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

Si, Shubin; Dui, Hongyan; Zhao, Xibin; Zhang, Shenggui; Sun, Shudong

doi:10.1109/tr.2011.2182394

Cited by 93 publications

(50 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…W ( ) represents the contribution of state of component to the system performance. Natvig importance measure [29], like the Barlow-Proschan measure, is a weighted average of the Birnbaum measure [30] and is static with the mean lifetime and repair time [1].…”

Section: Traditional Importance Measuresmentioning

confidence: 99%

“…The reason is that Wu ( ) represents the contribution of component in the system from state to state 0. Wu importance is a static indicator of the system performance [1]. The rank of Barlow-Proschan importance is constant.…”

Section: Verification By Examplesmentioning

confidence: 99%

“…With the importance values of all components, proper actions can be taken on the weakest component to improve system reliability at the minimum cost or effort [1].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Dynamic Importance Analysis of Components with Known Failure Contribution of Complex Systems

Zhang

Dai

et al. 2018

Mathematical Problems in Engineering

View full text Add to dashboard Cite

Importance analysis deals with the investigation of influence of individual system component on system operation. This paper mainly focuses on dynamic important analysis of components in a multistate system. Assuming that failure probabilities of system components are independent, a new time integral-based importance measure approach (TIIM) is proposed to measure the loss of system performance that is caused by each individual component. Reversely the importance of a component can be evaluated according to the magnitude of performance loss of the system caused by it. Moreover, the dynamic varying curve of importance of a component with time can be described by calculating criticality of the component at each state. On the other hand, in the proposed approach, the importance probability curve of a component is fitted by using the failure data from all components of system excluding that of the component itself so as to solve the problem of inaccurate fitting caused by small sample data. The approach has been verified by probability analysis of failure data of CNC machines.

show abstract

Section: Traditional Importance Measuresmentioning

confidence: 99%

Section: Verification By Examplesmentioning

confidence: 99%

See 1 more Smart Citation

Dynamic Importance Analysis of Components with Known Failure Contribution of Complex Systems

Zhang

Dai

et al. 2018

Mathematical Problems in Engineering

View full text Add to dashboard Cite

show abstract

“…Borgonovo and Apostolakis [9] designed the differential importance measure for the probabilistic safety assessment. Si et al [10][11][12] introduced the integrated importance measure and do some research of integrated importance measure in various situations. Based on the integrated importance measure, Dui et al [13] extended the integrated importance measure from unit time to system lifetime and to different life stages; then Dui et al [14] also studied how the transition of component states affects system performance under the semiMarkov process.…”

Section: Mathematical Problems In Engineeringmentioning

confidence: 99%

Reliability Optimization and Importance Analysis of Circular‐Consecutive k‐out‐of‐n System

Zhang

Zhao

et al. 2017

Mathematical Problems in Engineering

View full text Add to dashboard Cite

The circular-consecutive -out-of-: ( ) system (Cir/Con/ / : ( ) system) usually consists of components arranged in a circle where the system fails (works) if consecutive components fail (work). The optimization of the Cir/Con/ / system is a typical case in the component assignment problem. In this paper, the Birnbaum importance-based genetic algorithm (BIGA), which takes the advantages of genetic algorithm and Birnbaum importance, is introduced to deal with the reliability optimization for Cir/Con/ / system. The detailed process and property of BIGA are put forward at first. Then, some numerical experiments are implemented, whose results are compared with two classic Birnbaum importance-based search algorithms, to evaluate the effectiveness and efficiency of BIGA in Cir/Con/ / system. Finally, three typical cases of Cir/Con/ / systems are introduced to demonstrate the relationships among the component reliability, optimal permutation, and component importance.

show abstract

“…Kuo and Zhu [11,28] summarized the concepts of importance measures and their application in reliability and mathematical programming. Si, Dui et al [17,18] proposed the integrated importance measure (IIM) of component states, which evaluates how the transition of component states affects the system performance in multi-state systems. Si, Dui et al [19] studied the IIM from component states to the component, which can identify the most important component for improving the system performance.…”

Section: Introductionmentioning

confidence: 99%

Generalized integrated importance measure for system performance evaluation: application to a propeller plane system

Dui¹,

Chen²,

Wu³

2017

EiN

Self Cite

View full text Add to dashboard Cite

Generalized inteGrated importance measure for system performance evaluation: application to a propeller plane system uoGólniona miara zinteGrowanej ważności komponentów jako narzędzie oceny wydajności systemu: zastosowanie w odniesieniu do układu śmiGłowca : state vector of the components at time t ( ( )) X t Φ system structure function at time t and range {0,1, , [7,8] studied the IIM in system lifetime and semi-Markov process to evaluate the change of the system performance, respectively.The IIM evaluates the rate of system performance change due to a component changing from one state to another. The IIM simply considers the scenarios when the transition rate of a component from one state to another is constant. This may contradict the assumption of the degradation, based on which system performance is degrading and therefore the transition rate may be increasing over time.On the other hand, the Weibull distribution is one of the most commonly used lifetime distributions in reliability modeling and lifetime testing [25]. It has been used in many different engineering applications to model complex data sets, such as life tests [15], fault diagnosis [4,5], oral irrigators [16], et al. The Weibull distribution and its variants can accommodate increasing, constant or decreasing failure rates [10]. Thus, one may extend the IIM to a new importance measure that considers the scenarios where the transition rate of a component changing from one state to another as a time-dependent function. Typically, one may consider the conditional probability distribution of a component sojourning in a state is the Weibull distribution, given the next state that the component will jump to. On the basis of such consideration, this paper proposes a new importance measure. The research on the new importance measure can identify the most important component during three different time periods of the system lifetime, which is corresponding to the characteristics of Weibull distributions. The paper then derives some probabilistic properties. It also analyzes the properties of the proposed importance measure of the parallel-series systems and the series-parallel systems, respectively. A real-world example is borrowed to illustrate the proposed importance measure.The rest of the paper is as follows. Section 2 extends the IIM. The corresponding properties of IIM in the Weibull distribution are analyzed for typical parallel-series system and series-parallel system structures in Section 3. An application is presented to illustrate the proposed method in Section 4. Section 5 gives the conclusion of this paper. The extended IIM for system performance under Weibull distributionsIn this paper, the state space of component i is {0,1,…, M i } and the state space of the system is{0,1,…, M}. State 0 represents the complete failure state and state M (M i ) is the perfect functioning state. The states are ordered from the complete failure state to the perfect functioning state.We assume that the levels of maintenance cost and the system states ( 0 1) a...

show abstract

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

Cited by 93 publications

References 28 publications

Dynamic Importance Analysis of Components with Known Failure Contribution of Complex Systems

Dynamic Importance Analysis of Components with Known Failure Contribution of Complex Systems

Reliability Optimization and Importance Analysis of Circular‐Consecutive k‐out‐of‐n System

Generalized integrated importance measure for system performance evaluation: application to a propeller plane system

Contact Info

Product

Resources

About