Phased mission analysis using the cause–consequence diagram method

Most reliability analysis techniques and tools assume that a system used for a mission consists of a single phase. However, multiple phases are natural in many missions. A system that can be modelled as a mission consisting of a sequence of phases is called a phased mission system. In this case, for successful completion of each phase the system may have to meet different requirements. System failure during any phase will result in mission failure. Fault tree analysis, binary decision diagrams and Markov techniques have been used to model phased missions. The cause–consequence diagram method is an alternative technique capable of modelling all system outcomes (success and failure) in one logic diagram. [Continues.

Systems reliability for phased missions

The concept of a phased mission has been introduced as a sequential set of objectives that operate over different time intervals. During each phase of the mission, the system may alter such that the logic model, system configuration, or system failure characteristics may change to accomplish a required objective. A new fault tree method has been proposed to enable the probability of failure in each phase to be determined in addition to the whole mission unreliability. Phase changes are assumed to be instantaneous, and component failure rates are assumed to be constant through the mission. For any phase, the method combines the causes of success of previous phases with the causes of failure for the phase being considered to allow both qualitative and quantitative analysis of both phase and mission failure. A new set of Boolean laws is introduced to combine component success and failure events through multiple phases so that the expression for each phase failure can be reduced into minimal form. [Continues.

Management issues in systems engineering

When applied to a system, the doctrine of successive refinement is a divide-and-conquer strategy. Complex systems are sucessively divided into pieces that are less complex, until they are simple enough to be conquered. This decomposition results in several structures for describing the product system and the producing system. These structures play important roles in systems engineering and project management. Many of the remaining sections in this chapter are devoted to describing some of these key structures. Structures that describe the product system include, but are not limited to, the requirements tree, system architecture and certain symbolic information such as system drawings, schematics, and data bases. The structures that describe the producing system include the project's work breakdown, schedules, cost accounts and organization

Constellation Probabilistic Risk Assessment (PRA): Design Consideration for the Crew Exploration Vehicle

Managed by NASA's Office of Safety and Mission Assurance, a pilot probabilistic risk analysis (PRA) of the NASA Crew Exploration Vehicle (CEV) was performed in early 2006. The PRA methods used follow the general guidance provided in the NASA PRA Procedures Guide for NASA Managers and Practitioners'. Phased-mission based event trees and fault trees are used to model a lunar sortie mission of the CEV - involving the following phases: launch of a cargo vessel and a crew vessel; rendezvous of these two vessels in low Earth orbit; transit to th$: moon; lunar surface activities; ascension &om the lunar surface; and return to Earth. The analysis is based upon assumptions, preliminary system diagrams, and failure data that may involve large uncertainties or may lack formal validation. Furthermore, some of the data used were based upon expert judgment or extrapolated from similar components~systemsT. his paper includes a discussion of the system-level models and provides an overview of the analysis results used to identify insights into CEV risk drivers, and trade and sensitivity studies. Lastly, the PRA model was used to determine changes in risk as the system configurations or key parameters are modified

Method and system for dynamic probabilistic risk assessment

The DEFT methodology, system and computer readable medium extends the applicability of the PRA (Probabilistic Risk Assessment) methodology to computer-based systems, by allowing DFT (Dynamic Fault Tree) nodes as pivot nodes in the Event Tree (ET) model. DEFT includes a mathematical model and solution algorithm, supports all common PRA analysis functions and cutsets. Additional capabilities enabled by the DFT include modularization, phased mission analysis, sequence dependencies, and imperfect coverage

Performability modeling with continuous accomplishment sets

A general modeling framework that permits the definition, formulation, and evaluation of performability is described. It is shown that performability relates directly to system effectiveness, and is a proper generalization of both performance and reliability. A hierarchical modeling scheme is used to formulate the capability function used to evaluate performability. The case in which performance variables take values in a continuous accomplishment set is treated explicitly

An Integrated Approach to Life Cycle Analysis

Life Cycle Analysis (LCA) is the evaluation of the impacts that design decisions have on a system and provides a framework for identifying and evaluating design benefits and burdens associated with the life cycles of space transportation systems from a "cradle-to-grave" approach. Sometimes called life cycle assessment, life cycle approach, or "cradle to grave analysis", it represents a rapidly emerging family of tools and techniques designed to be a decision support methodology and aid in the development of sustainable systems. The implementation of a Life Cycle Analysis can vary and may take many forms; from global system-level uncertainty-centered analysis to the assessment of individualized discriminatory metrics. This paper will focus on a proven LCA methodology developed by the Systems Analysis and Concepts Directorate (SACD) at NASA Langley Research Center to quantify and assess key LCA discriminatory metrics, in particular affordability, reliability, maintainability, and operability. This paper will address issues inherent in Life Cycle Analysis including direct impacts, such as system development cost and crew safety, as well as indirect impacts, which often take the form of coupled metrics (i.e., the cost of system unreliability). Since LCA deals with the analysis of space vehicle system conceptual designs, it is imperative to stress that the goal of LCA is not to arrive at the answer but, rather, to provide important inputs to a broader strategic planning process, allowing the managers to make risk-informed decisions, and increase the likelihood of meeting mission success criteria

Interoperability between a dynamic reliability modeling and a Systems Engineering process – Principles and Case Study

International audienceIndustrial systems are often large, and complex, in terms of structure, dynamic interactions between subsystems and components, dynamic operational environment, ageing, etc. The dynamic reliability approach is a convenient framework to model the behavior of such systems. However, there is a price to pay, e.g. in terms of amount of data, size of state graphs, volume of reliability calculations, and combination of various engineering activities. A sound Systems Engineering process, benefiting from the improvement of most recent tools, may be a fruitful approach to decrease these difficulties. Although feasibility demonstrations have been done for conventional, static, approaches of dependability, interoperability between dynamic reliability modeling and Systems Engineering has not the same maturity level. The article explains how, on the basis of Systems Engineering (SE) process definitions, a Meta-model defines a framework for integrating the safety into SE processes. It supports a "hub automaton", that is the key element for interoperability with the tools and activities required for a dynamic reliability assessment. The case study is the dynamic assessment of availability of a feed-water control system in a power plant steam generator, presented in previous articles

Reliability Analysis of Correlated Competitive and Dependent Components Considering Random Isolation Times

Funding Information: Funding Statement: This work was supported by the National Natural Science Foundation of China (NSFC) (Grant No. 62172058) and the Hunan Provincial Natural Science Foundation of China (Grant Nos. 2022JJ10052, 2022JJ30624). Publisher Copyright: © 2023 Tech Science Press. All rights reserved.Peer reviewedPublisher PD