High-dimensional control charts with application to surveillance of grease damage in bearings of wind turbines
High-dimensional data, characterized by having more attributes or variables than observations, presents unique challenges in industrial operations surveillance. Traditional multivariate control charts, like Hotelling’s [Formula: see text] chart, perform adequately with lower-dimensional data. Howeve...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2024-12-01
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| Series: | Production and Manufacturing Research: An Open Access Journal |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/21693277.2024.2377739 |
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| author | Tahir Mahmood Fuhad Ahmed Muhammad Riaz Nasir Abbas |
| author_facet | Tahir Mahmood Fuhad Ahmed Muhammad Riaz Nasir Abbas |
| author_sort | Tahir Mahmood |
| collection | DOAJ |
| description | High-dimensional data, characterized by having more attributes or variables than observations, presents unique challenges in industrial operations surveillance. Traditional multivariate control charts, like Hotelling’s [Formula: see text] chart, perform adequately with lower-dimensional data. However, they often fail to detect variations in process means as data dimensionality increases. This research proposes new control charts designed to enhance the detection of mean variations in both high and low-dimensional data. Specifically, Srivastava-Du (SD), Bai-Saranadasa (BS) and Dempster (DS) statistic-based charts are introduced, and their effectiveness is evaluated through simulations and real-life data applications. The performance of these charts is compared under various multivariate normal and non-normal distributions. Results indicate that DS and BS charts perform similarly, with the DS chart outperforming in low-dimensional normal distribution. Conversely, the SD chart outperformed in high-dimensional non-normal distributions. Additionally, the practical application of these proposed charts is illustrated through the monitoring of grease degradation in wind turbine bearings. |
| format | Article |
| id | doaj-art-b31c948b867048e7bcebb94418fe47e5 |
| institution | OA Journals |
| issn | 2169-3277 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Production and Manufacturing Research: An Open Access Journal |
| spelling | doaj-art-b31c948b867048e7bcebb94418fe47e52025-08-20T02:34:04ZengTaylor & Francis GroupProduction and Manufacturing Research: An Open Access Journal2169-32772024-12-0112110.1080/21693277.2024.2377739High-dimensional control charts with application to surveillance of grease damage in bearings of wind turbinesTahir Mahmood0Fuhad Ahmed1Muhammad Riaz2Nasir Abbas3School of Computing, Engineering and Physical Sciences, University of the West of Scotland, Paisley, United KingdomDepartment of Mathematics, King Fahd University of Petroleum and Minerals, Dhahran, Saudi ArabiaDepartment of Mathematics, King Fahd University of Petroleum and Minerals, Dhahran, Saudi ArabiaDepartment of Mathematics, King Fahd University of Petroleum and Minerals, Dhahran, Saudi ArabiaHigh-dimensional data, characterized by having more attributes or variables than observations, presents unique challenges in industrial operations surveillance. Traditional multivariate control charts, like Hotelling’s [Formula: see text] chart, perform adequately with lower-dimensional data. However, they often fail to detect variations in process means as data dimensionality increases. This research proposes new control charts designed to enhance the detection of mean variations in both high and low-dimensional data. Specifically, Srivastava-Du (SD), Bai-Saranadasa (BS) and Dempster (DS) statistic-based charts are introduced, and their effectiveness is evaluated through simulations and real-life data applications. The performance of these charts is compared under various multivariate normal and non-normal distributions. Results indicate that DS and BS charts perform similarly, with the DS chart outperforming in low-dimensional normal distribution. Conversely, the SD chart outperformed in high-dimensional non-normal distributions. Additionally, the practical application of these proposed charts is illustrated through the monitoring of grease degradation in wind turbine bearings.https://www.tandfonline.com/doi/10.1080/21693277.2024.2377739Control charthigh-dimensional datalow-dimensional datamemoryless chartstatistical process control |
| spellingShingle | Tahir Mahmood Fuhad Ahmed Muhammad Riaz Nasir Abbas High-dimensional control charts with application to surveillance of grease damage in bearings of wind turbines Production and Manufacturing Research: An Open Access Journal Control chart high-dimensional data low-dimensional data memoryless chart statistical process control |
| title | High-dimensional control charts with application to surveillance of grease damage in bearings of wind turbines |
| title_full | High-dimensional control charts with application to surveillance of grease damage in bearings of wind turbines |
| title_fullStr | High-dimensional control charts with application to surveillance of grease damage in bearings of wind turbines |
| title_full_unstemmed | High-dimensional control charts with application to surveillance of grease damage in bearings of wind turbines |
| title_short | High-dimensional control charts with application to surveillance of grease damage in bearings of wind turbines |
| title_sort | high dimensional control charts with application to surveillance of grease damage in bearings of wind turbines |
| topic | Control chart high-dimensional data low-dimensional data memoryless chart statistical process control |
| url | https://www.tandfonline.com/doi/10.1080/21693277.2024.2377739 |
| work_keys_str_mv | AT tahirmahmood highdimensionalcontrolchartswithapplicationtosurveillanceofgreasedamageinbearingsofwindturbines AT fuhadahmed highdimensionalcontrolchartswithapplicationtosurveillanceofgreasedamageinbearingsofwindturbines AT muhammadriaz highdimensionalcontrolchartswithapplicationtosurveillanceofgreasedamageinbearingsofwindturbines AT nasirabbas highdimensionalcontrolchartswithapplicationtosurveillanceofgreasedamageinbearingsofwindturbines |