Vibration Sensors for Detecting Critical Events: A Case Study in Ferrosilicon Production
The mining and metal processing industries are undergoing a transformation through digitization, with sensors and data analysis playing a crucial role in modernization and increased efficiency. Vibration sensors are particularly important in monitoring production infrastructure in metal processing p...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10409512/ |
| _version_ | 1797339243706580992 |
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| author | Maryna Waszak Terje Moen Anders H. Hansen Gregory Bouquet Antoine Pultier Xiang Ma Dumitru Roman |
| author_facet | Maryna Waszak Terje Moen Anders H. Hansen Gregory Bouquet Antoine Pultier Xiang Ma Dumitru Roman |
| author_sort | Maryna Waszak |
| collection | DOAJ |
| description | The mining and metal processing industries are undergoing a transformation through digitization, with sensors and data analysis playing a crucial role in modernization and increased efficiency. Vibration sensors are particularly important in monitoring production infrastructure in metal processing plants. This paper presents the installation of vibration sensors in an actual industrial environment and the results of spectral vibration data analysis. The study demonstrates that vibration sensors can be installed in challenging environments such as metal processing plants and that analyzing vibration patterns can provide valuable insights into predicting machine failures and different machine states. By utilizing dimensionality reduction and dominant frequency observation, we analyzed vibration data and identified patterns that are indicative of potential machine states and critical events that reduce production throughput. This information can be used to improve maintenance, minimize downtime, and ultimately enhance the production process’s overall efficiency. This study highlights the importance of digitization and data analysis in the mining and metal processing industries, particularly the capability not only to predict critical events before they impact production throughput and take action accordingly but also to identify machine states for legacy equipment and be part of retrofitting strategies. |
| first_indexed | 2024-03-08T09:43:08Z |
| format | Article |
| id | doaj.art-d1f65e47377f4ec399457e6af488f9aa |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-08T09:43:08Z |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-d1f65e47377f4ec399457e6af488f9aa2024-01-30T00:03:53ZengIEEEIEEE Access2169-35362024-01-0112124651247710.1109/ACCESS.2024.335606710409512Vibration Sensors for Detecting Critical Events: A Case Study in Ferrosilicon ProductionMaryna Waszak0https://orcid.org/0000-0001-7374-210XTerje Moen1Anders H. Hansen2https://orcid.org/0000-0001-9917-4382Gregory Bouquet3https://orcid.org/0000-0002-3175-3502Antoine Pultier4https://orcid.org/0009-0009-3247-4108Xiang Ma5https://orcid.org/0000-0001-6465-0254Dumitru Roman6https://orcid.org/0000-0001-6397-3705SINTEF, Oslo, NorwaySINTEF, Oslo, NorwaySINTEF, Oslo, NorwaySINTEF, Oslo, NorwaySINTEF, Oslo, NorwaySINTEF, Oslo, NorwaySINTEF, Oslo, NorwayThe mining and metal processing industries are undergoing a transformation through digitization, with sensors and data analysis playing a crucial role in modernization and increased efficiency. Vibration sensors are particularly important in monitoring production infrastructure in metal processing plants. This paper presents the installation of vibration sensors in an actual industrial environment and the results of spectral vibration data analysis. The study demonstrates that vibration sensors can be installed in challenging environments such as metal processing plants and that analyzing vibration patterns can provide valuable insights into predicting machine failures and different machine states. By utilizing dimensionality reduction and dominant frequency observation, we analyzed vibration data and identified patterns that are indicative of potential machine states and critical events that reduce production throughput. This information can be used to improve maintenance, minimize downtime, and ultimately enhance the production process’s overall efficiency. This study highlights the importance of digitization and data analysis in the mining and metal processing industries, particularly the capability not only to predict critical events before they impact production throughput and take action accordingly but also to identify machine states for legacy equipment and be part of retrofitting strategies.https://ieeexplore.ieee.org/document/10409512/Data managementdata miningclassificationferrosilicon productionsensor datatime series |
| spellingShingle | Maryna Waszak Terje Moen Anders H. Hansen Gregory Bouquet Antoine Pultier Xiang Ma Dumitru Roman Vibration Sensors for Detecting Critical Events: A Case Study in Ferrosilicon Production IEEE Access Data management data mining classification ferrosilicon production sensor data time series |
| title | Vibration Sensors for Detecting Critical Events: A Case Study in Ferrosilicon Production |
| title_full | Vibration Sensors for Detecting Critical Events: A Case Study in Ferrosilicon Production |
| title_fullStr | Vibration Sensors for Detecting Critical Events: A Case Study in Ferrosilicon Production |
| title_full_unstemmed | Vibration Sensors for Detecting Critical Events: A Case Study in Ferrosilicon Production |
| title_short | Vibration Sensors for Detecting Critical Events: A Case Study in Ferrosilicon Production |
| title_sort | vibration sensors for detecting critical events a case study in ferrosilicon production |
| topic | Data management data mining classification ferrosilicon production sensor data time series |
| url | https://ieeexplore.ieee.org/document/10409512/ |
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