Existing Importance Measures Research Articles

Integrated availability importance measure analysis for multi‐state manufacturing system based on proportional hazards model

AbstractAvailability is a comprehensive reflection of the overall performance for a manufacturing system, so it is necessary to evaluate the impact of component changes on the availability of a manufacturing system. However, most of the existing importance measures are based on reliability change or state degradation to measure the relative importance of components, which cannot be comprehensively analyzed for multi‐state manufacturing systems. The availability of a multi‐state manufacturing system is influenced by time and state. Therefore, considering the synergistic effect of time and state, this paper proposes an Integrated Availability Importance Measure (IAIM) based on proportional hazards model combining with component state probability and state transition rate. This measure fully examines the time parameters, state parameters and maintenance impact of multi‐state systems, and can comprehensively evaluate the relative importance of multi‐state coherent system components, provide theoretical basis for further developing the availability maintenance strategy, and indicate the improvement direction for further improving the reliability of multi‐state manufacturing systems. A numerical example is given to prove its feasibility, and the influence of related parameters is investigated by sensitivity analysis. Finally, a real‐world case study is used to illustrate its applicability.

Importance measure construction and solving algorithm oriented to the cost-constrained reliability optimization model

Importance measures are used to prioritize the system components for achieving high efficiency and economy of reliability optimization. The existing importance measures pay more attention to evaluating the impact of component performance changes on the objective function (such as system reliability, cost and so on). Still, these importance measures do not consider the influence of constraints in the system reliability optimization models during the ranking process of components, which may leave the component with the largest importance measure almost no space to improve its reliability. For the cost-constrained reliability optimization model (ROM), this paper proposes a cost-constrained ROM-based importance measure (CRIM) by comprehensively considering the objective function and constraints, and several properties of CRIM are discussed. Then, the CRIM-based genetic algorithm (CRIGA) is developed to solve the cost-constrained ROM. The numerical experiment for different scale systems is implemented to evaluate the performance of CRIGA by comparing it with other optimization algorithms. Experimental results show that CRIGA can get better solutions with faster convergence speed and better robustness. Finally, the case on the coal transportation system of the thermal power plant is introduced to illustrate the application of CRIM and CRIGA in reliability optimization.

Risk-based bridge component importance measures under seismic loads

This article formulates risk-based component importance measures (RCIMs) to identify critical components for a bridge subjected to earthquakes. RCIM, unlike traditional reliability-based importance measures, is well suited for complex systems like bridges as it uses a flexible system failure definition using bridge level risk consequences or performance objectives. Contrasting traditional notions of risk and reliability related to structural system failures, the RCIMs embrace a broader definition of risk which includes consequences of system failure to society and the environment. System failure is defined as exceedance of a user-defined threshold of system risk (e.g. based on cost, emissions, embodied energy, or other metrics). Our definition of system failure using global performance metrics helps relating component to system reliability explicitly and offers an alternative perspective to aggregate joint failure events of bridge components into relevant damage states. RCIM combines information about the reliability of a component and its contribution to the system performance and hence the importance measures developed offer risk mitigation indicators for decision makers as to which components may require upgrade to achieve a given performance objective. The proposed RCIMs generalize existing importance measures through analytical exploration of the entire space of system configurations with correlated component failures, while also considering multiple risk-based criteria beyond reliability. These RCIMs, demonstrated through a seismic bridge system case study, further show that as the bridge components age and the hazard intensity varies, the relative contribution of the components to system risk also shifts.

Comparing with the joint importance under consideration of consecutive-k-out-of-n system structure changes

For a multi-component system, the impact of the alteration of components on the system reliability often needs assessment. Existing importance measures, however, do not consider the impact of the possible change of the system structure during its life cycle. Therefore, relevant factors should be considered to better reflect the changes in system reliability. To this end, this paper proposes joint importance measures for the optimal component sequence of a consecutive-k-out-of-n system. Incorporating the generalized measures, the paper obtains the joint integrated importance measure and the joint differential importance measure for the optimal component sequence in the binary and multistate consecutive-k-out-of-n systems. Then some properties of the proposed joint importance measures for optimal component sequence are analyzed. Furthermore, this measure reveals the relationship between component reliability and joint importance measure under consideration of consecutive-k-out-of-n system structure changes. Finally, numerical examples are given to demonstrate the applicability of the proposed measures.

