A Review on Data-Driven Condition Monitoring of Industrial Equipment
This paper presents an up-to-date review of data-driven condition monitoring of industrial equipment with the focus on three commonly used equipment: motors, pumps, and bearings. Firstly, the general framework of data-driven condition monitoring is discussed and the utilized mathematical and statist...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-12-01
|
| Series: | Algorithms |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1999-4893/16/1/9 |
| _version_ | 1797447147909545984 |
|---|---|
| author | Ruosen Qi Jie Zhang Katy Spencer |
| author_facet | Ruosen Qi Jie Zhang Katy Spencer |
| author_sort | Ruosen Qi |
| collection | DOAJ |
| description | This paper presents an up-to-date review of data-driven condition monitoring of industrial equipment with the focus on three commonly used equipment: motors, pumps, and bearings. Firstly, the general framework of data-driven condition monitoring is discussed and the utilized mathematical and statistical approaches are introduced. The utilized techniques in recent literature are discussed. Then, fault detection, diagnosis, and prognosis on the three types of equipment are highlighted using a variety of popular shallow and deep learning models. Applications of these techniques in recent literature are summarized. Finally, some potential future challenges and research directions are presented. |
| first_indexed | 2024-03-09T13:50:39Z |
| format | Article |
| id | doaj.art-f93f563c6c6243a598d09f01d780d63b |
| institution | Directory Open Access Journal |
| issn | 1999-4893 |
| language | English |
| last_indexed | 2024-03-09T13:50:39Z |
| publishDate | 2022-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Algorithms |
| spelling | doaj.art-f93f563c6c6243a598d09f01d780d63b2023-11-30T20:51:08ZengMDPI AGAlgorithms1999-48932022-12-01161910.3390/a16010009A Review on Data-Driven Condition Monitoring of Industrial EquipmentRuosen Qi0Jie Zhang1Katy Spencer2School of Engineering, Merz Court, Newcastle University, Newcastle upon Tyne NE1 7RU, UKSchool of Engineering, Merz Court, Newcastle University, Newcastle upon Tyne NE1 7RU, UKSellafield Ltd., Sellafield, Seascale, Cumbria CA20 1PG, UKThis paper presents an up-to-date review of data-driven condition monitoring of industrial equipment with the focus on three commonly used equipment: motors, pumps, and bearings. Firstly, the general framework of data-driven condition monitoring is discussed and the utilized mathematical and statistical approaches are introduced. The utilized techniques in recent literature are discussed. Then, fault detection, diagnosis, and prognosis on the three types of equipment are highlighted using a variety of popular shallow and deep learning models. Applications of these techniques in recent literature are summarized. Finally, some potential future challenges and research directions are presented.https://www.mdpi.com/1999-4893/16/1/9data-drivencondition monitoringmotorpumpbearingfault detection |
| spellingShingle | Ruosen Qi Jie Zhang Katy Spencer A Review on Data-Driven Condition Monitoring of Industrial Equipment Algorithms data-driven condition monitoring motor pump bearing fault detection |
| title | A Review on Data-Driven Condition Monitoring of Industrial Equipment |
| title_full | A Review on Data-Driven Condition Monitoring of Industrial Equipment |
| title_fullStr | A Review on Data-Driven Condition Monitoring of Industrial Equipment |
| title_full_unstemmed | A Review on Data-Driven Condition Monitoring of Industrial Equipment |
| title_short | A Review on Data-Driven Condition Monitoring of Industrial Equipment |
| title_sort | review on data driven condition monitoring of industrial equipment |
| topic | data-driven condition monitoring motor pump bearing fault detection |
| url | https://www.mdpi.com/1999-4893/16/1/9 |
| work_keys_str_mv | AT ruosenqi areviewondatadrivenconditionmonitoringofindustrialequipment AT jiezhang areviewondatadrivenconditionmonitoringofindustrialequipment AT katyspencer areviewondatadrivenconditionmonitoringofindustrialequipment AT ruosenqi reviewondatadrivenconditionmonitoringofindustrialequipment AT jiezhang reviewondatadrivenconditionmonitoringofindustrialequipment AT katyspencer reviewondatadrivenconditionmonitoringofindustrialequipment |