NDLI: Bayesian Approach for Fatigue Life Prediction From Field Data

Content Provider	The American Society of Mechanical Engineers (ASME) Digital Collection
Author	Nam, H. Kim Pattabhiraman, Sriram Lonnie, A. Houck
Copyright Year	2010
Abstract	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.
Sponsorship	International Gas Turbine Institute
Starting Page	695
Ending Page	703
Page Count	9
File Format	PDF
ISBN	9780791844014
DOI	10.1115/GT2010-23780
e-ISBN	9780791838723
Volume Number	Volume 6: Structures and Dynamics, Parts A and B
Conference Proceedings	ASME Turbo Expo 2010: Power for Land, Sea, and Air
Language	English
Publisher Date	2010-06-14
Publisher Place	Glasgow, UK
Access Restriction	Subscribed
Subject Keyword	Fatigue life Uncertainty Volatility Service life (equipment) Probability Risk Maintenance Risk analysis Machinery Design Gas turbines Hardware Manufacturing
Content Type	Text
Resource Type	Article

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