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 language | English |
|---|---|
| Title of host publication | Proceedings of the 2014 Annual Conference of the Prognostics and Health Management Society |
| Number of pages | 13 |
| Publication date | 2014 |
| Publication status | Published - 2014 |
| Event | 2014 Annual Conference of the Prognostics and Health Management Society - Fort Worth, TX, United States Duration: 29 Sept 2014 → 2 Oct 2014 http://www.phmsociety.org/events/conference/phm/14 |
Conference
| Conference | 2014 Annual Conference of the Prognostics and Health Management Society |
|---|---|
| Country/Territory | United States |
| City | Fort Worth, TX |
| Period | 29/09/2014 → 02/10/2014 |
| Internet address |