Phased mission system design optimisation using genetic algorithms

A phased mission system represents a system whose performance is divided into consecutive non-overlapping phases. It is important to ensure safety of a phased mission system since the failure of it can have both life threatening and financial consequences. The focus of this paper is to develop an optimisation method to construct an optimal design case for a phased mission system, with the aim of minimising its unreliability and at the same time ensuring optimal usage of available resources throughout all phases. The introduced phased mission optimisation is represented as the constrained single objective problem. Here failure of the overall mission is the objective function and the introduced constraints are employed to determine the optimal use of resources. The implemented optimisation method employs Fault Tree Analysis to represent system performance and Binary Decision Diagrams to quantify each phase failure probability. A single objective Genetic Algorithm has been chosen as the optimisation technique. An Unmanned Aerial Vehicle mission has been selected to demonstrate the methods application. The results and the influence of modifications to the optimisation algorithm are discussed

High performance reliability analysis of phased mission systems

Systems often operate over a set of time periods, known as phases, in which their reliability structure varies and many include both repairable and nonrepairable components. Success for such systems is defined as the completion of all phases, known as a phased mission, without failure. An example of such a system is an aircraft landing gear system during a flight. The Binary Decision Diagram (BDD) method provides the most efficient solution to the unreliability of non-repairable systems whilst for repairable systems Markov or other state-space based methods have been most widely applied. For systems containing both repairable and non-repairable components the repairable modelling methods are normally used, despite having far higher computational expense than the non-repairable methods, since only they are able to handle the dependencies involved. This paper introduces improvements to the BDD method for analysing non-repairable systems as well as an entirely new method that utilises a new modelling technique involving both BDD and Markov techniques

A Data-Driven Reliability Estimation Approach for Phased-Mission Systems

We attempt to address the issues associated with reliability estimation for phased-mission systems (PMS) and present a novel data-driven approach to achieve reliability estimation for PMS using the condition monitoring information and degradation data of such system under dynamic operating scenario. In this sense, this paper differs from the existing methods only considering the static scenario without using the real-time information, which aims to estimate the reliability for a population but not for an individual. In the presented approach, to establish a linkage between the historical data and real-time information of the individual PMS, we adopt a stochastic filtering model to model the phase duration and obtain the updated estimation of the mission time by Bayesian law at each phase. At the meanwhile, the lifetime of PMS is estimated from degradation data, which are modeled by an adaptive Brownian motion. As such, the mission reliability can be real time obtained through the estimated distribution of the mission time in conjunction with the estimated lifetime distribution. We demonstrate the usefulness of the developed approach via a numerical example

A Stochastic Hybrid Systems Framework for Analysis of Markov Reward Models

In this paper, we propose a framework to analyze Markov reward models, which are commonly used in system performability analysis. The framework builds on a set of analytical tools developed for a class of stochastic processes referred to as “Stochastic Hybrid Systems (SHS).” The state space of an SHS is composed of: i) a discrete state that describes the possible configurations/modes that a system can adopt, which includes the nominal (non-faulty) operational mode, but also those operational modes that arise due to component faults, and ii) a continuous state that describes the reward. Discrete state transitions are stochastic, and governed by transition rates that are (in general) a function of time and the value of the continuous state. The evolution of the continuous state is described by a stochastic differential equation, and reward measures are defined as functions of the continuous state. Additionally, each transition is associated with a reset map that defines the mapping between the pre- and post-transition values of the discrete and continuous states; these mappings enable the definition of impulses and losses in the reward. The proposed SHS-based framework unifies the analysis of a variety of previously studied reward models. We illustrate the application of the framework to performability analysis via analytical and numerical examples.National Science Foundation / CMG-0934491Ope

Methods for the efficient measurement of phased mission system reliability and component importance

An increasing number of systems operate over a number of consecutive time periods, in which their reliability structure and the consequences of failure differ, in order to perform some overall operation. Each distinct time period is known as a phase and the overall operation is known as a phased mission. Generally, a phased mission fails immediately if the system fails at any point and is considered a success only if all phases are completed without failure. The work presented in this thesis provides efficient methods for the prediction and optimisation of phased mission reliability. A number of techniques and methods for the analysis of phased mission reliability have been previously developed. Due to the component and system failure time dependencies introduced by the phases, the computational expense of these methods is high and this limits the size of the systems that can be analysed in reasonable time frames on modern computers. Two importance measures, which provide an index of the influence of each component on the system reliability, have also been previously developed. This is useful for the optimisation of the reliability of a phased mission, however a much larger number have been developed for non-phased missions and the different perspectives and functions they provide are advantageous. This thesis introduces new methods as well as improvements and extensions to existing methods for the analysis of both non-repairable and repairable systems with an emphasis on improved efficiency in the derivation of phase and mission reliability. New importance measures for phased missions are also presented, including interpretations of those currently available for non-phased missions. These provide a number of interpretations of component importance, allowing those most suitable in a given context to be employed and thus aiding in the optimisation of mission reliability. In addition, an extensive computer code has been produced that implements and tests the majority of the newly developed techniques and methods.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Methods for the efficient measurement of phased mission system reliability and component importance

An increasing number of systems operate over a number of consecutive time periods, in which their reliability structure and the consequences of failure differ, in order to perform some overall operation. Each distinct time period is known as a phase and the overall operation is known as a phased mission. Generally, a phased mission fails immediately if the system fails at any point and is considered a success only if all phases are completed without failure. The work presented in this thesis provides efficient methods for the prediction and optimisation of phased mission reliability. A number of techniques and methods for the analysis of phased mission reliability have been previously developed. Due to the component and system failure time dependencies introduced by the phases, the computational expense of these methods is high and this limits the size of the systems that can be analysed in reasonable time frames on modern computers. Two importance measures, which provide an index of the influence of each component on the system reliability, have also been previously developed. This is useful for the optimisation of the reliability of a phased mission, however a much larger number have been developed for non-phased missions and the different perspectives and functions they provide are advantageous. This thesis introduces new methods as well as improvements and extensions to existing methods for the analysis of both non-repairable and repairable systems with an emphasis on improved efficiency in the derivation of phase and mission reliability. New importance measures for phased missions are also presented, including interpretations of those currently available for non-phased missions. These provide a number of interpretations of component importance, allowing those most suitable in a given context to be employed and thus aiding in the optimisation of mission reliability. In addition, an extensive computer code has been produced that implements and tests the majority of the newly developed techniques and methods.EThOS - Electronic Theses Online ServiceGBUnited Kingdo