A Review of Artificial Intelligence Methods for Condition Monitoring and Fault Diagnosis of Rolling Element Bearings for Induction Motor
The fault detection and diagnosis (FDD) along with condition monitoring (CM) and of rotating machinery (RM) have critical importance for early diagnosis to prevent severe damage of infrastructure in industrial environments. Importantly, valuable industrial equipment needs continuous monitoring to en...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Shock and Vibration |
| Online Access: | http://dx.doi.org/10.1155/2020/8843759 |
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| _version_ | 1832552580887085056 |
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| author | Omar AlShorman Muhammad Irfan Nordin Saad D. Zhen Noman Haider Adam Glowacz Ahmad AlShorman |
| author_facet | Omar AlShorman Muhammad Irfan Nordin Saad D. Zhen Noman Haider Adam Glowacz Ahmad AlShorman |
| author_sort | Omar AlShorman |
| collection | DOAJ |
| description | The fault detection and diagnosis (FDD) along with condition monitoring (CM) and of rotating machinery (RM) have critical importance for early diagnosis to prevent severe damage of infrastructure in industrial environments. Importantly, valuable industrial equipment needs continuous monitoring to enhance the safety, reliability, and availability and to decrease the cost of maintenance of modern industrial systems and applications. However, induction motor (IM) has been extensively used in several industrial processes because it is cheap, reliable, and robust. Rolling bearings are considered to be the main component of IM. Undoubtedly, any failure of this basic component can lead to a serious breakdown of IM and for whole industrial system. Thus, many current methods based on different techniques are employed as a fault prognosis and diagnosis of rolling elements bearing of IM. Moreover, these techniques include signal/image processing, intelligent diagnostics, data fusion, data mining, and expert systems for time and frequency as well as time-frequency domains. Artificial intelligence (AI) techniques have proven their significance in every field of digital technology. Industrial machines, automation, and processes are the net frontiers of AI adaptation. There are quite developed literatures that have been approaching the issues using signals and data processing techniques. However, the key contribution of this work is to present an extensive review of CM and FDD of the IM, especially for rolling elements bearings, based on artificial intelligent (AI) methods. This study highlights the advantages and performance limitations of each method. Finally, challenges and future trends are also highlighted. |
| format | Article |
| id | doaj-art-999ca25adba44fbca0b93ed49fd4c2d6 |
| institution | Kabale University |
| issn | 1070-9622 1875-9203 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Shock and Vibration |
| spelling | doaj-art-999ca25adba44fbca0b93ed49fd4c2d62025-02-03T05:58:24ZengWileyShock and Vibration1070-96221875-92032020-01-01202010.1155/2020/88437598843759A Review of Artificial Intelligence Methods for Condition Monitoring and Fault Diagnosis of Rolling Element Bearings for Induction MotorOmar AlShorman0Muhammad Irfan1Nordin Saad2D. Zhen3Noman Haider4Adam Glowacz5Ahmad AlShorman6Faculty of Engineering and AlShrouk Trading Company, Najran University, Najran, Saudi ArabiaCollege of Engineering, Electrical Engineering Department, Najran University, Najran, Saudi ArabiaDepartment of Electrical and Electronics Engineering, Universiti Teknologi PETRONAS, 32610 Seri Iskandar, Perak, MalaysiaHebei University of Technology, Tianjin, ChinaCollege of Engineering and Science, Victoria University, Sydney, AustraliaDepartment of Automatic Control and Robotics, AGH University of Science and Technology, 30-059 Kraków, PolandMechanical Engineering Department, Jordan University of Science and Technology, Irbid, JordanThe fault detection and diagnosis (FDD) along with condition monitoring (CM) and of rotating machinery (RM) have critical importance for early diagnosis to prevent severe damage of infrastructure in industrial environments. Importantly, valuable industrial equipment needs continuous monitoring to enhance the safety, reliability, and availability and to decrease the cost of maintenance of modern industrial systems and applications. However, induction motor (IM) has been extensively used in several industrial processes because it is cheap, reliable, and robust. Rolling bearings are considered to be the main component of IM. Undoubtedly, any failure of this basic component can lead to a serious breakdown of IM and for whole industrial system. Thus, many current methods based on different techniques are employed as a fault prognosis and diagnosis of rolling elements bearing of IM. Moreover, these techniques include signal/image processing, intelligent diagnostics, data fusion, data mining, and expert systems for time and frequency as well as time-frequency domains. Artificial intelligence (AI) techniques have proven their significance in every field of digital technology. Industrial machines, automation, and processes are the net frontiers of AI adaptation. There are quite developed literatures that have been approaching the issues using signals and data processing techniques. However, the key contribution of this work is to present an extensive review of CM and FDD of the IM, especially for rolling elements bearings, based on artificial intelligent (AI) methods. This study highlights the advantages and performance limitations of each method. Finally, challenges and future trends are also highlighted.http://dx.doi.org/10.1155/2020/8843759 |
| spellingShingle | Omar AlShorman Muhammad Irfan Nordin Saad D. Zhen Noman Haider Adam Glowacz Ahmad AlShorman A Review of Artificial Intelligence Methods for Condition Monitoring and Fault Diagnosis of Rolling Element Bearings for Induction Motor Shock and Vibration |
| title | A Review of Artificial Intelligence Methods for Condition Monitoring and Fault Diagnosis of Rolling Element Bearings for Induction Motor |
| title_full | A Review of Artificial Intelligence Methods for Condition Monitoring and Fault Diagnosis of Rolling Element Bearings for Induction Motor |
| title_fullStr | A Review of Artificial Intelligence Methods for Condition Monitoring and Fault Diagnosis of Rolling Element Bearings for Induction Motor |
| title_full_unstemmed | A Review of Artificial Intelligence Methods for Condition Monitoring and Fault Diagnosis of Rolling Element Bearings for Induction Motor |
| title_short | A Review of Artificial Intelligence Methods for Condition Monitoring and Fault Diagnosis of Rolling Element Bearings for Induction Motor |
| title_sort | review of artificial intelligence methods for condition monitoring and fault diagnosis of rolling element bearings for induction motor |
| url | http://dx.doi.org/10.1155/2020/8843759 |
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