Component Importance Measures Research Articles

Computing Lifetime Distributions and Reliability for Systems With Outsourced Components: A Case Study

Extracted reliability information is universal in practice and makes it difficult to estimate system lifetime distributions and reliability. In order to address this problem, this paper develops a method to compute lifetime distributions of serial, parallel, and serial/parallel systems using the failure probability density functions of outsourced components, and then to compute system reliability and component importance measures. Time-varying weights are introduced to simplify the lifetime distribution of a system with multiple types of components and make the system lifetime distribution to be a sum of component probability density functions. A case study illustrates the developed method by identifying the lifetime distribution of a radio navigation system for large passenger aircraft. To demonstrate the effectiveness of the developed method, the estimation results from the developed method are compared with the results from a computer simulation method.

Variance-based importance analysis measure for mission reliability of phased mission system

ABSTRACTImportance measures are used to estimate the relative importance of components to system reliability. Phased mission systems (PMS) have many components working in several phases with different success criteria, and their component structural importance is distinct in different phases. Additionally, reliability parameters of components in PMS always have uncertainty in practice. Therefore, existing component importance measures based on either the partial derivative of system structure function or component structural importance may have difficulties in PMS importance analysis. This paper presents a simulation method to evaluate the component global importance for PMS based on the variance-based method and the Monte-Carlo method. To facilitate the practical use, we further discuss the correlation relationship between the component global importance and its possible influence factors, and present here a fitting model for evaluating component global importance. Finally, two examples are given to show that the fitting model displays quite reasonable component importance.

Component importance based on dependence measures

We discuss the construction of component importance measures for binary coherent reliability systems from known stochastic dependence measures by measuring the dependence between system and component failures. We treat both the time-dependent case in which the system and its components are described by binary random variables at a fixed instant as well as the continuous time case where the system and component life times are random variables. As dependence measures we discuss covariance and mutual information, the latter being based on Shannon entropy. We prove some basic properties of the resulting importance measures and obtain results on importance ordering of components.

A Bayesian kernel approach to modeling resilience-based network component importance

The resilience of infrastructure networks is an increasingly important consideration in infrastructure planning and risk management. One aspect of resilience-based planning is determining which components in the network are most important to the resilience of the network. This work makes use of a resilience-based component importance measure, the resilience worth, and proposes to model this measure under uncertainty using a Bayesian kernel technique. Such a technique can be useful in modeling component importance as it enables the probability distribution for the importance measure to be updated using data and prior information with a Bayesian kernel model. The proposed approach is applied to study the importance of locks and dams along the Mississippi River Navigation System. The highest predictive overall accuracy is achieved with a uniform prior distribution, and using the posterior distribution and a multicriteria decision analysis technique, we identify the five locks and dams with the largest impact on the system's resilience.

Importance measures for optimal structure in linear consecutive-k-out-of-n systems

Importance measures can describe which component reliability change is the most promising if the system structure can be easily changed. Recently, the Birnbaum importance for reconfigurable systems based on the constant reliability has been discussed. This importance shows that importance variation corresponds to the change in system optimal configuration. Traditionally, importance measures do not consider the possible change in a system structure throughout the system's lifetime. However, the possible optimal structural change and the system's lifetime should be considered in importance measures, which can describe the change of component importance with respect to the changes of the component sequencing in optimal system structure during the system lifetime. This paper studies the Birnbaum importance measure, integrated importance measure, and Mean Absolute Deviation with respect to the changes in optimal system structure throughout the system's lifetime. These measures provide useful information regarding the relationships between component reliability and importance measures with the changes in optimal system structure. Finally, examples of linear consecutive-k-out-of-n systems are used to illustrate the utilization of the proposed method.

An Overview of Various Importance Measures of Reliability System

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.

Component Importance Measures for Multi-Industry Vulnerability of a Freight Transportation Network

The multi-modal freight transportation network plays an important role in the economic vitality of states, regions, and the broader country. The functionality of this network is threatened by disruptive events that can disable the capacity of the network to enable flows of commodities in portions of nodes and links. This work integrates a multi-commodity network flow formulation with an economic interdependency model to quantify the multi-industry impacts of a disruption in the transportation network to ultimately measure and assess the importance of network components. The framework developed here can be used to measure the efficacy of strategies to reduce network vulnerability from the unique perspective of multi-industry impacts. The framework is illustrated with a case study considering the multi-modal freight transportation network consisting of inland waterways, railways, and interstate highways that connect the state of Oklahoma to surrounding states.

Component importance for multi-commodity networks: Application in the Swedish railway

Preparedness and resilience planning for critical infrastructure networks requires evaluating the impact to the network when its components are disrupted. We extend the well-studied problem of component importance measures in single-commodity networks to multi-commodity networks by integrating a flow based multi-commodity optimization model with a multi-criteria decision analysis tool. A three-stage approach is proposed to assess critical component importance with multi-commodity impacts on network vulnerability of one-at-a-time component disruptions. We analyze commodity-specific impacts on network performance of the Swedish railway system to rank critical links with respect to the 20 different commodity types transported in the network. We conclude that the two proposed metrics to measure vulnerability, unmet demand and link usage over 90%, give complementary information regarding the vulnerability of the network. It is further concluded that the approach supports exploration of how the lack of redundancy and capacity in the network can give rise to high levels of unmet demand or increased link usage for specific commodities that are transported in the network, although the overall robustness of the railway system towards single link failures is high.

