In this study, we propose a method to monitor state transitions for wind turbines based on the online inference on model residuals. Slowly developing faults in wind turbine can, when not detected and fixed on time, cause severe damage and downtime. Early state transition detection attempts to reduce the risk of sever damage and downtime by recognizing changes in the data and adapted predictive models appropriately. As fault detection studies often deal with hard thresholds, the Bayesian analysis comes with the advantage of probability measures. We propose a Bayesian approach to state transition based on hidden variables relevant for the online predictor, namely the time since the last state transition. It is of great interest to see that exact online inference can be performed at every time step, given an underlying predictive model based on a hazard function. Here the hazard function describes how likely it is to undergo a transition given the data since the last state transition. It is imperative that the hyper-parameters are known beforehand in order to perform the inference on the model. We show that Bayesian inference on state transition can be performed for assumed fixed and known hyper-parameters, and we emphasize that the selection of the hyper-parameters can be treated as a machine learning problem and trained given a data set. Comparing fixed to learned hyper-parameters points out the impact they have on the predictive performance.
|Titel||Proceedings of the 2015 Conference on research in adaptive and convergent systems|
|Forlag||Association for Computing Machinery|
|Status||Udgivet - 2015|
|Begivenhed||Conference on research in adaptive and convergent systems - Prague, Tjekkiet|
Varighed: 9. okt. 2015 → 12. okt. 2015
|Konference||Conference on research in adaptive and convergent systems|
|Periode||09/10/2015 → 12/10/2015|