Residual life-based importance measures for predictive maintenance decision-making

Importance measures have been widely used as meaningful decision-aiding indicators in reliability engineering, risk management and maintenance optimization. However, few importance measures integrates the actual condition (working states or degradation levels) of components that dynamically evolves with time. This work develops a novel time-dependent importance measure defined as the capacity of a component (or group of components) to improve, when it is replaced, the system residual life. The proposed [Formula: see text] measure can help to better prioritize a component or group of components regarding to its improvement ability in the system life time while considering the actual conditions of all components of the system. The originality and complementarity of the proposed measure when compared to existing importance measures is also investigated. The proposed importance measure is then extended to integrate the economic dimension of the maintenance decision, through the maintenance costs, the benefit gained by the maintenance operations and as well as the economic dependence between components. It is finally shown how the proposed [Formula: see text] measure and its extension can “optimally” suggest a component or a group of several components for preventive maintenance decision-making, based on both the technical criterion (residual life of the system) and the economic aspects (benefit and costs). The use and the advantages of the proposed importance measure and its extension are illustrated on a four-component system.

The α, β-Cut Intervals and Weakest t-Norm Based Importance Measure for Criticality Analysis in Intuitionisitic Fuzzy Fault Tree Analysis of LNG-ESD System

Different importance measures have been developed and widely utilized in system reliability analysis to measure the importance of system components. During operation period of a complex system, it becomes more difficult to collect exact failure data of system components i.e. the collected failure data may have some sort of uncertainties due to various reasons. Generally, triangular shaped fuzzy numbers or intuitionistic fuzzy numbers (IFNs) are used to quantify the various types of uncertainty in failure data of the system’s components and fuzzy arithmetic operations are utilized which computes each reliability index with wide range of prediction due to accumulating phenomenon of fuzziness. In this paper, different types of intuitionistic fuzzy number (IFN) are used to quantify the failure data uncertainty of the system’s components while α, β-cut intervals and weakest t-norm (Tw)-based arithmetic operations are employed to reduce the range of prediction for each reliability index. The objective of the present study is to develop Tw-based importance measure for criticality analysis in intuitionistic fuzzy fault tree analysis (IF-FTA) of a system, when different types of uncertainties in crisp failure data of basic events are quantified by different types of membership and non-membership functions. The developed importance measure applies system fault tree, α-cut and β-cut intervals, different types of intuitionistic fuzzy number (IFN) and weakest t-norm (Tw) based intuitionistic fuzzy arithmetic operations. To demonstrate the applicability of the proposed measure, a case study of liquefied natural gas terminal emergency shutdown system (LNG-ESD system) is performed. The IF-FTA of LNG-ESD system is presented to evaluate the fault intervals of the system at different levels using Tw-based arithmetic operations on different types of IFNs. The developed importance measure is applied to identify and rank the critical basic events in IF-FTA of LNG-ESD system. The proposed measure is also compared with some existing importance measures.

Minimal path set importance in complex systems

To find the best mode for system design in reliability optimization, risk engineers around the world use the importance measure as a basic tool. This paper introduces a new importance measure taking into account minimal path sets of the system. It helps to optimize the system designs that occur in many situations. For instance, this importance measure can be used (a) in identifying important components of any complex system and (b) solving constrained redundancy optimization problems. This is illustrated by providing two heuristic algorithms. In the first algorithm, this measure is used to find important components of any complex system ensuring improved system reliability. The second algorithm is used to solve a constrained redundancy optimization problem for any general coherent system giving (near) optimal solutions in 1-neighborhood. The results show that the new importance measure is easily applicable, unlike the classical ones. Hence, it serves as a very useful tool in measuring the important component(s) and solving constrained redundancy optimization problems of complex systems. Thus, it can be considered as a good alternative to the existing importance measures.