Joint predictive maintenance and inventory strategy for multi-component systems using Birnbaum’s structural importance

Maintenance and inventory optimizations are two interrelated processes but often investigated separately in the literature. Joint optimization of these processes can reduce the total maintenance and inventory cost, it may however lead to a complex problem with a large numbers of decision parameters to be optimized. To face this open issue, in this paper, we present a joint predictive maintenance and inventory strategy for systems with complex structure and multiple non-identical components. Both predictive maintenance and spare parts provisioning operations are studied and optimized jointly. The prognostic condition index and the structural importance measure of components are jointly used to build the thresholds for preventive maintenance and spare part ordering. Additionally, in order to take the advantages of economic dependence, opportunistic maintenance decision rules based on the criticality level of components and their spare parts availability are proposed. To evaluate the proposed joint strategy, a cost model taking into account the economic dependence of both maintenance and inventory activities is developed. A numerical study of a 7-component system is finally presented to illustrate the use and the advantages of the proposed strategy. Indeed, this strategy shows the flexibility and the efficiency in joint maintenance and inventory optimization.

A cost-based integrated importance measure of system components for preventive maintenance

Preventive maintenance may be performed on a few selected components when a component fails. Importance measures can be used to identify the most important component that requires maintenance. However, this process involves two problems: (a) the preventive maintenance time of the selected component may be bigger than the maintenance time of the failed component; (b) the most important component may incur the highest maintenance cost. Traditional importance measures do not consider the possible effect of maintenance time and cost, which significantly affect the improvement of system reliability. Given the joint effect of component maintenance cost and time on system reliability, this study proposes a cost-based integrated importance measure (IIM) to identify the component or group of components that may be selected for preventive maintenance. The characteristics of cost-based IIM are examined to determine the relationships among failure rates, shape parameters, and the scale parameters of different components. Finally, an application to a wind turbine system is used to illustrate its usage.

A linear weighted (n, f, k) system for non-homogeneous Markov-dependent components

ABSTRACTWe study a linear weighted (n, f, k) system, denoted by L(n, f, k, w) system and consider the situation where components are non-homogeneous Markov-dependent. An L(n, f, k, w) system consists of n components ordered in a line, and each component u has a positive integer weight wu for u = 1, 2, …, n and w = (w1, w2, …, wn). The L(n, f, k, w):F (G) system fails (works) if the total weight of failed (working) components is at least f or the total weight of consecutive failed (working) components is at least k. For the L(n, f, k, w):F system with non-homogeneous Markov-dependent components, we derive closed-form formulas for the system reliability, the marginal reliability importance measure of a single component, and the joint reliability importance measure of multiple components using a conditional probability generating function method. We extend these results to the L(n, f, k, w):G systems, the weighted consecutive-k-out-of-n systems, and the weighted f-out-of-n systems. Our numerical examples and a case study on a bridge system demonstrate the use of derived formulas and provide the insights on the L(n, f, k, w) systems and the importance measures. In addition, the two failure modes associated with the L(n, f, k, w):F systems are analyzed by comparing to the single failure mode associated with the weighted consecutive-k-out-of-n:F systems and the single failure mode associated with the weighted f-out-of-n:F systems.

Reliability and Component Importance in Networks Subject to Spatially Distributed Hazards Followed by Cascading Failures

Abstract We investigate reliability and component importance in spatially distributed infrastructure networks subject to hazards characterized by large-scale spatial dependencies. In particular, we consider a selected IEEE benchmark power transmission system. A generic hazard model is formulated through a random field with continuously scalable spatial autocorrelation to study extrinsic common-cause-failure events such as storms or earthquakes. Network performance is described by a topological model, which accounts for cascading failures due to load redistribution after initial triggering events. Network reliability is then quantified in terms of the decrease in network efficiency and number of lost lines. Selected importance measures are calculated to rank single components according to their influence on the overall system reliability. This enables the identification of network components that have the strongest effect on system reliability. We thereby propose to distinguish component importance related to initial (triggering) failures and component importance related to cascading failures. Numerical investigations are performed for varying correlation lengths of the random field to represent different hazard characteristics. Results indicate that the spatial correlation has a discernible influence on the system reliability and component importance measures, while the component rankings are only mildly affected by the spatial correlation. We also find that the proposed component importance measures provide an efficient basis for planning network improvements.

A probabilistic framework based on statistical learning theory for structural reliability analysis of transmission line systems

This paper describes a novel application of statistical learning theory to structural reliability analysis of transmission lines considering the uncertainties of climatic variables such as, wind speed, ice thickness and wind angle, and of the resistance of structural elements. The problem of reliability analysis of complex structural systems with implicit limit state functions is addressed by statistical model selection, where the goal is to select a surrogate model of the finite element solver that provides the value of the performance function for each conductor, insulator or tower element. After determining the performance function for each structural element, Monte Carlo simulation is used to calculate their failure probabilities. The failure probabilities of towers and the entire line are then estimated from the failure probabilities of their elements/components considering the correlation between failure events. In order to quantify the relative importance of line components and provide the engineers with a practical decision tool, the paper presents the calculation of two types of component importance measures. The presented methodology can be used to achieve optimised design, and to assess upgrading strategies to increase the line capacity.

