology in industrial applications has helped streamline industrial processes and make them more cost-e...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-12-01
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| Series: | Energies |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1996-1073/15/24/9507 |
| _version_ | 1827640091360624640 |
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| author | Hadi Ashraf Raja Karolina Kudelina Bilal Asad Toomas Vaimann Ants Kallaste Anton Rassõlkin Huynh Van Khang |
| author_facet | Hadi Ashraf Raja Karolina Kudelina Bilal Asad Toomas Vaimann Ants Kallaste Anton Rassõlkin Huynh Van Khang |
| author_sort | Hadi Ashraf Raja |
| collection | DOAJ |
| description | Industrial revolution 4.0 has enabled the advent of new technological advancements, including the introduction of information technology with physical devices. The implementation of information technology in industrial applications has helped streamline industrial processes and make them more cost-efficient. This combination of information technology and physical devices gave birth to smart devices, which opened up a new research area known as the Internet of Things (IoT). This has enabled researchers to help reduce downtime and maintenance costs by applying condition monitoring on electrical machines utilizing machine learning algorithms. Although the industry is trying to move from scheduled maintenance towards predictive maintenance, there is a significant lack of algorithms related to fault prediction of electrical machines. There is quite a lot of research going on in this area, but it is still underdeveloped and needs a lot more work. This paper presents a signal spectrum-based machine learning approach toward the fault prediction of electrical machines. The proposed method is a new approach to the predictive maintenance of electrical machines. This paper presents the details regarding the algorithm and then validates the accuracy against data collected from working electrical machines for both cases. A comparison is also presented at the end of multiple machine learning algorithms used for training based on this approach. |
| first_indexed | 2024-03-09T16:54:09Z |
| format | Article |
| id | doaj.art-cd8c2cd6bba44af5866c4c00e28c854c |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-09T16:54:09Z |
| publishDate | 2022-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-cd8c2cd6bba44af5866c4c00e28c854c2023-11-24T14:38:11ZengMDPI AGEnergies1996-10732022-12-011524950710.3390/en15249507Signal Spectrum-Based Machine Learning Approach for Fault Prediction and Maintenance of Electrical MachinesHadi Ashraf Raja0Karolina Kudelina1Bilal Asad2Toomas Vaimann3Ants Kallaste4Anton Rassõlkin5Huynh Van Khang6Department of Electrical Power Engineering & Mechatronics, Tallinn University of Technology, 19086 Tallinn, EstoniaDepartment of Electrical Power Engineering & Mechatronics, Tallinn University of Technology, 19086 Tallinn, EstoniaDepartment of Electrical Power Engineering & Mechatronics, Tallinn University of Technology, 19086 Tallinn, EstoniaDepartment of Electrical Power Engineering & Mechatronics, Tallinn University of Technology, 19086 Tallinn, EstoniaDepartment of Electrical Power Engineering & Mechatronics, Tallinn University of Technology, 19086 Tallinn, EstoniaDepartment of Electrical Power Engineering & Mechatronics, Tallinn University of Technology, 19086 Tallinn, EstoniaDepartment of Engineering Sciences, University of Agder, 4604 Kristiansand, NorwayIndustrial revolution 4.0 has enabled the advent of new technological advancements, including the introduction of information technology with physical devices. The implementation of information technology in industrial applications has helped streamline industrial processes and make them more cost-efficient. This combination of information technology and physical devices gave birth to smart devices, which opened up a new research area known as the Internet of Things (IoT). This has enabled researchers to help reduce downtime and maintenance costs by applying condition monitoring on electrical machines utilizing machine learning algorithms. Although the industry is trying to move from scheduled maintenance towards predictive maintenance, there is a significant lack of algorithms related to fault prediction of electrical machines. There is quite a lot of research going on in this area, but it is still underdeveloped and needs a lot more work. This paper presents a signal spectrum-based machine learning approach toward the fault prediction of electrical machines. The proposed method is a new approach to the predictive maintenance of electrical machines. This paper presents the details regarding the algorithm and then validates the accuracy against data collected from working electrical machines for both cases. A comparison is also presented at the end of multiple machine learning algorithms used for training based on this approach.https://www.mdpi.com/1996-1073/15/24/9507artificial intelligencefault predictionpredictive maintenancemachine learningneural network |
| spellingShingle | Hadi Ashraf Raja Karolina Kudelina Bilal Asad Toomas Vaimann Ants Kallaste Anton Rassõlkin Huynh Van Khang Signal Spectrum-Based Machine Learning Approach for Fault Prediction and Maintenance of Electrical Machines Energies artificial intelligence fault prediction predictive maintenance machine learning neural network |
| title | Signal Spectrum-Based Machine Learning Approach for Fault Prediction and Maintenance of Electrical Machines |
| title_full | Signal Spectrum-Based Machine Learning Approach for Fault Prediction and Maintenance of Electrical Machines |
| title_fullStr | Signal Spectrum-Based Machine Learning Approach for Fault Prediction and Maintenance of Electrical Machines |
| title_full_unstemmed | Signal Spectrum-Based Machine Learning Approach for Fault Prediction and Maintenance of Electrical Machines |
| title_short | Signal Spectrum-Based Machine Learning Approach for Fault Prediction and Maintenance of Electrical Machines |
| title_sort | signal spectrum based machine learning approach for fault prediction and maintenance of electrical machines |
| topic | artificial intelligence fault prediction predictive maintenance machine learning neural network |
| url | https://www.mdpi.com/1996-1073/15/24/9507 |
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