Accession Number : AD1002410

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

Descriptive Note : Conference Paper

Corporate Author : Bruel and Kjar Vibro AS Narum Denmark

Personal Author(s) : Marhadi,Kun S ; Skrimpas,Georgios A

Full Text :

Report Date : 23 Dec 2014

Pagination or Media Count : 13

Abstract : 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 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 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.

Descriptors :   wind turbines , failure mode and effect analysis , probability distributions , statistical analysis

Distribution Statement : APPROVED FOR PUBLIC RELEASE