Abstract
Sensitivity analysis measures how changes in system inputs affect outputs. Previously, a large amount of sensitivity analysis research was relevant to the precise probability that is regarded as an ideal condition of engineering. Due to insufficient test samples and the low accuracy of test data, system reliability with hybrid uncertainty is difficult to be described as a precise value. As a profusion of highly integrated electromechanical equipment is applied in modern life, it is impossible to apply sufficient resources to eliminate the stochastic property of every component, which necessitates the identification of highly sensitive components to efficiently reduce imprecision. Hence, based on the theory of imprecise probability, imprecise sensitivity analysis has become a popular research topic in the last decade. In this paper, a method for uncertain system reliability and imprecise sensitivity analysis is proposed based on a Bayesian network, a probability box and the pinching method. The feasibility and accuracy of the combined method are fully verified through the evaluation and analysis of a numerical example and a case study of an electromechanical system, and the highly sensitive components that heavily influence the imprecision of system outputs are accurately identified.
Highlights
With the improvement of industrial techniques and requirements for productivity, plenty of high-integrity and complex-structured electromechanical systems (EMSs) have been widely employed and utilized
After matrix D is obtained from the Bayesian network (BN) model, referring to Table 1 and Table 2, the conditional probability tables (CPTs) of the child nodes in the directed acyclic graph (DAG) can be listed in the form of a column vector to participate in the forward inference
Sensitivity analysis has prominent application in the risk and reliability analysis field to explore how changes in the inputs of the component affect the outputs of the system
Summary
With the improvement of industrial techniques and requirements for productivity, plenty of high-integrity and complex-structured electromechanical systems (EMSs) have been widely employed and utilized. Feng et al [17] evaluated sensitivity by utilizing the p-box as the quantification metric and a survival signature as the reliability modeling method. In response to the necessity of ISA studies of uncertain system reliability, this paper proposes a method to establish a reliability model with a BN, using the pinching method [27] to complete sensitivity analysis with the imprecision characterized by the p-box. The high-sensitivity components and subsystems can be identified by ranking the indices This approach is introduced as a new solution that implements the Bayesian network and pinching method for reliability and sensitivity analysis.
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