Using Johnson Distribution for Automatic Threshold Setting in Wind Turbine Condition Monitoring System

Kun Saptohartyadi Marhadi, Georgios Alexandros Skrimpas

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Abstract

Setting optimal alarm thresholds in vibration based condition monitoring system is inherently difficult. There are no es- tablished 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 rep- resent the whole operating conditions of a turbine, which re- sults in uncertainty in the parameters of the fitted probabil- ity distribution and the thresholds calculated. In this study Johnson distribution is used to identify shape, location, and scale parameters of distribution that can best fit vibration data. This study shows that using Johnson distribution can elim- inate 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, im- plementations with bootstrap method and Bayesian inference are investigated
Original languageEnglish
Title of host publicationProceedings of the 2014 Annual Conference of the Prognostics and Health Management Society
Number of pages13
Publication date2014
Publication statusPublished - 2014
Event2014 Annual Conference of the Prognostics and Health Management Society - Fort Worth, TX, United States
Duration: 29 Sep 20142 Oct 2014
http://www.phmsociety.org/events/conference/phm/14

Conference

Conference2014 Annual Conference of the Prognostics and Health Management Society
CountryUnited States
CityFort Worth, TX
Period29/09/201402/10/2014
Internet address

Bibliographical note

Kun Marhadi et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 United States License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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