Artificial Intelligence-Driven Prognostics and Health Management for Centrifugal Pumps: A Comprehensive Review
This comprehensive review explores data-driven methodologies that facilitate the prognostics and health management (PHM) of centrifugal pumps (CPs) while utilizing both vibration and non-vibration sensor data. This review investigates common fault types in CPs, while placing a specific emphasis on a...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-12-01
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| Series: | Actuators |
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| Online Access: | https://www.mdpi.com/2076-0825/13/12/514 |
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| author | Salman Khalid Soo-Ho Jo Syed Yaseen Shah Joon Ha Jung Heung Soo Kim |
| author_facet | Salman Khalid Soo-Ho Jo Syed Yaseen Shah Joon Ha Jung Heung Soo Kim |
| author_sort | Salman Khalid |
| collection | DOAJ |
| description | This comprehensive review explores data-driven methodologies that facilitate the prognostics and health management (PHM) of centrifugal pumps (CPs) while utilizing both vibration and non-vibration sensor data. This review investigates common fault types in CPs, while placing a specific emphasis on artificial intelligence (AI) approaches, including machine learning (ML) and deep learning (DL) techniques, for fault diagnosis and prognosis. A key innovation of this review is its in-depth analysis of cutting-edge methods, such as adaptive thresholding, hybrid models, and advanced neural network architectures, aimed at accurately predicting the remaining useful life (RUL) of CPs under varying operational conditions. This review also addresses the limitations and challenges of the current AI-driven methodologies, offering insights into potential solutions. By synthesizing these methodologies and presenting practical applications through case studies, this review provides a forward-looking perspective to empower industry professionals and researchers with effective strategies to ensure the reliability and efficiency of centrifugal pumps. These findings could contribute to optimizing industrial processes and advancing health management strategies for critical components. |
| format | Article |
| id | doaj-art-cd1d3eb2aea84a878aeda40cfdb6f2f4 |
| institution | Kabale University |
| issn | 2076-0825 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Actuators |
| spelling | doaj-art-cd1d3eb2aea84a878aeda40cfdb6f2f42024-12-27T14:02:08ZengMDPI AGActuators2076-08252024-12-01131251410.3390/act13120514Artificial Intelligence-Driven Prognostics and Health Management for Centrifugal Pumps: A Comprehensive ReviewSalman Khalid0Soo-Ho Jo1Syed Yaseen Shah2Joon Ha Jung3Heung Soo Kim4Department of Mechanical, Robotics, and Energy Engineering, Dongguk University, 30 Pil-dong 1 Gil, Jung-gu, Seoul 04620, Republic of KoreaDepartment of Mechanical, Robotics, and Energy Engineering, Dongguk University, 30 Pil-dong 1 Gil, Jung-gu, Seoul 04620, Republic of KoreaDepartment of Industrial Engineering, Ajou University, Suwon 16499, Republic of KoreaDepartment of Industrial Engineering, Ajou University, Suwon 16499, Republic of KoreaDepartment of Mechanical, Robotics, and Energy Engineering, Dongguk University, 30 Pil-dong 1 Gil, Jung-gu, Seoul 04620, Republic of KoreaThis comprehensive review explores data-driven methodologies that facilitate the prognostics and health management (PHM) of centrifugal pumps (CPs) while utilizing both vibration and non-vibration sensor data. This review investigates common fault types in CPs, while placing a specific emphasis on artificial intelligence (AI) approaches, including machine learning (ML) and deep learning (DL) techniques, for fault diagnosis and prognosis. A key innovation of this review is its in-depth analysis of cutting-edge methods, such as adaptive thresholding, hybrid models, and advanced neural network architectures, aimed at accurately predicting the remaining useful life (RUL) of CPs under varying operational conditions. This review also addresses the limitations and challenges of the current AI-driven methodologies, offering insights into potential solutions. By synthesizing these methodologies and presenting practical applications through case studies, this review provides a forward-looking perspective to empower industry professionals and researchers with effective strategies to ensure the reliability and efficiency of centrifugal pumps. These findings could contribute to optimizing industrial processes and advancing health management strategies for critical components.https://www.mdpi.com/2076-0825/13/12/514centrifugal pumps (CPs)fault diagnosisprognosticsartificial intelligencemachine learningdeep learning |
| spellingShingle | Salman Khalid Soo-Ho Jo Syed Yaseen Shah Joon Ha Jung Heung Soo Kim Artificial Intelligence-Driven Prognostics and Health Management for Centrifugal Pumps: A Comprehensive Review Actuators centrifugal pumps (CPs) fault diagnosis prognostics artificial intelligence machine learning deep learning |
| title | Artificial Intelligence-Driven Prognostics and Health Management for Centrifugal Pumps: A Comprehensive Review |
| title_full | Artificial Intelligence-Driven Prognostics and Health Management for Centrifugal Pumps: A Comprehensive Review |
| title_fullStr | Artificial Intelligence-Driven Prognostics and Health Management for Centrifugal Pumps: A Comprehensive Review |
| title_full_unstemmed | Artificial Intelligence-Driven Prognostics and Health Management for Centrifugal Pumps: A Comprehensive Review |
| title_short | Artificial Intelligence-Driven Prognostics and Health Management for Centrifugal Pumps: A Comprehensive Review |
| title_sort | artificial intelligence driven prognostics and health management for centrifugal pumps a comprehensive review |
| topic | centrifugal pumps (CPs) fault diagnosis prognostics artificial intelligence machine learning deep learning |
| url | https://www.mdpi.com/2076-0825/13/12/514 |
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