Resilience Assessment Based on Time-Dependent System Reliability Analysis

2016 ◽  
Author(s):  
Zhen Hu ◽  
Sankaran Mahadevan
Keyword(s):  
Reliability Analysis ◽  
System Reliability ◽  
Assessment Method ◽  
Time Dependent ◽  
Importance Measure ◽  
System Reliability Analysis ◽  
Component Importance ◽  
System Resilience ◽  
Resilience Assessment ◽  
Time Dependent System

Significant efforts have been recently devoted to the qualitative and quantitative evaluation of resilience in engineering systems. Current resilience evaluation methods, however, have mainly focused on business supply chains and civil infrastructure, and need to be extended for application in engineering design. A new resilience metric is proposed in this paper for the design of mechanical systems to bridge this gap, by investigating the effects of recovery activity and failure scenarios on system resilience. The defined resilience metric is connected to design through time-dependent system reliability analysis. This connection enables us to design a system for a specific resilience target in the design stage. Since computationally expensive computer simulations are usually used in design, a surrogate modeling method is developed to efficiently perform time-dependent system reliability analysis for resilience assessment. System resilience assessment is then investigated based on the developed time-dependent system reliability analysis method. The connection between the proposed resilience assessment method and design is discussed through the sensitivity analysis and component importance measure. Two numerical examples are used to illustrate the effectiveness of the proposed resilience assessment method and the associated sensitivity analysis and component importance measure.

scholarly journals Resilience Assessment Based on Time-Dependent System Reliability Analysis

2016 ◽  
Vol 138 (11) ◽  
Author(s):  
Zhen Hu ◽  
Sankaran Mahadevan
Keyword(s):  
Reliability Analysis ◽  
System Reliability ◽  
Assessment Method ◽  
Time Dependent ◽  
Design Stage ◽  
System Failure ◽  
System Reliability Analysis ◽  
System Resilience ◽  
Resilience Assessment ◽  
Time Dependent System

Significant efforts have been recently devoted to the qualitative and quantitative evaluation of resilience in engineering systems. Current resilience evaluation methods, however, have mainly focused on business supply chains and civil infrastructure, and need to be extended for application in engineering design. A new resilience metric is proposed in this paper for the design of mechanical systems to bridge this gap, by investigating the effects of recovery activity and system failure paths on system resilience. The defined resilience metric is connected to design through time-dependent system reliability analysis. This connection enables us to design a system for a specific resilience target in the design stage. Since computationally expensive computer simulations are usually used in design, a surrogate modeling method is developed to efficiently perform time-dependent system reliability analysis. Based on the time-dependent system reliability analysis, dominant system failure paths are enumerated and then the system resilience is estimated. The connection between the proposed resilience assessment method and design is explored through sensitivity analysis and component importance measure (CIM). Two numerical examples are used to illustrate the effectiveness of the proposed resilience assessment method.

Time-Dependent System Reliability Analysis Using Random Field Discretization

2015 ◽  
Vol 137 (10) ◽  
Author(s):  
Zhen Hu ◽  
Sankaran Mahadevan
Keyword(s):  
Random Field ◽  
Reliability Analysis ◽  
System Reliability ◽  
Probability Of Failure ◽  
Two Dimensions ◽  
Time Dependent ◽  
Time Interval ◽  
System Reliability Analysis ◽  
Reliability Method ◽  
Time Dependent System

This paper proposes a novel and efficient methodology for time-dependent system reliability analysis of systems with multiple limit-state functions of random variables, stochastic processes, and time. Since there are correlations and variations between components and over time, the overall system is formulated as a random field with two dimensions: component index and time. To overcome the difficulties in modeling the two-dimensional random field, an equivalent Gaussian random field is constructed based on the probability equivalency between the two random fields. The first-order reliability method (FORM) is employed to obtain important features of the equivalent random field. By generating samples from the equivalent random field, the time-dependent system reliability is estimated from Boolean functions defined according to the system topology. Using one system reliability analysis, the proposed method can get not only the entire time-dependent system probability of failure curve up to a time interval of interest but also two other important outputs, namely, the time-dependent probability of failure of individual components and dominant failure sequences. Three examples featuring series, parallel, and combined systems are used to demonstrate the effectiveness of the proposed method.