Aggregation of importance measures for decision making in reliability engineering

This article investigates the aggregation of rankings based on component importance measures to provide the decision maker with a guidance for design or maintenance decisions. In particular, ranking aggregation algorithms of the literature are considered, a procedure for ensuring that the aggregated ranking is compliant with the Condorcet criterion of majority principle is presented and two original ranking aggregation approaches are proposed. Comparisons are made on a case study of an auxiliary feed-water system of a nuclear pressurized water reactor.

Component Importance Measure Computation Method Based Fuzzy Integral with Its Application

In view of the negative impact of component importance measures based on system reliability theory and centrality measures based on complex networks theory, there is an attempt to provide improved centrality measures (ICMs) construction method with fuzzy integral for measuring the importance of components in electromechanical systems in this paper. ICMs are the meaningful extension of centrality measures and component importance measures, which consider influences on function and topology between components to increase importance measures usefulness. Our work makes two important contributions. First, we propose a novel integration method of component importance measures to define ICMs based on Choquet integral. Second, a meaningful fuzzy integral is first brought into the construction comprehensive measure by fusion multi-ICMs and then identification of important components which could give consideration to the function of components and topological structure of the whole system. In addition, the construction method of ICMs and comprehensive measure by integration multi-CIMs based on fuzzy integral are illustrated with a holistic topological network of bogie system that consists of 35 components.

Reliability Evaluation of Network Systems with Dependent Propagated Failures Using Decision Diagrams

In a network system, a propagated failure (PF) is a failure originating from a network component that can cause extensive damages to other network components or even the failure of the entire system. Existing works on PFs have mostly assumed the deterministic effect from a component PF, i.e., a fixed subset of system components is affected whenever the PF occurs. However, in many real-world systems, the components may have different levels of protection, and the effect of damage from a component PF can be dependent upon the status of other components within the same system or the occurrence order of component failures. This paper proposes a new analytical method based on multi-valued decision diagrams (MDDs) for the reliability analysis of network systems with dependent propagation effects. Particularly, new MDD modeling procedures are proposed for considering different types of dependent PF effects introduced by different protection levels. After the system MDD is generated using a new MDD combination algorithm to efficiently handle the dependent PF effects, methods for computing the network reliability and component importance measures are presented. The detailed analysis of an example network system subjected to dependent PFs is presented to illustrate the basics and application of the proposed method. It is shown that the proposed MDD-based method generates smaller model size and thus presents lower computational complexity in the model generation and evaluation than the existing Markov method and separable method.

Importance Index of Components in Uncertain Reliability Systems

Importance measure is an index for estimating the importance of an individual component or a group of components in a reliability system. So far, the importance measures for components in stochastic reliability systems have been investigated. In order to calculate the importance of a component or a group of components in an uncertain reliability system, this paper proposes a new concept of importance index. Some formulas are given to calculate the importance index of a component and a group of components in an uncertain reliability system. Then, some special types of uncertain reliability systems such as uncertain series, parallel, parallel-series, series-parallel, bridge, and k-out-of-n systems are studied.

A complex network-based importance measure for mechatronics systems

Abstract In view of the negative impact of functional dependency, this paper attempts to provide an alternative importance measure called Improved-PageRank (IPR) for measuring the importance of components in mechatronics systems. IPR is a meaningful extension of the centrality measures in complex network, which considers usage reliability of components and functional dependency between components to increase importance measures usefulness. Our work makes two important contributions. First, this paper integrates the literature of mechatronic architecture and complex networks theory to define component network. Second, based on the notion of component network, a meaningful IPR is brought into the identifying of important components. In addition, the IPR component importance measures, and an algorithm to perform stochastic ordering of components due to the time-varying nature of usage reliability of components and functional dependency between components, are illustrated with a component network of bogie system that consists of 27 components.

Resilience Assessment Based on Time-Dependent System Reliability Analysis

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.

Dynamic decision-making for reliability and maintenance analysis of manufacturing systems based on failure effects

ABSTRACTA framework for reliability and maintenance analysis of job shop manufacturing systems is proposed in this paper. An efficient preventive maintenance (PM) policy in terms of failure effects analysis (FEA) is proposed. Subsequently, reliability evaluation and component importance measure based on FEA are performed under the PM policy. A job shop manufacturing system is applied to validate the reliability evaluation and dynamic maintenance policy. Obtained results are compared with existed methods and the effectiveness is validated. Some vague understandings for issues such as network modelling, vulnerabilities identification, the evaluation criteria of repairable systems, as well as PM policy during manufacturing system reliability analysis are elaborated. This framework can help for reliability optimisation and rational maintenance resources allocation of job shop manufacturing systems.