Component Importance Measures Research Articles

Component uncertainty importance measure in complex multi-state system considering epistemic uncertainties

Importance measures can be used to identify the vulnerable components in an aviation system at the early design stage. However, due to lack of knowledge or less available information on the component or system, the epistemic uncertainties may be one of the challenging issues in importance evaluation. In addition, the properties of the aircraft system, which are the fundamentals of the component importance measure, including the hierarchy, dependency, randomness, and uncertainty, should be taken into consideration. To solve these problems, this paper proposes the component Uncertainty Integrated Importance Measure (component UIIM) which considers multiple epistemic uncertainties in the complex multi-state systems. The degradation process for the components is described by a Markov model, and the system reliability model is developed using the Markov hierarchal evidential network. The concept of integrated importance measure is then extended into component UIIM to evaluate the component criticality rather than the component state change criticality, from the perspective of system performance. A case study on displacement compensation hydraulic system is presented to show the effectiveness of the proposed uncertainty importance measure. The results show that the component UIIM can be an effective method for evaluating the component criticality from system performance perspective at the system early design.

Calculation of the System Unavailability Measures of Component Importance Using the D2T2 Methodology of Fault Tree Analysis

A recent development in Fault Tree Analysis (FTA), known as Dynamic and Dependent Tree Theory (D2T2), accounts for dependencies between the basic events, making FTA more powerful. The method uses an integrated combination of Binary Decision Diagrams (BDDs), Stochastic Petri Nets (SPN) and Markov models. Current algorithms enable the prediction of the system failure probability and failure frequency. This paper proposes methods which extend the current capability of the D2T2 framework to calculate component importance measures. Birnbaum’s measure of importance, the Criticality measure of importance, the Risk Achievement Worth (RAW) measure of importance and the Risk Reduction Worth (RRW) measure of importance are considered. This adds a vital ability to the framework, enabling the influence that components have on system failure to be determined and the most effective means of improving system performance to be identified. The algorithms for calculating each measure of importance are described and demonstrated using a pressure vessel cooling system.

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.

System-Reliability-Based Disaster Resilience Analysis of Infrastructure Networks and Causality-Based Importance Measure

Abstract Civil infrastructure systems become highly complex and thus get more vulnerable to disasters. The concept of disaster resilience, the overall capability of a system to manage risks posed by catastrophic events, is emerging to address the challenge. Recently, a system-reliability-based disaster resilience analysis framework was proposed for a holistic assessment of the components' reliability, the system's redundancy, and the society's ability to recover the system functionality. The proposed framework was applied to individual structures to produce diagrams visualizing the pairs of the reliability index (β) and the redundancy index (π) defined to quantify the likelihood of each initial disruption scenario and the corresponding system-level failure probability, respectively. This paper develops methods to apply the β-π analysis framework to infrastructure networks and demonstrates its capability to evaluate the disaster resilience of networks from a system reliability viewpoint. We also propose a new causality-based importance measure of network components based on the β-π analysis and a causal diagram model that can consider the causality mechanism of the system failure. Compared with importance measures in the literature, the proposed measure can evaluate a component's relative importance through a well-balanced consideration of network topology and reliability. The proposed measure is expected to provide helpful guidelines for making optimal decisions to secure the disaster resilience of infrastructure networks.

A dynamic risk-early-warning methodology of distribution system faults incorporating spatiotemporal imbalanced data distributions

Power distribution systems are susceptible to external environmental disturbances. The early warning of potential fault risks in both spatial and temporal scales can assist in maintenance planning and overhaul scheduling for distribution systems, thus their overall reliability consequently. To achieve it, this paper proposes a self-adaptive prediction model for future failure risks in distribution systems, namely the association rules exploration with conditional filter and fitness regulation (AREcffr). In this approach, electrical attributes along with surrounding condition factors are both implemented as inputs. Then, to cope with the commonly-occurred imbalanced data distribution in both two scales when distinguishing risky factors, the conditional importance diagnostic threshold setting and importance diagnostic standard calculation methods set are designed. In that case, the included high-risk-low-probability (HRLP) time series and condition factors in the sparsely distributed input data can be taken into the assessment. Next, to conduct more reasonable measurements of risk levels for those selected risky components, a component importance measure (CIM)-based relative weight analysis model, which is according to the variation of total system risks caused by each risky component rather than its appearance frequency or database share, is established. Finally, an empirical research is presented, and the flexibility and advantages of this risk-early-warning model can be validated and demonstrated in consequence.

