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Optimal loading strategy for multi-state systems: Cumulative performance perspective.

Authors :
Jiang, Tao
Liu, Yu
Zheng, Yi-Xuan
Source :
Applied Mathematical Modelling. Oct2019, Vol. 74, p199-216. 18p.
Publication Year :
2019

Abstract

• The cumulative performance of multi-state systems is evaluated. • The mission success probability is derived for infinite and finite time horizons. • Load optimization models are formulated from a cumulative performance perspective. • The computational burden is alleviated by an approximation algorithm. Multi-state is a characteristic of advanced manufacturing systems and complicated engineering systems. Multi-state systems (MSSs) have gained considerable popularity in the last few decades due to their reliability. In this study, the load optimization problem for MSSs is investigated from the perspective of cumulative performance. The cumulative performance of MSSs and the corresponding mission success probability (MSP) are formulated for both infinite and finite time horizons. The distribution of the cumulative performance of a system at failure or a particular time is evaluated using a set of multiple integrals. Correspondingly, two load optimization models are formulated to identify the optimal loading strategy for each state of an MSS to achieve the maximum MSP. As an example, a set of comparative studies are performed to demonstrate the advantages of the proposed method. The results show that (1) the proposed method can effectively evaluate the MSP from a cumulative performance perspective in a computationally efficient manner, and (2) the optimal loading strategy of an MSS can be determined by the proposed method, while varying with respect to the set amount of work to be completed and the maximum allowable mission time. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
0307904X
Volume :
74
Database :
Academic Search Index
Journal :
Applied Mathematical Modelling
Publication Type :
Academic Journal
Accession number :
136983472
Full Text :
https://doi.org/10.1016/j.apm.2019.04.043