Trends and Challenges in Intelligent Condition Monitoring of Electrical Machines Using Machine Learning
A review of the fault diagnostic techniques based on machine is presented in this paper. As the world is moving towards industry 4.0 standards, the problems of limited computational power and available memory are decreasing day by day. A significant amount of data with a variety of faulty conditions...
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MDPI AG
2021-03-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/11/6/2761 |
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doaj-22a6295bdd7a4ecea719844f1d7a37402021-03-20T00:02:30ZengMDPI AGApplied Sciences2076-34172021-03-01112761276110.3390/app11062761Trends and Challenges in Intelligent Condition Monitoring of Electrical Machines Using Machine LearningKarolina Kudelina0Toomas Vaimann1Bilal Asad2Anton Rassõlkin3Ants Kallaste4Galina Demidova5Department of Electrical Power Engineering and Mechatronics, Tallinn University of Technology, 19086 Tallinn, EstoniaDepartment of Electrical Power Engineering and Mechatronics, Tallinn University of Technology, 19086 Tallinn, EstoniaDepartment of Electrical Power Engineering and Mechatronics, Tallinn University of Technology, 19086 Tallinn, EstoniaDepartment of Electrical Power Engineering and Mechatronics, Tallinn University of Technology, 19086 Tallinn, EstoniaDepartment of Electrical Power Engineering and Mechatronics, Tallinn University of Technology, 19086 Tallinn, EstoniaDepartment of Electrical Power Engineering and Mechatronics, Tallinn University of Technology, 19086 Tallinn, EstoniaA review of the fault diagnostic techniques based on machine is presented in this paper. As the world is moving towards industry 4.0 standards, the problems of limited computational power and available memory are decreasing day by day. A significant amount of data with a variety of faulty conditions of electrical machines working under different environments can be handled remotely using cloud computation. Moreover, the mathematical models of electrical machines can be utilized for the training of AI algorithms. This is true because the collection of big data is a challenging task for the industry and laboratory because of related limited resources. In this paper, some promising machine learning-based diagnostic techniques are presented in the perspective of their attributes.https://www.mdpi.com/2076-3417/11/6/2761fault diagnosticsmachine learningartificial intelligencepattern recognitionneural networks |
| collection |
DOAJ |
| language |
English |
| format |
Article |
| sources |
DOAJ |
| author |
Karolina Kudelina Toomas Vaimann Bilal Asad Anton Rassõlkin Ants Kallaste Galina Demidova |
| spellingShingle |
Karolina Kudelina Toomas Vaimann Bilal Asad Anton Rassõlkin Ants Kallaste Galina Demidova Trends and Challenges in Intelligent Condition Monitoring of Electrical Machines Using Machine Learning Applied Sciences fault diagnostics machine learning artificial intelligence pattern recognition neural networks |
| author_facet |
Karolina Kudelina Toomas Vaimann Bilal Asad Anton Rassõlkin Ants Kallaste Galina Demidova |
| author_sort |
Karolina Kudelina |
| title |
Trends and Challenges in Intelligent Condition Monitoring of Electrical Machines Using Machine Learning |
| title_short |
Trends and Challenges in Intelligent Condition Monitoring of Electrical Machines Using Machine Learning |
| title_full |
Trends and Challenges in Intelligent Condition Monitoring of Electrical Machines Using Machine Learning |
| title_fullStr |
Trends and Challenges in Intelligent Condition Monitoring of Electrical Machines Using Machine Learning |
| title_full_unstemmed |
Trends and Challenges in Intelligent Condition Monitoring of Electrical Machines Using Machine Learning |
| title_sort |
trends and challenges in intelligent condition monitoring of electrical machines using machine learning |
| publisher |
MDPI AG |
| series |
Applied Sciences |
| issn |
2076-3417 |
| publishDate |
2021-03-01 |
| description |
A review of the fault diagnostic techniques based on machine is presented in this paper. As the world is moving towards industry 4.0 standards, the problems of limited computational power and available memory are decreasing day by day. A significant amount of data with a variety of faulty conditions of electrical machines working under different environments can be handled remotely using cloud computation. Moreover, the mathematical models of electrical machines can be utilized for the training of AI algorithms. This is true because the collection of big data is a challenging task for the industry and laboratory because of related limited resources. In this paper, some promising machine learning-based diagnostic techniques are presented in the perspective of their attributes. |
| topic |
fault diagnostics machine learning artificial intelligence pattern recognition neural networks |
| url |
https://www.mdpi.com/2076-3417/11/6/2761 |
| work_keys_str_mv |
AT karolinakudelina trendsandchallengesinintelligentconditionmonitoringofelectricalmachinesusingmachinelearning AT toomasvaimann trendsandchallengesinintelligentconditionmonitoringofelectricalmachinesusingmachinelearning AT bilalasad trendsandchallengesinintelligentconditionmonitoringofelectricalmachinesusingmachinelearning AT antonrassolkin trendsandchallengesinintelligentconditionmonitoringofelectricalmachinesusingmachinelearning AT antskallaste trendsandchallengesinintelligentconditionmonitoringofelectricalmachinesusingmachinelearning AT galinademidova trendsandchallengesinintelligentconditionmonitoringofelectricalmachinesusingmachinelearning |
| _version_ |
1724212424545927168 |