Application of a sampling-based method for estimation of cumulative failure probability functions of mechanisms

• Published in: Mechanism and machine theory Vol. 155; p. 104050
• Main Authors: Li, Hong-Shuang, Wang, Xiao-Wei, Nan, Hang, Liu, Miao
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.01.2021
• Subjects: Composite limit state; Cumulative failure probability function; Generalized subset simulation; Multiple-response system; Time-dependent reliability analysis; Generalized subset simulation; Cumulative failure probability function; Time-dependent reliability analysis; Composite limit state; Multiple-response system
• ISSN: 0094-114X; 1873-3999
• DOI: 10.1016/j.mechmachtheory.2020.104050

This contribution addresses the estimation of cumulative failure probability functions of mechanisms subject to random inputs and with random process outputs, which is still a challenging topic for mechanism design. The cumulative failure probability function over a whole time interval is time-dependent and estimated by combining the concept of composite limit state function with Generalized Subset Simulation. The failure domain associated with a composite limit state function at a certain time instant is viewed as a response from the time-dependent mechanism system. Then, a multiple-response system is formed by considering all discrete time instants along the motion locus of a mechanism. The failure domains at all time instants are explored taking advantage of the dependency among multiple responses. Such a method allows estimating small failure probabilities with high accuracy and precision while requiring a reduced number of samples with only one simulation run. A mathematical example is used for parametric study, followed by the time-dependent reliability analysis of a slider-crank mechanism and a four-bar function generator mechanism. The results show that the presented method possesses high accuracy and efficiency for time-dependent mechanism reliability analysis.