TY - JOUR
T1 - Reliability analysis model of time-dependent multi-mode system under fuzzy uncertainty
T2 - Applied to undercarriage structures
AU - Chen, Zhuangbo
AU - Lu, Zhenzhou
AU - Ling, Chunyan
AU - Feng, Kaixuan
N1 - Publisher Copyright:
© 2021 Elsevier Masson SAS
PY - 2022/1
Y1 - 2022/1
N2 - There lacks fuzzy reliability analysis model to assess the reliability level of time-dependent multi-mode system (T-sys) under fuzzy uncertainty, thus this paper defines T-sys failure possibility firstly to address this problem. Then fuzzy simulation is proposed for estimating the failure possibility of T-sys (T-sys-FS), in which the failure possibility estimation of T-sys is transformed into searching fuzzy design point, a failure point with the largest joint membership degree. To improve the efficiency of T-sys-FS for searching fuzzy design point, adaptive Kriging is inserted into T-sys-FS to establish a more efficient method shorten as T-sys-AK-FS. By use of the relationship between the failure state of T-sys and that of transient single mode, a new learning function is proposed in T-sys-AK-FS, on which the critical mode and critical instant may be selected for efficiently updating Kriging model of T-sys performance function. Additionally, based on the partial correct information provided by the current Kriging model and the properties of the fuzzy design point, an improved T-sys-AK-FS version (T-sys-AK-FSi) is proposed by combining the candidate sample pool reduction strategy, then the computational efficiency is enhanced further for searching the T-sys fuzzy design point. Several examples are presented to illustrate the accuracy and efficiency of the proposed methods.
AB - There lacks fuzzy reliability analysis model to assess the reliability level of time-dependent multi-mode system (T-sys) under fuzzy uncertainty, thus this paper defines T-sys failure possibility firstly to address this problem. Then fuzzy simulation is proposed for estimating the failure possibility of T-sys (T-sys-FS), in which the failure possibility estimation of T-sys is transformed into searching fuzzy design point, a failure point with the largest joint membership degree. To improve the efficiency of T-sys-FS for searching fuzzy design point, adaptive Kriging is inserted into T-sys-FS to establish a more efficient method shorten as T-sys-AK-FS. By use of the relationship between the failure state of T-sys and that of transient single mode, a new learning function is proposed in T-sys-AK-FS, on which the critical mode and critical instant may be selected for efficiently updating Kriging model of T-sys performance function. Additionally, based on the partial correct information provided by the current Kriging model and the properties of the fuzzy design point, an improved T-sys-AK-FS version (T-sys-AK-FSi) is proposed by combining the candidate sample pool reduction strategy, then the computational efficiency is enhanced further for searching the T-sys fuzzy design point. Several examples are presented to illustrate the accuracy and efficiency of the proposed methods.
KW - Failure possibility
KW - Fuzzy input
KW - Fuzzy simulation
KW - Kriging
KW - Sample pool reduction
KW - Time-dependent multi-mode structure system
UR - http://www.scopus.com/inward/record.url?scp=85121236161&partnerID=8YFLogxK
U2 - 10.1016/j.ast.2021.107278
DO - 10.1016/j.ast.2021.107278
M3 - 文章
AN - SCOPUS:85121236161
SN - 1270-9638
VL - 120
JO - Aerospace Science and Technology
JF - Aerospace Science and Technology
M1 - 107278
ER -