A Bayesian networks approach for event tree time-dependency analysis on phased-mission system

Abstract

Article citation info: IntroductionAmong several techniques available to model sequence and quantify the failure probability in probabilistic risk assessment (PRA), event trees (ETs) are the most recognized methods that develop logical relationship among the events leading to the possible consequences, while fault trees (FTs) best represent the logic corresponding to pivotal events (PEs) and estimate the probabilities [16].Dependencies in event tree/ fault tree (E/FT) model are frequently encountered, and, if neglected, may result in an error estimation. Hosseini and Takahashi [4] classify dependencies into two categoriesimplicit and explicit. Explicit dependencies are due to shared basic events (SBEs) such as shared utilities or shared components which appear in more than one corresponding FTs, while the expression of implicit dependencies is a bit vague. Nývlt and Rausand [13] expanded the before-mentioned division to cover more types of dependencies such as common cause failures and cascading effect, and further classified the explicit dependencies with static and dynamic behaviour. However, in practice of aerospace PRA, such as lunar exploration which has the characteristics of the phased-mission system (PMS), ETs are typically used to portray progressions of phase mission over time, and the time interval between pivotal events (PEs) is not negligible, dependencies therefore become phase-dependency (as a subset of time-dependency in this context), and make the E/FT based reliability and risk analysis more difficult [1,13].In ET analysis, not so much work has been done with time-dependency analysis, and the papers cited above are mainly based on the hypothesis about static or time-independent behaviour [1,4,13,23]. PMS reliability attracts substantial attentions, and various techniques have been developed to deal with the phase-dependency. The analytical techniques for the PMS can be classified into two categories: combinatorial models (e.g., mini-components, sum of disjoint phase products, BDD) and state-space transition models (e.g., Markov models, Petri nets) [19,21]. The combinatorial method is based on the JiA M. A Bayesian networks approach for event tree time-dependency analysis on phased-mission system. Eksploatacja i Niezawodnosc -Maintenance and Reliability 2015; 17 (2): 273-281, http://dx.doi.org/10.17531

A decision diagram based reliability evaluation method for multiple phased-mission systems

“…Step 4 A completes its task in mission Q and starts its task in mission P , meaning that phase 2 P consists of four components ( 7 A , 8 A , 9 A , 4 A ).The new system will be divided into two phases: 1) Phase 1 includes 1 P and 1 Q ; 2) phase 2 includes 2 P . For the entire system, component 4 A involves in two missions during different time periods.…”

“…Mo and Xing [9,10] built a new analytical method based on multi-valued decision diagrams for reliability analysis of non-repairable PMS with multimode failures. Li and Tao [7] combined the Bayesian networks with event tree and fault tree analysis to analyze PMS based on conditional probability by giving expression of the phase-dependency.…”

“…Fig.14 shows the MMDD model for 1 P . In phase 2 of mission P , there are four components, when component 4 A is in state '1', 9 A is in state '1' , and 7 A is in state '1' or '2', the system is normal. If component 8 A is in state '1', and 7 A is in state '1' or '2', the system is also normal.…”

Reliability assessment of repairable phased-mission system by monte carlo simulation based on modular sequence-enforcing fault tree model

“…Phased-mission systems (PMSs) are systems that perform multiple, consecutive and non-overlapping tasks [13]. Such systems are common in many fields, like power [4], spacecraft [5][6], distributed computing system [13], and military [26]. As the name suggests, the whole mission undertaken by PMS includes multiple tasks; each specified task lasts for a duration and the system has to withstand different stress loads.…”

Time-dependent reliability of spur gear system based on gradually wear process

“…As a matter of fact, wear-based reliability of mechanical product is varying gradually with time [22] . There are many methods to build time-dependent reliability model, such as Markov theory [19], dynamic fault-tree model [7,13], stochastic MCS model [5], Go-flow model [11], stochastic petri nets [20]. As mechanical structure is so complex that the methods above are hardly applied to dynamic reliability analysis for mechanical systems and components.…”