Structural Importance Measure Research Articles

Importance analysis of a system based on survival signature by structural importance measures

The analysis of the influence of system components on system failure is a typical problem in reliability engineering which is known as importance analysis of a system. There are different methods for the calculation of the importance measures of the system components. In this paper, a new method for the calculation of structural importance measures that are one of the types of importance measures is presented. The novelty of the proposed method is the use of the survival signature instead of the structure function for the computation of the importance measures. The proposed method is based on the tool of logical differential calculus, in particular, direct partial logical derivatives. The advantages and practical applicability of the proposed method were confirmed in the analysis of the reliability characteristics of the fuel system of the propulsion complex of a passenger aircraft.

Maintenance optimisation of multicomponent systems using hierarchical coordinated reinforcement learning

The Markov decision process (MDP) is a widely used method to optimise the maintenance of multicomponent systems, which can provide a system-level maintenance action at each decision point to address various dependences among components. However, MDP suffers from the “curse of dimensionality” and can only process small-scale systems. This paper develops a hierarchical coordinated reinforcement learning (HCRL) algorithm to optimise the maintenance of large-scale multicomponent systems. Both parameters of agents and the coordination relationship among agents are designed based on system characteristics. Furthermore, the hierarchical structure of agents is established according to the structural importance measures of components. The effectiveness of the proposed HCRL algorithm is validated using two maintenance optimisation problems, one on a natural gas plant system and the other using a 12-component series system under dependant competing risks. Results show that the proposed HCRL outperforms methods in two recently published papers and other benchmark approaches including the new emerging deep reinforcement learning.

Reliability Importance Measures for Network Based on Failure Counting Process

Traditional importance measures seldom consider how the number of failed components influences the network reliability. This paper proposes two importance measures under the circumstance that the failure sequence of the components follows a counting process. The first importance measure aims to assess the contribution of the individual component (edge) to the network failure. The second evaluates the contribution of the individual component to the network functionality. Both importance measures are time-dependent functions, and their values are jointly determined by the network structure and the distribution of the number of failed components at a particular time. We prove that the proposed importance measures are able to generate consistent rankings based on edge's impact on the network reliability behavior. When networks possess special structure or the number of failed edges follows the special distribution, the rankings are coincident with the results generated from some traditional importance measures. When component's failure sequence follows a saturated nonhomogeneous Poisson process, the proposed importance measures are equivalent to the structural importance measure as time approaches zero or infinite. Finally, numerical examples are provided to demonstrate the application and performance of the proposed measures.

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.

Reliability Analysis of Logic Network with Multiple Outputs

Reliability analysis of a logic network with multiple outputs is considered in this paper. One of the principal tasks of reliability engineering is identification of those system components that have the most influence on the system activity. There exist several measures that are used for this purpose. One of them is Structural Importance Measure (SIM) which focuses on topological importance of individual system components. This measure is calculated from the system structure function, i.e. function that defines dependency between system activity and activity of its components, using logical differential calculus. In this paper, we present a method that can be used to identify the structure function of a logic network with multiple outputs and, based on logical differential calculus, we propose several definitions of the SIM for this type of systems. As a case study, the topological analysis of a one-bit full adder is considered in the last part of this paper. This study demonstrates usefulness of our approach in reliability analysis of logic networks and indicates that its further development and practical implementation could be beneficial.

Infrastructure Communication Sensitivity Analysis of Wireless Sensor Networks

Sensitivity or importance analysis has been widely used for identifying system weaknesses and supporting system improvement and maintenance activities. Despite the rich literature on the sensitivity analysis of many mission-critical and safety-critical systems, no existing work has been devoted to wireless sensor networks (WSN). In this paper, we first analyze link and node importance with respect to the infrastructure communication reliability of WSN systems. The binary decision diagrams based algorithms are implemented to evaluate and compare three importance measures: structural importance measure, Birnbaum's measure, and criticality importance measure. The effects of node degree, choice of the destination node, data delivery models, as well as mission time on the importance analysis results are investigated through examples. Results from this work can facilitate the design, deployment, and maintenance of reliable WSN for critical applications. Copyright © 2015 John Wiley & Sons, Ltd.

Condition-based maintenance for multi-component systems using importance measure and predictive information

This paper presents a predictive condition-based maintenance strategy for multi-component systems whose structure may impact components deterioration process. To select components for preventive maintenance actions, a decision rule relying on both structural importance measure of components and their predictive reliability that can be estimated at inspection times is proposed. For corrective maintenance actions, an adaptive opportunistic maintenance decision rule taking into account both the criticality level of components and logistic support constraints is introduced. Moreover, both economic and structure dependencies between components are studied and integrated in maintenance model. A 12-component system is finally introduced to illustrate the use and the performance of the proposed predictive maintenance strategy. Indeed, the proposed strategy provides more flexibility in maintenance decision-making, hence leading to significant profits in terms of maintenance cost when compared to existing strategies.

A Joint Predictive Maintenance and Spare Parts Provisioning Policy for Multi-component Systems Using RUL Prediction and Importance Measure

The paper presents a joint predictive maintenance and spare parts provisioning policy for gradually deteriorating multicomponent systems with complex structure. The decisionmaking process related to maintenance, spare parts ordering, as well as inspections scheduling is based on both RUL prediction and structural importance measure. Moreover, economic dependency between components is studied and integrated in decision rules. In addition, the impacts of the system structure on components deterioration process are also investigated. This dependency may have a significant influence on the RUL estimation of components. In order to evaluate the performance of the proposed joint predictive policy, a cost model is used. Finally, a numerical example of a 6-component system is introduced to illustrate the use and the advantages of the proposed joint maintenance and spare parts provisioning policy.

