Abstract

The performance of a mechanical or structural system can be improved through a proper selection of its design parameters such as the geometric dimensions, external actions (loads), and material characteristics. The computation of the reliability of a system, in general, requires a knowledge of the probability distributions of the parameters of the system. It is known that for most practical systems, the exact probability distributions of the parameters are not known. However, the first few moments of the parameters of the system may be readily available in many cases from experimental data. The determination of the reliability and the sensitivity of reliability to variations or fluctuations in the parameters of the system starts with the establishment of a suitable limit state equation. This work presents an approximate reliability analysis for mechanical and structural systems using the fourth-order moment function for approximating the first four moments of the limit state function. By combining the fourth-order moment function with the probabilistic perturbation method, numerical methods are developed for finding the reliability and sensitivity of reliability of the system. An automobile brake and a power screw are considered for demonstrating the methodology and effectiveness of the proposed computational approach. The results of the automobile brake are compared with those given by the Monte Carlo method.

References

1.
Lemaire
,
M.
,
1997
, “
Reliability and Mechanical Design
,”
Reliab. Eng. Syst. Saf.
,
55
(
2
), pp.
163
170
.10.1016/S0951-8320(96)00083-X
2.
Li
,
Q. S.
,
Fang
,
J. Q.
, and
Liu
,
D. K.
,
1999
, “
Evaluation of Structural Dynamic Responses by Stochastic Finite Element Method
,”
Struct. Eng. Mech.
,
8
(
5
), pp.
477
490
.10.12989/sem.1999.8.5.477
3.
Tu
,
J.
,
Choi
,
K. K.
, and
Park
,
Y. H.
,
1999
, “
A New Study on Reliability-Based Design Optimization
,”
ASME J. Mech. Des.
,
121
(
4
), pp.
557
564
.10.1115/1.2829499
4.
Melchers
,
R. E.
, and
Ahammed
,
M.
,
2004
, “
A Fast Approximate Method for Parameter Sensitivity Estimation in Monte Carlo Structural Reliability
,”
Comput. Struct.
,
82
(
1
), pp.
55
61
.10.1016/j.compstruc.2003.08.003
5.
Li
,
J.
, and
Chen
,
J. B.
,
2004
, “
Probability Density Evolution Method for Dynamic Response Analysis of Structures With Uncertain Parameters
,”
Comput. Mech.
,
34
(
5
), pp.
400
409
.10.1007/s00466-004-0583-8
6.
Beck
,
A. T.
, and
Melchers
,
R. E.
,
2004
, “
On the Ensemble Crossing Rate Approach to Time Variant Reliability Analysis of Uncertain Structures
,”
Probab. Eng. Mech.
,
19
(
1–2
), pp.
9
19
.10.1016/j.probengmech.2003.11.018
7.
Au
,
S. K.
,
2005
, “
Reliability-Based Design Sensitivity by Efficient Simulation
,”
Comput. Struct
,.
83
(
14
), pp.
1048
1061
.10.1016/j.compstruc.2004.11.015
8.
Zhang
,
Y. M.
,
He
,
X. D.
, and
Liu
,
Q. L.
,
2006
, “
Reliability-Based Sensitivity Design of Vehicle Components Upon Information of Incomplete Probability
,”
Acta Armamentarii (Chin.
),
27
(
4
), pp.
608
612
.
9.
Ayala
,
U. E.
, and
Moan
,
T.
,
2007
, “
Time-Variant Reliability Assessment of FPSO Hull Girder With Long Cracks
,”
ASME J. Offshore Mech. Arct.
,
129
(
2
), pp.
81
89
.10.1115/1.2355513
10.
Lu
,
Y.
,
Zeng
,
J.
, and
Wu
,
P.
,
2009
, “
Reliability and Parametric Sensitivity Analysis of Railway Vehicle Bogie Frame Based on Monte Carlo Numerical Simulation
,”
International Conference on High Performance Computing and Applications (HPCA 2009)
, Springer-Verlag, Berlin, pp.
280
287
.
11.
Men
,
Y. Z.
,
2010
, “
Research on the Application of Fuzzy Neural Network in the Automobile Reliability
,”
Adv. Mat. Res.
,
136
, pp.
77
81
.10.4028/www.scientific.net/AMR.136.77
12.
Wang
,
X. G.
,
Wang
,
B. Y.
,
Zhu
,
L. S.
, and
Lu
,
H.
,
2011
, “
Dynamic Reliability Sensitivity Design of Mechanical Components With Arbitrary Distribution Parameters
,”
Adv. Mat. Res.
