Imprecise system reliability and component importance based on survival signature

Abstract

Use policyThe full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-prot purposes provided that:• a full bibliographic reference is made to the original source • a link is made to the metadata record in DRO • the full-text is not changed in any way The full-text must not be sold in any format or medium without the formal permission of the copyright holders.Please consult the full DRO policy for further details. AbstractThe concept of the survival signature has recently attracted increasing attention for performing reliability analysis on systems with multiple types of components.It opens a new pathway for a structured approach with high computational efficiency based on a complete probabilistic description of the system. In practical applications, however, some of the parameters of the system might not be defined completely due to limited data, which implies the need to take imprecisions of component specifications into account. This paper presents a methodology to include explicitly the imprecision, which leads to upper and lower bounds of the survival function of the system. In addition, the approach introduces novel and efficient component importance measures. By implementing relative importance index of each component without or with imprecision, the most critical component in the system can be identified depending on the service time of the system. Simulation method based on survival signature is introduced to deal with imprecision within components, which is precise and efficient. Numerical example is presented to show the applicability of the approach for systems.

Mean residual life of coherent systems consisting of multiple types of dependent components

“…It should be noted that (10) can be used jointly with the results in Navarro et al () to compare different systems.Example Consider the system in Figure which has been considered in Feng, Patelli, Beer, and Coolen (). The system has six components with types with and Type 1 and type 2 components are represented respectively by blank and black boxes.…”

Reliability analysis and optimisation of subsea compression system facing operational covariate stresses

“…To check the fitness of Weibull 2-parameter modelling for analysis, a line fit plot as shown in Fig 3 between failure values and the natural log of the median is generated. Having generated a covariate parameter to represent the influence of marine conditions, next step is to estimate the overall reliability index of the SCS system using Equation 15 as shown below containing the values of the shape and scale parameters derived from the failure data: A stressed survival signature has been proven to be an effective method to estimate the survival function of systems with multiple component [42] and table 3 shows the values for both stressed and unstressed failure data using the new failure model. The contribution to unreliability by each failure data is taken into account and as a consequence, bounds of survival functions of the system and ratings of relative importance index values can be obtained using further optimization analysis.…”

“…It refers to the criticality a certain component exerts on 15 overall reliability. Mathematically, Reliability Importance (IR) is expressed as [42]. The method employed for human factor analysis in the paper is a simplification of the Barrier and Operational Risk Analysis (BORA) model by [16] which is a very comprehensive framework for modelling and optimising barriers on offshore production installations.…”

On the structure function and survival signature for system reliability

“…Coolen et al [10] illustrate nonparametric predictive inference [3,6] for the system survival function using equation (2), where the ciid assumption is not made and with the generalization to imprecise probabilities [4]. Feng et al [12] illustrate the use of the survival signature combined with known sets of probability distributions for the failure times of the components of different types, so also within theory of imprecise probability [4], and they also discuss computation of importance measures for components in the systems using the survival signature. To get further insight into the system survival function for complex systems, when analytical derivations are not feasible anymore, the ability to perform simulations efficiently is crucial.…”