arrow
Return

Product resilience evaluation: A Bayesian network modeling based method

delete2024-10-01
delete1
PRE
AI
R
Ruihan Zhou
郭
郭鑫 (Xin Guo) *
J
Junli Hou
M
Miao Cai
H
Honggang Gou
W
Wu Zhao
施
施建成 (Jiancheng Shi)
DOI:10.1016/j.aei.2024.102679delete
deleteOriginal
deleteOriginal request for help
deleteShare
deleteSave
Abstract

Abstract

En 中文
Future products will have a higher degree of intelligence, and more complex and changing use environments, so resilience has been introduced into the design and operation of products as a concept that helps them cope with high-impact shocks and the damage they cause. With the work on product resilience, there is a need to find methods that can objectively reflect product resilience. Existing methods that can be used to evaluate product resilience are usually based on performance curves, but it is difficult to reflect the multifactorial and stochastic character of product resilience process. Therefore, this paper firstly reviews the resilience related research, and analyzes the factors affecting product resilience and their interrelationships layer by layer to construct the index system of product resilience. Then, a Bayesian network model is established based on the results of the above analysis, and the corresponding calculation method is proposed. Finally, the proposed method is illustrated with a case study of a complex terrain drilling rig and its improvement program. After discussion, the proposed method can be applied to the quantitative evaluation of product resilience, and the possible design direction of resilient products can be suggested using this method.
Keywords:
Product resilience
Resilience evaluation
Bayesian network model

Journal

Advanced Engineering Informatics cover
Advanced Engineering Informatics
IF:
9.9
Papers:
4.4K
Citations:
1.7W

Organization

S
sichuan university
Scholars:
12.1W
Papers: 7.8W
Citations: 100
Cited Papers

Cited Papers

Predicting tipping points of dynamical systems during a period-doubling route to chaos
err2018-07-18
err0
PREAI
errFahimeh Nazarimehr; Sajad Jafari; Seyed Mohammad Reza Hashemi Golpayegani; Matjaž Perc; Julien Clinton Sprott
errShare
errSave
Supply-side risk modelling using Bayesian network approach
err2022-02-16
err5
PREAI
errSharma, Satyendra Kumar; Routroy, Srikanta; Chanda, Udayan
errShare
errSave
errShare
errSave
Proactive and visual approach for product maintainability design
err2023-01-01
err3
PREAI
errGeng, Jie; Gao, Zhuoying; Li, Ying; He, Zhiyi; Yu, Dequan; Wang, Zili; Lv, Chuan
errShare
errSave
Silicon Nanotweezers with Adjustable and Controllable Gap for the Manipulation and Characterization of DNA Molecules
err2006-05-01
err0
PREAI
errC. Yamahata; T. Takekawa; K. Ayano; M. Hosogi; M. Kumemura; B. Legrand; D. Collard; G. Hashiguchi; H. Fujita
errShare
errSave
Transmission-type plasmonic sensor for surface-enhanced Raman spectroscopy
err2016-11-16
err0
errOAAI
errMasahiro Yanagisawa; Mikiko Saito; Masahiro Kunimoto; Takayuki Homma
errShare
errSave
researcher View more