A condition evaluation ensemble for power metering HPLC units within complex data scenarios

Abstract The High-Speed Power Line Carrier Communication (HPLC) enables the connections among power metering devices in integrated energy systems, and thus their satisfying operations are indispensable for system reliabilities. In order to more precisely diagnose their conditions especially in real complex data scenes, a multi-model evaluation ensemble is proposed in this paper. Firstly, typical IoT application contexts of customer-side metering equipment are analyzed, thus the corresponding main impact factors along with their performance evaluation indices can be probed. Next, to handle the multi-source, heterogeneous, high-dimensional datasets during applications, the Kernel Independent Component Analysis (KICA) is established to diminish data dimensionalities, thus the individual weights of each index can be rated. On the other hand, the Component Importance Measure (CIM) model is built to differentiate the impact degree of each indicator on the overall IoT connection performance, where the influence of dissimilar index on the entire performance, rather than the proportion or frequency, will be directly assessed to determine their impact weights. Ergo, a comprehensive diagnosis can be achieved via these two-fold total weights accordingly. Finally, the feasibility and effectiveness of the proposed method can be verified by an empirical case study, which is conducive to further improving the accuracy and rationality of HPLC condition evaluations.

Deriving a minimum set of indicators to assess network component importance

The importance of the components of a system has been widely studied in the literature. The main objective of such component importance measures (CIMs) is to evaluate the effect that the components can cause in the system under study by not being present or to quantify the participation of their presence in functional characteristics of the system. When dealing with several CIMs that represent different perspectives on importance, different CIMs can produce different rankings of importance. To detect the possible existence of a minimum set of indicators useful for assessing the importance of the components, this work proposes an innovative two-step procedure, which we apply to networks. In the first step, a set of similar functional networks is proposed as evaluators or “judges” for determining the set of CIMs that provide the most information on the importance of the components. And in the second step, a multi-criteria decision analysis technique is adopted to rank the CIMs. The proposed approach is illustrated for (i) a set of CIMs and (ii) a set of 13 different electric power networks.

Importance measures for critical components in complex system based on Copula Hierarchical Bayesian Network

In order to identify the vulnerable components and ensure the required reliability of mechatronics systems, importance measures of critical components are crucially used in the early design of systems. However, complex mechatronics systems have the properties of hierarchy, nonlinearity, dependency, uncertainty, and randomness, which make it difficult to analyze the coupling failure mechanisms, model the system, estimate its reliability, and complete importance measures of its components. This paper proposes importance measures for components with continuous time degradation. The Wiener process model is used to describe the continuous-time degradation process, and the Copula Hierarchical Bayesian Network (CHBN) is developed for system reliability estimation. Six importance measures are proposed for continuous-time degrading components. These importance measures provide a time-dependent analysis of the criticality of components, thus adding insights on the contributions of the components on the system reliability or performance over time. A case study on the harmonic gear drive is then conducted to demonstrate the use of the proposed importance measures. The results of the study show that the CHBN-based importance measures can be a valuable decision-support tool for designers in the early design of systems.

Maintenance cost-based importance analysis under different maintenance strategies

As system components and their interactions become more and more complex, system reliability problems become increasingly prominent. In the reliability research, component importance measures have been widely used as an important decision-making aid index. Particularly, importance measures have been used in designing or selecting the optimal maintenance strategy. However, the practical factor of maintenance cost is not comprehensively considered in the existing importance analysis. This paper proposes new importance measures based on two types of maintenance costs under different maintenance strategies, including the failure-triggered replacement strategy that replaces damaged components, the age-based replacement strategy where a component is preventively replaced and when it has been used for a certain period of time, and the hybrid strategy that integrates the former two strategies. Methods for computing the proposed maintenance cost-based importance measures for series systems and parallel systems are given. A case study of a propeller plane system is provided to illustrate and validate the proposed method.

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.

Computing Birnbaum and Barlow-Proschan importance measures using system signature

Component importance measures are widely used in engineering and reliability analysis in testing the safety of running systems. There are several component importance measures to realize which components in a coherent system play a more important role than the others. The most common ones belong to Birnbaum and Barlow & Proschan. In this paper a new method, based on the number of path sets with exactly i working components such that the component at sth place functions, has been proposed to evaluate these two measures for all types of systems appearing in the literature.