Dynamic importance measure for the K-out-of-n: G system under repeated random load

Importance measures have been widely used in system reliability design and optimization to find out system weaknesses. But the existing importance measures are hard to identify the dynamic weakness effectively for systems subjected to repeated and random load. Based on the stress-strength interference (SSI) model, this paper aims to develop a dynamic importance measure for k-out-of-n: G systems under repeated random load. At first, the importance measure for systems under repeated random load is put forward by using the order statistics theory. Then by describing the loading process with the Poisson process, a time-dependent model of the dynamic importance measure is established. Further, a Monte Carlo (MC) simulation method is proposed to verify the correctness of the dynamic importance measures. Finally, the reliability analysis for a multi-lock cargo door system in the air freighter is implemented to illustrate the practical application of the dynamic importance measure.

A cost-based importance measure for system components: An extension of the Birnbaum importance

In reliability engineering, component importance measures are used to prioritise components in a system for purposes such as reliability improvement and maintenance planning. Existing importance measures have paid little attention to the costs incurred by maintaining a system and its components within a given time period. Cost-effectiveness analysis, however, is critically important in increasingly competitive markets. This paper proposes a new cost-based importance measure which considers costs incurred by maintaining a system and its components within a finite time horizon. Possible extensions are discussed and examples are given to show the use of the new measure.

Maximal conditional chi-square importance in random forests

High-dimensional data are frequently generated in genome-wide association studies (GWAS) and other studies. It is important to identify features such as single nucleotide polymorphisms (SNPs) in GWAS that are associated with a disease. Random forests represent a very useful approach for this purpose, using a variable importance score. This importance score has several shortcomings. We propose an alternative importance measure to overcome those shortcomings. We characterized the effect of multiple SNPs under various models using our proposed importance measure in random forests, which uses maximal conditional chi-square (MCC) as a measure of association between a SNP and the trait conditional on other SNPs. Based on this importance measure, we employed a permutation test to estimate empirical P-values of SNPs. Our method was compared to a univariate test and the permutation test using the Gini and permutation importance. In simulation, the proposed method performed consistently superior to the other methods in identifying of risk SNPs. In a GWAS of age-related macular degeneration, the proposed method confirmed two significant SNPs (at the genome-wide adjusted level of 0.05). Further analysis showed that these two SNPs conformed with a heterogeneity model. Compared with the existing importance measures, the MCC importance measure is more sensitive to complex effects of risk SNPs by utilizing conditional information on different SNPs. The permutation test with the MCC importance measure provides an efficient way to identify candidate SNPs in GWAS and facilitates the understanding of the etiology between genetic variants and complex diseases. heping.zhang@yale.edu Supplementary data are available at Bioinformatics online.

EFFECTIVE COMPONENT IMPORTANCE ANALYSIS FOR THE MAINTENANCE OF SYSTEMS WITH COMMON-CAUSE FAILURES

In this paper, we consider the component importance analysis of coherent systems subject to common-cause failures. The purpose of component importance analysis is to obtain information regarding a component's contribution or importance to the system reliability. The results from the component importance analysis are key contributors to the system design, tuning, and maintenance activities. Various measures have been proposed for the component importance analysis, but little work has been done to compare their performance. In this research, we investigate and compare a set of nine existing importance measures and select the most informative and appropriate one for guiding the system maintenance activity. An important concern in the traditional fault tree reliability analysis, common-cause failures, is also considered in the component importance analysis. An example is designed and analyzed to show the selection process and to illustrate our efficient method for considering the effects of common-cause failures in the component importance analysis.

Four decades of reliability progress—Annual reliability and maintainability symposium : C. Raymond Knight. Proc. A. Reliab. Maintainab. Symp., 156 (1991)

Importance measures have been widely used in system reliability design and optimization to find out system weaknesses. But the existing importance measures are hard to identify the dynamic weakness effectively for systems subjected to repeated and random load. Based on the stress-strength interference (SSI) model, this paper aims to develop a dynamic importance measure for k-out-of-n: G systems under repeated random load. At first, the importance measure for systems under repeated random load is put forward by using the order statistics theory. Then by describing the loading process with the Poisson process, a time-dependent model of the dynamic importance measure is established. Further, a Monte Carlo (MC) simulation method is proposed to verify the correctness of the dynamic importance measures. Finally, the reliability analysis for a multi-lock cargo door system in the air freighter is implemented to illustrate the practical application of the dynamic importance measure.