Redundancy Allocation using Component-Importance Measures for Maximizing System Reliability

SYNOPTIC ABSTRACTRedundancy allocation, being an important and effective way to improve system reliability, has been discussed by many authors. The main idea, which has been advocated in this article, lies in the fact that some components have more significance in the functioning of the system than the others; because of this, it is expected that if allocation of the redundant component is made according to some component-importance measure, optimality can be achieved easily. The novelty of this study is that the problem of redundancy allocation has been solved by allocating redundant components according to some component-importance measures that are not very difficult to obtain and comprehend for an engineered system, even when there is no information about the reliability of the components. The component structural-importance measure and reliability-importance measure have been used in this work for the purpose of allocating redundancy. The results derived can be used to maximize system reliability using redundancy allocation for a general n-component coherent system. Applications of the results have been illustrated with examples of coherent systems.

최소절단집합과 최소경로집합을 이용한 구조 및 신뢰성 중요도 척도의 개발 및 적용

The research discusses interrelationship of structural and reliability importance measures which used in the probabilistic safety assessment. The most frequently used component importance measures, such as Birnbaum`s Importance (BI), Risk Reduction (RR), Risk Reduction Worth (RRW), RA (Risk Achievement), Risk Achievement Worth (RAW), Fussel Vesely (FV) and Critically Importance (CI) can be derived from two structure importance measures that are developed based on the size and the number of Minimal Path Set (MPS) and Minimal Cut Set (MCS). In order to show an effectiveness of importance measures which is developed in this paper, the three representative functional structures, such as series-parallel, k out of n and bridge are used to compare with Birnbaum`s Importance measure. In addition, the study presents the implementation examples of Total Productive Maintenance (TPM) metrics and alternating renewal process models with exponential distribution to calculate the availability and unavailability of component facility for improving system performances. System state structure functions in terms of component states can be converted into the system availability (unavailability) functions by substituting the component reliabilities (unavailabilities) for the component states. The applicable examples are presented in order to help the understanding of practitioners.

BDD-based two-party trust sensitivity analysis for social networks

This paper presents a binary decision diagrams BDD-based algorithm for the trust sensitivity analysis in social networks. Trust is a crucial basis for interactions among parties in social networks. Based on trust scores of direct links between parties, the trust sensitivity analysis can help identify which direct links in the social network contributes the most to a trust relationship between two particular parties who are not directly connected in the network. Three different sensitivity measures including Birnbaum's measure, criticality importance factor, and structural importance measure are investigated and adapted for the two-party trust sensitivity analysis for social networks. Several examples are given to illustrate the application of the presented BDD-based method and sensitivity measures.

Consecutive expansions of [formula omitted]-out-of- [formula omitted] systems

We introduce consecutive expansions of k –out–of– n systems, which have the property that components are totally ordered by the node criticality relation and with respect to well-known structural importance measures. We propose some formulae to easily compute these measures and study the hierarchies induced for them for large systems.

Comments on “A Hierarchy of Importance Indices”

The paper by Hwang ( <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">IEEE</i> <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Trans.</i> <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Reliability</i> , vol. 54, no.1, p. 169-172, 2005) presented structural importance measures in terms of cutsets or pathsets, and investigated how they are related. Hwang's paper has made contributions to importance measures. However, several errors which cause confusions are found in the paper. This note is to clarify these errors, and discuss the suspicions.

A performance index for semicoherent structures

The index introduced here provides a normalized measure of the effectiveness of any semicoherent structure. We study the mathematical properties of the index and derive different axiomatic characterizations for it. Moreover, close relationships are shown to reliability functions and also to the Birnbaum structural importance measure—for which twice the performance index plays the role of potential function—that give rise to computational procedures. Some numerical examples illustrate the application of the index.

A Combinatorial Approach for Component Importance

A number of structural importance measures like Birnbaum importance, Barlow–Proschan importance etc., are used for studying component importance in a system. A unified approach for calculating component importance in a system was introduced by Seth and Ramamurthy. It is based on the concept of structural matrix which needs to be determined from the simple form of a structure function of a system. In this article, we present a combinatorial expression for the elements of the structural matrix. Other related results and applications of this approach to different systems are considered. Results of this article is also applicable to the problem of finding power of a player in game theory.

An ordering heuristic to develop the binary decision diagram based on structural importance

Abstract Fault tree analysis is often used to assess risks within industrial systems. The technique is commonly used although there are associated limitations in terms of accuracy and efficiency when dealing with large fault tree structures. The most recent approach to aid the analysis of the fault tree diagram is the Binary Decision Diagram (BDD) methodology. To utilise the technique the fault tree structure needs to be converted into the BDD format. Converting the fault tree requires the basic events of the tree to be placed in an ordering. The ordering of the basic events is critical to the resulting size of the BDD, and ultimately affects the performance and benefits of this technique. A number of heuristic approaches have been developed to produce an optimal ordering permutation for a specific tree. These heuristic approaches do not always yield a minimal BDD structure for all trees. This paper looks at a heuristic that is based on the structural importance measure of each basic event. Comparing the resulting size of the BDD with the smallest generated from a set of six alternative ordering heuristics, this new structural heuristic produced a BDD of smaller or equal dimension on 77% of trials.