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.

Research on Time-Dependent Component Importance Measures Considering State Duration and Common Cause Failure

Unlike the current risk monitors, Real-time Online Risk Monitoring and Management Technology is characterized by time-dependent modeling on the state duration of components. Given the real-time plant configuration, it eventually provides the time-dependent risk level and importance measures for operation and maintenance management. This paper focuses on the assessment method of time-dependent importance measures and its risk-informed applications in real-time online risk monitoring and management technology, including Fussell-Vesely (FV), risk achievement worth (RAW), and risk reduction worth (RRW). In this study, the values of component importance have been investigated with a time-dependent risk quantification model, as well as the common cause failure treatment model. Here three options of common cause failure treatment have been developed, assuming that the unavailability of a component could be due to an independent factor (Option 1), a common cause factor (Option 2), or an unconfirmed cause (Option 3). In the special case of “what if a component is out-of-service” of the RAW numerator, a hybrid method for the RAW evaluation is presented resulting in a balanced and reasonable RAW value. A simple case study was demonstrated. The results showed that the absolute values and ranking order of time-dependent importance not only reflected the effect of the cumulative state duration of component on risk, but also comprehensively accounted for all possible situations of component unavailability. Moreover, time-dependent importance measures improved and provided novel insights for online configuration management, 1) ranking SSCs/events/human actions for controlling increased risk and optimizing near–term plans; and 2) exempting or limiting temporary configurations during online operation.

A reliability based measure of structural robustness for coherent systems

Progressive collapse (the spread of a relatively minor local damage through a chain of failures to the collapse of a significant portion of a structure) constitutes an important mode of system failure. A robust structure is able to locally confine the effects of a relatively minor initial damage. As a technical term, robustness has different connotations in different fields; a variety of robustness measures, phenomenological in nature, are also available in the structural engineering literature. The measure of structural robustness against progressive collapse must ideally be a monotonic function of a suitable damage measure, must help identify and prioritize critically important initial damages, and must be able to rank different structures in terms of their susceptibility to progressive collapse under similar loading and initial damages. It must also take all uncertainties into account. Starting with relative entropy between the joint distributions of safety margins and the generalized Birnbaum/Barlow-Proschan-Iyer component importance measure, we propose a new robustness index for binary, coherent structural systems that is bounded by zero and one; zero signifying certain collapse given the initial damage and one signifying the structure’s complete indifference to the given initial damage. The methodology is demonstrated on an indeterminate planar truss structure. The initial damage considered is the failure of any one given member. Element strengths and the load are taken to be random. Progressive collapse being a sequence driven event, an enumerative scheme is adopted for reliability analyses of the intact and damaged structures. The relations between robustness index, importance measure, element reliability and correlation between intact and damage safety margins are investigated; comparison of the proposed index with existing robustness measures is undertaken.

Flow-based seismic risk assessment of a water transmission network employing probabilistic seismic hazard analysis

In this study, a seismic risk assessment model was proposed to evaluate the seismic reliability of a water transmission network. The proposed risk assessment model involves earthquake generation and hydraulic analysis modules. To consider a comprehensive approach, the numerical simulation strategy includes probabilistic seismic hazard analysis, buried pipeline deterioration, numerical modeling of network facilities, and the interdependency between pumping plant and substation. For this purpose, a flow-based MATLAB code has been developed that enables iterative hydraulic analysis using EPANET software. For numerical simulation, the epicenter and earthquake magnitudes were determined based on probabilistic seismic hazard analysis, and a real water transmission network in South Korea was constructed. From the numerical results, two performance indicators (system serviceability and nodal serviceability) and four mean normal status ratios of facilities were adopted to evaluate the network performance. In addition, a component importance measure of facilities in a network system was calculated by introducing a reduction factor. The numerical results using the proposed flow-based model show that the system performance is affected by buried pipeline deterioration and network interdependency, as well as the location and magnitude of the input earthquake. It is thus concluded that the seismic risk assessment of a water transmission network should be performed using a model with a comprehensive approach.