,
199–200
, pp.
487
494
.10.4028/www.scientific.net/AMR.199-200.487
13.
Yang
,
Z.
,
Zhang
,
Y.
,
Zhang
,
X.
, and
Huang
,
X.
,
2012
, “
Reliability Sensitivity-Based Correlation Coefficient Calculation in Structural Reliability Analysis
,”
Chin. J. Mech. Eng.
,
25
(
3
), pp.
608
614
.10.3901/CJME.2012.03.608
14.
Lv
,
H.
, and
Zhang
,
Y. M.
,
2014
, “
Gradual Reliability Analysis of Mechanical Component Systems
,”
Mater. Res. Innov.
,
18
(
Suppl. 1
), pp.
29
32
.
15.
Borgonovo
,
E.
, and
Plischke
,
E.
,
2016
, “
Sensitivity Analysis: A Review of Recent Advances
,”
Eur. J. Oper. Res.
,
248
(
3
), pp.
869
887
.10.1016/j.ejor.2015.06.032
16.
Liu
,
X.
,
Zheng
,
S.
,
Feng
,
J.
,
Huang
,
H.
,
Chu
,
J.
, and
Zhao
,
L.
,
2014
, “
Reliability Analysis and Evaluation of Automobile Welding Structure
,”
Qual. Reliab. Eng. Int.
,
30
(
8
), pp.
1293
1300
.10.1002/qre.1550
17.
Sun
,
Z. G.
,
Wang
,
C. X.
,
Niu
,
X. M.
, and
Song
,
Y. D.
,
2017
, “
Design Optimization Method for Composite Components Based on Moment Reliability-Sensitivity Criteria
,”
Int. J. Turbo. Jet. Eng.
,
34
(
3
), pp.
233
244
.10.1515/tjj-2016-0003
18.
Xiao
,
S. N.
, and
Lu
,
Z. Z.
,
2017
, “
Structural Reliability Sensitivity Analysis Based on Classification of Model Output
,”
Aerosp. Sci. Technol.
,
71
, pp.
52
61
.10.1016/j.ast.2017.09.009
19.
Papaioannou
,
I.
,
Breitung
,
K.
, and
Straub
,
D.
,
2018
, “
Reliability Sensitivity Estimation With Sequential Importance Sampling
,”
Struct. Saf.
,
75
, pp.
24
34
.10.1016/j.strusafe.2018.05.003
20.
Zhao
,
J. Y.
,
Zeng
,
S. K.
,
Guo
,
J. B.
, and
Du
,
S. H.
,
2018
, “
Global Reliability Sensitivity Analysis Based on Maximum Entropy and 2-Layer Polynomial Chaos Expansion
,”
Entropy
,
20
(
3
), p.
202
.10.3390/e20030202
21.
Lu
,
Z.-H.
,
Hu
,
D.-Z.
, and
Zhao
,
Y.-G.
,
2017
, “
Second-Order Fourth-Moment Method for Structural Reliability
,”
J. Eng. Mech.
,
143
(
4
), p.
06016010
.10.1061/(ASCE)EM.1943-7889.0001199
22.
Sadeghi
,
J.
,
de Angelis
,
M.
, and
Patelli
,
E.
,
2020
, “
Analytic Probabilistic Safety Analysis Under Severe Uncertainty
,”
ASCE-ASME J. Risk Uncertainty Eng. Syst.
, Part A
6
(
1
), p.
04019019
.10.1061/AJRUA6.0001028
23.
Beer
,
M.
, and
Patelli
,
E.
, (Guest Editors),
2015
, “
Editorial: Engineering Analysis With Vague and Imprecise Information
,”
Struct. Saf.
,
52
(
Part B
), p.
143
.10.1016/j.strusafe.2014.11.001
24.
Yang
,
G.
,
Huang
,
X.
,
Li
,
Y.
, and
Ding
,
P.
,
2019
, “
System Reliability Assessment With Imprecise Probabilities
,”
Appl. Sci.
,
9
(
24
), p.
5422
.10.3390/app9245422
25.
Choi
,
J.
,
An
,
D.
, and
Won
,
J.
,
2010
, “
Bayesian Approach for Structural Reliability Analysis and Optimization Using Kriging Dimension Reduction Method
,”
ASME J. Mech. Des.
,
132
(
5
), p.
051003
.10.1115/1.4001377
26.
Straub
,
D.
, and
Kiureghian
,
A. D.
,
2010
, “
Bayesian Network Enhanced With Structural Reliability Methods: Methodology
,”
J. Eng. Mech., ASCE
,
136
(
10
), p.
8
.10.1061/(ASCE)EM.1943-7889.0000170
27.
Steeb
,
W. H.
,
1997
,
Matrix Calculus and Kronecker Product With Applications and c++ Programming
,
World Scientific Publishing
,
Singapore
.
28.
Cari
,
D. M.
,
2001
,
Matrix Analysis and Applied Linear Algebra
,
Society for Industrial and Applied Mathematics
, Philadelphia, PA.
29.
Zhao
,
Y. G.
, and
Ono
,
T.
,
2001
, “
Moment Methods for Structural Reliability
,”
Struct. Saf.
,
23
(
1
), pp.
47
75
.10.1016/S0167-4730(00)00027-8
30.
Stuart
,
A.
, and
Ord
,
J. K.
,
1987
,
Kendall's Advanced Theory of Statistics
,
Charles Griffin & Co
.,
London
.
31.
Rao
,
S. S.
, and
Cao
,
L. T.
,
2002
, “
Optimum Design of Mechanical Systems Involving Interval Parameters
,”
ASME J. Mech. Des.
,
124
(
3
), pp.
465
472
.10.1115/1.1479691
32.
Budynas
,
R. G.
, and
Nisbett
,
J. K.
,
2020
,
Shigley's Mechanical Engineering Design
,
McGraw-Hill
,
New York
.
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