Automatic Threshold Setting and Its Uncertainty Quantification in Wind Turbine Condition Monitoring System
Setting optimal alarm thresholds in vibration based condition monitoring system is inherently difficult. There are no established thresholds for many vibration based measurements. Most of the time, the thresholds are set based on statistics of the collected data available. Often times the underlying...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
The Prognostics and Health Management Society
2015-12-01
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| Series: | International Journal of Prognostics and Health Management |
| Subjects: | |
| Online Access: | https://papers.phmsociety.org/index.php/ijphm/article/view/2291 |
| _version_ | 1819148878762475520 |
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| author | Kun S. Marhadi Georgios Alexandros Skrimpas |
| author_facet | Kun S. Marhadi Georgios Alexandros Skrimpas |
| author_sort | Kun S. Marhadi |
| collection | DOAJ |
| description | Setting optimal alarm thresholds in vibration based condition monitoring system is inherently difficult. There are no established thresholds for many vibration based measurements. Most of the time, the thresholds are set based on statistics of the collected data available. Often times the underlying probability distribution that describes the data is not known. Choosing an incorrect distribution to describe the data and then setting up thresholds based on the chosen distribution could result in sub-optimal thresholds. Moreover, in wind turbine applications the collected data available may not represent the whole operating conditions of a turbine, which results in uncertainty in the parameters of the fitted probability distribution and the thresholds calculated. In this study, Johnson, Normal, and Weibull distributions are investigated; which distribution can best fit vibration data collected from a period of time. False alarm rate resulted from using threshold determined from each distribution is used as a measure to determine which distribution is the most appropriate. This study shows that using Johnson distribution can eliminate testing or fitting various distributions to the data, and have more direct approach to obtain optimal thresholds. To quantify uncertainty in the thresholds due to limited data, implementations with bootstrap method and Bayesian inference are investigated. |
| first_indexed | 2024-12-22T13:52:43Z |
| format | Article |
| id | doaj.art-18e8c1fa5b2a42c3a67b79a25498683c |
| institution | Directory Open Access Journal |
| issn | 2153-2648 2153-2648 |
| language | English |
| last_indexed | 2024-12-22T13:52:43Z |
| publishDate | 2015-12-01 |
| publisher | The Prognostics and Health Management Society |
| record_format | Article |
| series | International Journal of Prognostics and Health Management |
| spelling | doaj.art-18e8c1fa5b2a42c3a67b79a25498683c2022-12-21T18:23:38ZengThe Prognostics and Health Management SocietyInternational Journal of Prognostics and Health Management2153-26482153-26482015-12-0164doi:10.36001/ijphm.2015.v6i4.2291Automatic Threshold Setting and Its Uncertainty Quantification in Wind Turbine Condition Monitoring SystemKun S. Marhadi0Georgios Alexandros Skrimpas1Brüel & Kjær Vibro A/S, 2850 Nærum, DenmarkBrüel & Kjær Vibro A/S, 2850 Nærum, DenmarkSetting optimal alarm thresholds in vibration based condition monitoring system is inherently difficult. There are no established thresholds for many vibration based measurements. Most of the time, the thresholds are set based on statistics of the collected data available. Often times the underlying probability distribution that describes the data is not known. Choosing an incorrect distribution to describe the data and then setting up thresholds based on the chosen distribution could result in sub-optimal thresholds. Moreover, in wind turbine applications the collected data available may not represent the whole operating conditions of a turbine, which results in uncertainty in the parameters of the fitted probability distribution and the thresholds calculated. In this study, Johnson, Normal, and Weibull distributions are investigated; which distribution can best fit vibration data collected from a period of time. False alarm rate resulted from using threshold determined from each distribution is used as a measure to determine which distribution is the most appropriate. This study shows that using Johnson distribution can eliminate testing or fitting various distributions to the data, and have more direct approach to obtain optimal thresholds. To quantify uncertainty in the thresholds due to limited data, implementations with bootstrap method and Bayesian inference are investigated.https://papers.phmsociety.org/index.php/ijphm/article/view/2291uncertainty quantificationjohnson distributionalarm thresholdwind turbine condition monitoring |
| spellingShingle | Kun S. Marhadi Georgios Alexandros Skrimpas Automatic Threshold Setting and Its Uncertainty Quantification in Wind Turbine Condition Monitoring System International Journal of Prognostics and Health Management uncertainty quantification johnson distribution alarm threshold wind turbine condition monitoring |
| title | Automatic Threshold Setting and Its Uncertainty Quantification in Wind Turbine Condition Monitoring System |
| title_full | Automatic Threshold Setting and Its Uncertainty Quantification in Wind Turbine Condition Monitoring System |
| title_fullStr | Automatic Threshold Setting and Its Uncertainty Quantification in Wind Turbine Condition Monitoring System |
| title_full_unstemmed | Automatic Threshold Setting and Its Uncertainty Quantification in Wind Turbine Condition Monitoring System |
| title_short | Automatic Threshold Setting and Its Uncertainty Quantification in Wind Turbine Condition Monitoring System |
| title_sort | automatic threshold setting and its uncertainty quantification in wind turbine condition monitoring system |
| topic | uncertainty quantification johnson distribution alarm threshold wind turbine condition monitoring |
| url | https://papers.phmsociety.org/index.php/ijphm/article/view/2291 |
| work_keys_str_mv | AT kunsmarhadi automaticthresholdsettinganditsuncertaintyquantificationinwindturbineconditionmonitoringsystem AT georgiosalexandrosskrimpas automaticthresholdsettinganditsuncertaintyquantificationinwindturbineconditionmonitoringsystem |