INVERSE RELIABILITY ANALYSIS OF A LINEAR DYNAMICAL SYSTEM UNDER UNCERTAIN LOAD

Abstract eng:
The reliability of a linear dynamical system driven by a partially known Gaussian load process, specified only through its total average energy, is studied. A simple dynamic parallel or series system reliability configuration analysis is investigated for the failure using the crossing theory of stochastic processes. A trade-off curve between the maximum mean crossing rate of the parallel or series network and the disorder in the input, quantitatively measured through its entropy rate, is generated. Mathematically, the generation of the trade-off curve, in the sense of Pareto, leads to a highly nonlinear multi-criteria non-convex optimization problem with conflicting objectives. A recently developed Pareto optimization technique in conjunction with genetic algorithm (GA) has been successfully used to solve the optimization problem. The optimally disordered inputs which simulate the worst performance of the system of a spring-supported coupled rod assembly, has been studied as a numerical illustration of the mathematical formulation.

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