| Popis: |
Due to uncertainty in design, manufacturing and operating processes, the initial prediction of a machine’s useful life is often quite different from that of the actual machine. In this paper, we utilize the Bayesian technique to incorporate the field data with the initial predictions in order to improve the prediction. The field data is interpreted in terms of the probability of having defective hardware, and then the likelihood function is generated from the binomial distribution. Since the predictions incorporate field experience, as time progresses and more data becomes available the probabilistic predictions are continuously updated. This results in a continuous increase of confidence and accuracy of the prediction. The resulting distributions can then be used directly in risk analysis, maintenance scheduling, and financial forecasting by both manufacturers and operators of heavy-duty gas turbines. This presents a quantification of the real time risk for direct comparison with the volatility of the power market. |
