A Most Probable Point-Based Method for Efficient Uncertainty Analysis

Abstract

Uncertainty is inevitable at every stage of life cycle development of a product. To make use of probabilistic information and to make reliable decisions by incorporating decision maker's risk attitude, methods for propagating the effect of uncertainty are therefore needed. When designing complex systems, the efficiency of methods for uncertainty propagation becomes critical. In this paper, a most probable point (MPP)-based uncertainty analysis (MPPUA) method is proposed. The concept of the MPP is utilized to generate the cumulative distribution function (CDF) of a system output by evaluating probability estimates at a serial of limit states across a range of output performance. To improve the efficiency of locating the MPP, a novel MPP search algorithm is presented that employs a new search algorithm and a search strategy. A mathematical example and an engine design problem are used to verify the effectiveness of the proposed method. The proposed MPPUA method will serve as a useful tool for propagating the effect of uncertainty when making decisions under uncertainty.

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