Hongyan Dui

Integrated Importance Measure of Component States Based on Loss of System Performance

This paper mainly focuses on the integrated importance measure (IIM) of component states based on loss of system performance. To describe the impact of each component state, we first introduce the performance function of the multi-state system. Then, we present the definition of IIM of component states. We demonstrate its corresponding physical meaning, and then analyze the relationships between IIM and Griffith importance, Wu importance, and Natvig importance. Secondly, we present the evaluation method of IIM for multi-state systems. Thirdly, the characteristics of IIM of component states are discussed. Finally, we demonstrate a numerical example, and an application to an offshore oil and gas production system for IIM to verify the proposed method. The results show that 1) the IIM of component states concerns not only the probability distributions and transition intensities of the states of the object component, but also the change in the system performance under the change of the state distribution of the object component; and 2) IIM can be used to identify the key state of a component that affects the system performance most.

Semi-Markov Process-Based Integrated Importance Measure for Multi-State Systems

Importance measures in reliability engineering are used to identify weak components of a system and signify the roles of components in contributing to proper functioning of the system. Recently, an integrated importance measure (IIM) has been proposed to evaluate how the transition of component states affects the system performance based on the probability distributions and transition rates of component states. In the system operation phase, the bathtub curve presents the change of the transition rate of component states with time, which can be described by three different Weibull distributions. The behavior of a system under such distributions can be modeled by the semi-Markov process. So, based on the reported IIM equations of component states, this paper studies how the transition of component states affects system performance under the semi-Markov process. This measure can provide useful information for preventive actions (such as monitoring enhancement, construction improvement, etc.), and provide support to improve system performance. Finally, a simple numerical example is presented to illustrate the utilization of the proposed method.

Component state-based integrated importance measure for multi-state systems

Importance measures in reliability engineering are used to identify weak components and/or states in contributing to the reliable functioning of a system. Traditionally, importance measures do not consider the possible effect of groups of transition rates among different component states, which, however, has great effect on the component probability distribution and should therefore be taken into consideration. This paper extends the integrated importance measure (IIM) to estimate the effect of a component residing at certain states on the performance of the entire multi-state systems. This generalization of IIM describes in which state it is most worthy to keep the component to provide the desired level of system performance, and which component is the most important to keep in some state and above for improving the performance of the system. An application to an oil transportation system is presented to illustrate the use of the suggested importance measure.

The Integrated Importance Measure of Multi-State Coherent Systems for Maintenance Processes

This paper mainly focuses on the integrated importance measure (IIM) of component states for maintenance processes. To describe the impact of each component state in maintenance processes, a maintenance cost function of multi-state systems is defined at first. Second, considering the probability distributions, transition rates of the component states, and system maintenance costs, the IIM of component states is described. The corresponding characteristics of the IIM of the component states are discussed in both series systems and parallel systems. Then the relationships between IIM and Griffith importance, Wu importance, mean absolute deviation, and multi-state redundancy importance measures are also discussed. At last, a numerical example is given to demonstrate the IIM of component states. The results show that IIM can be used to identify the most important component state for the maintenance decision.

Component Importance for Multi-State System Lifetimes With Renewal Functions

Importance measures are widely used to characterize the roles of components in systems. The system lifetime can be divided into different life stages. Traditionally, importance measures do not consider the possible effect of the expected number of component failures over a systems lifetime and over different life stages, which, however, has a great effect on the system performance changes, and should therefore be taken into consideration. This paper extends the integrated importance measure (IIM) from unit time to system lifetime, and to different life stages. Based on the renewal functions of components, this measure can evaluate the changes of the system performance due to component failures. This generalization of the IIM describes which component is the most important to improve the performance of the system during the system lifetime and at different life stages. An example of the application of an oil transportation system is presented to illustrate the use of the generalized IIM.

A novel decision diagrams extension method

Abstract Binary decision diagram (BDD) is a graph-based representation of Boolean functions. It is a directed acyclic graph (DAG) based on Shannon׳s decomposition. Multi-state multi-valued decision diagram (MMDD) is a natural extension of BDD for the symbolic representation and manipulation of the multi-valued logic functions. This paper proposes a decision diagram extension method based on original BDD/MMDD while the scale of a reliability system is extended. Following a discussion of decomposition and physical meaning of BDD and MMDD, the modeling method of BDD/MMDD based on original BDD/MMDD is introduced. Three case studies are implemented to demonstrate the presented methods. Compared with traditional BDD and MMDD generation methods, the decision diagrams extension method is more computationally efficient as shown through the running time.

Importance analysis for reconfigurable systems

Importance measures are used in reliability engineering to rank the system components according to their contributions to proper functioning of the entire system and to find the most effective ways of reliability enhancement. Traditionally, the importance measures do not consider the possible change of system structure with the improvement of specific component reliability. However, if a component׳s reliability changes, the optimal system structure/configuration may also change and the importance of the corresponding component will depend on the chosen structure. When the most promising component reliability improvement is determined, the component importance should be taken into account with respect to the possible structure changes. This paper studies the component reliability importance indices with respect to the changes of the optimal component sequencing. This importance measure indicates the critical components in providing the system reliability enhancement by both enhancing the component׳s reliability and reconfiguring the system. Examples of linear consecutive-k-out-of-n: F and G systems are considered to demonstrate the change of the component Birnbaum importance with the optimal system reconfiguration. The results show that the change of the importance index corresponds to the change of the system optimal configuration and the importance index can change not monotonically with the variation of the component reliability.

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.

Compositional Performance Evaluation with Importance Measures

Importance measures are used to identify weak components and/or states in a system based on the component state random variables, which seem to be inadequate to show the corresponding actual situations. By contrast, the performance random variables own significant practical meanings and eliminate the subjectivity and limitation of state division and definition in many actual situations. In this paper, instead of state random variables, the performance stochastic processes are used for modeling all the components and the entire system, and the integrated importance measure (IIM) for the performance random variables are extended. The generalized IIM evaluates the contribution of component performance to the desired level of system performance. A case study of an oil transmission system is used to illustrate the effectiveness of our approach with importance measures.

Integrated importance measure for multi-state coherent systems of k level

To verify the effectiveness of the integrated importance measure (IIM) for multi-state coherent systems of k level,the definition and physical meaning of IIM are demonstrated.Then,the improvement potential and Δ-importance measures are generalized to multi-state coherent systems based on the system performance level,and the relationships between IIM and traditional importance measures are discussed.The characteristics of IIM are demonstrated in both series and parallel systems.Also,an application to an oil transportation system is given.The comparison results show that: (i) IIM has some useful properties that are not possessed by traditional importance measures; (ii) IIM is effective in evaluating the component role in multi-state systems when the component reliability and the failure rate are simultaneously considered.