An Outcrossing Rate Model and Its Efficient Calculation for Time-Dependent System Reliability Analysis

2017 ◽  
Vol 139 (4) ◽  
Author(s):  
C. Jiang ◽  
X. P. Wei ◽  
Z. L. Huang ◽  
J. Liu
Keyword(s):  
Reliability Analysis ◽  
System Reliability ◽  
Outcrossing Rate ◽  
Time Dependent ◽  
Engineering Practice ◽  
Rate Model ◽  
Random Loading ◽  
System Reliability Analysis ◽  
Efficient Calculation ◽  
Time Dependent System

Time-dependent reliability problems widely appear in the engineering practice when the material properties of the structure deteriorate in time or random loading modeled as random processes is involved. Among existing methods to the time-dependent reliability problems, the most dominating one is the outcrossing rate method. This paper presents an outcrossing rate model and its efficient calculation approach for system problems, and based on the presented model, a time-dependent system reliability analysis method is proposed. The main idea of the method is to transform the evaluation of the system outcrossing rates into the calculation of a time-invariant system reliability. Three numerical examples are used to demonstrate the effectiveness of the proposed method.

scholarly journals An Overview of Various Importance Measures of Reliability System

2017 ◽  
Vol 2 (3) ◽  
pp. 150-171 ◽  
Author(s):  
Kalpesh P. Amrutkar ◽  
Kirtee K. Kamalja
Keyword(s):  
Reliability Analysis ◽  
System Reliability ◽  
System Failure ◽  
Numerical Rank ◽  
Reliability Improvement ◽  
System Reliability Analysis ◽  
Component Importance ◽  
Critical Components ◽  
The Impact ◽  
Reliability Systems

One of the purposes of system reliability analysis is to identify the weaknesses or the critical components in a system and to quantify the impact of component’s failures. Various importance measures are being introduced by many researchers since 1969. These component importance measures provide a numerical rank to determine which components are more important to system reliability improvement or more critical to system failure. In this paper, we overview various components importance measures and briefly discuss them with examples. We also discuss some other extended importance measures and review the developments in study of various importance measures with respect to some of the popular reliability systems.

Time-dependent intuitionistic fuzzy system reliability analysis

2020 ◽  
Vol 24 (19) ◽  
pp. 14441-14448
Author(s):  
Mohammad Ghasem Akbari ◽  
Gholamreza Hesamian
Keyword(s):  
Reliability Analysis ◽  
System Reliability ◽  
Fuzzy System ◽  
Time Dependent ◽  
Intuitionistic Fuzzy ◽  
System Reliability Analysis

Adaptive Surrogate Modeling for Multidisciplinary Reliability Analysis Under Time-Dependent Uncertainty

2017 ◽  
Author(s):  
Zhen Hu ◽  
Sankaran Mahadevan
Keyword(s):  
Reliability Analysis ◽  
System Reliability ◽  
Uncertainty Propagation ◽  
Surrogate Modeling ◽  
Surrogate Models ◽  
Time Dependent ◽  
System Reliability Analysis ◽  
Training Point ◽  
Coupling Variables ◽  
Multidisciplinary System

Multidisciplinary systems will remain in transient states when time-dependent interactions are present among the coupling variables. This brings significant challenges to time-dependent multidisciplinary system reliability analysis. This paper develops an adaptive surrogate modeling approach (ASMA) for multidisciplinary system reliability analysis under time-dependent uncertainty. The proposed framework consists of three modules, namely initialization, uncertainty propagation, and three-level global sensitivity analysis (GSA). The first two modules check the quality of the surrogate models and determine when and where we should refine the surrogate models. Approaches are then proposed to estimate the potential error of the failure probability estimate and determine the location of the new training point. In the third module (i.e. three-level GSA), a method is developed to decide which surrogate model to refine, through GSA at three different levels. These three modules are integrated together systematically and enable us to adaptively allocate the computational resources to refine different surrogate models in the system and thus achieve high accuracy and efficiency in time-dependent multidisciplinary system reliability analysis. Results of two numerical examples demonstrate the effectiveness of the proposed framework.

Time-Dependent System Reliability Analysis With Second-Order Reliability Method

2020 ◽  
Vol 143 (3) ◽  
Author(s):  
Hao Wu ◽  
Zhangli Hu ◽  
Xiaoping Du
Keyword(s):  
Reliability Analysis ◽  
System Reliability ◽  
Limit State ◽  
Second Order ◽  
Time Dependent ◽  
Component Reliability ◽  
Reliability Method ◽  
Series Systems ◽  
State Functions ◽  
Time Dependent System

Abstract System reliability is quantified by the probability that a system performs its intended function in a period of time without failures. System reliability can be predicted if all the limit-state functions of the components of the system are available, and such a prediction is usually time consuming. This work develops a time-dependent system reliability method that is extended from the component time-dependent reliability method using the envelope method and second-order reliability method. The proposed method is efficient and is intended for series systems with limit-state functions whose input variables include random variables and time. The component reliability is estimated by the second-order component reliability method with an improve envelope approach, which produces a component reliability index. The covariance between component responses is estimated with the first-order approximations, which are available from the second-order approximations of the component reliability analysis. Then, the joint distribution of all the component responses is approximated by a multivariate normal distribution with its mean vector being component reliability indexes and covariance being those between component responses. The proposed method is demonstrated and evaluated by three examples.

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