Bayesian belief-based model for reliability improvement of the digital reactor protection system

The digital reactor protection system (RPS) is one of the most important digital instrumentation and control (I&C) systems utilized in nuclear power plants (NPPs). It ensures a safe reactor trip when the safety-related parameters violate the operational limits and conditions of the reactor. Achieving high reliability and availability of digital RPS is essential to maintaining a high degree of reactor safety and cost savings. The main objective of this study is to develop a general methodology for improving the reliability of the RPS in NPP, based on a Bayesian Belief Network (BBN) model. The structure of BBN models is based on the incorporation of failure probability and downtime of the RPS I&C components. Various architectures with dual-state nodes for the I&C components were developed for reliability-sensitive analysis and availability optimization of the RPS and to demonstrate the effect of I&C components on the failure of the entire system. A reliability framework clarified as a reliability block diagram transformed into a BBN representation was constructed for each architecture to identify which one will fit the required reliability. The results showed that the highest availability obtained using the proposed method was 0.9999998. There are 120 experiments using two common component importance measures that are applied to define the impact of I&C modules, which revealed that some modules are more risky than others and have a larger effect on the failure of the digital RPS.

Availability importance measures of components in smart electric power grid systems

The emergence of the smart grid has brought great innovation in the new distribution power system, facilitated a green and sustainable energy-based society, and mitigated the growing energy crisis. The smart grid is regarded as the next generation electrical power grid. It not only improves the power distribution systems with the techniques of distributed generation, but also makes the systems more complicated than the traditional ones. Thus it is important to evaluate the availability of smarts grids and to indicate the vulnerable parts of the systems. In this paper, a hierarchical model consisting of a layered fault tree (FT) and continuous-time Markov chains (CTMCs) is presented to model a smart grid system with two different power supply modes. We also analyze the component importance of the system, aiming to find the weak parts of the system thereby improving the system design. The importance analysis of components is based on parametric sensitivities and binary decision diagram (BDD) representation for the FTs. In a numerical illustration, we quantify the availability of the system with two power supply modes and also evaluate the importance of all components in the system.

Reliability and importance measures for a general consecutive-type system

ABSTRACT A general consecutive-type system, called -consecutive-,-out-of-:F system, is considered. Such a system consists of linearly or circularly ordered components and fails if and only if there are at least -overlapping runs of consecutive failed components. For systems with statistically independent components, the system reliability is obtained via recursive schemes and simulation. Reliability importance measures for system components are calculated and analyzed as well. Illustrative analytic and numerical examples demonstrate further the theoretical results.

Reliability and importance analysis of uncertain system with common cause failures based on survival signature

Redundant design has become the commonly used technique for ensuring the reliability of complex systems, which calls for great concern to common cause failure problems in such systems. Incomplete data in combination with vague judgments from experts introduce imprecision and epistemic uncertainties in the performance characterization of components. These issues need to be taken into account for assessing the system reliability. In this paper, a comprehensive reliability assessment method is presented by adopting the concept of survival signature to estimate the reliability of complex systems with multiple types of components. Particular attention is devoted to common cause failures (CCFs), which are modeled and quantified by decomposed partial α-decomposition method. Uncertainties caused by incomplete data for CCF events are reduced by hierarchical Bayesian inference. The component importance measure is enhanced to assess the importance of various possible CCF scenarios and to identify their potential impact on system reliability. The presented method is used to analyze the reliability of a dual-axis pointing mechanism for communication satellite, which is a commonly used satellite antenna control mechanism. The engineering application demonstrates the effectiveness of the method.

Ocena opartej na modelu obciążeniowo-wytrzymałościowym ważności elementów systemu szeregowego z uwzględnieniem uszkodzeń wywołanych wspólną przyczyną

Series systems, whose structures are simple, are widely discovered in practical engineering, but the interdependency between the components is complex, such as common cause failure. With the consideration of the components’ strength, this paper focuses on ranking the importance measure of components considering the common cause failure based on the stress-strength interference (SSI) model. The weakest component can be identified by integrating the SSI model with the importance measure when the strength mean and variance of the component under the load stress is known. Firstly, the analytic methods are proposed to calculate the SSI-based importance of components in the series systems. Then, the monotonicity of SSI-based importance is analyzed by changing the strength mean or strength variance of one component. The results show that the SSI-based importance of components, whose parameters are changed, will reduce monotonically with the increase of strength mean or increase monotonically with the increase of strength variance. Finally, a component replacement method is developed based on the rules that both the importance of replaced component and the importance ranks should be unchanged after the replacement. SSI-based importance can help engineers to make maintenance decisions, and the component replacement method can increase the diversity of spare parts by finding the equivalent components.