902 research outputs found
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
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.
Automated system design optimisation
The focus of this thesis is to develop a generic approach for solving reliability design optimisation problems which could be applicable to a diverse range of real engineering systems. The basic problem in optimal reliability design of a system is to explore the means of improving the system reliability within the bounds of available resources. Improving the reliability reduces the likelihood of system failure. The consequences of system failure can vary from minor inconvenience and cost to significant economic loss and personal injury. However any improvements made to the system are subject to the availability of resources, which are very often limited.
The objective of the design optimisation problem analysed in this thesis is to minimise system unavailability (or unreliability if an unrepairable system is analysed) through the manipulation and assessment of all possible design alterations available, which are subject to constraints on resources and/or system performance requirements. This thesis describes a genetic algorithm-based technique developed to solve the optimisation problem. Since an explicit mathematical form can not be formulated to evaluate the objective function, the system unavailability (unreliability) is assessed using the fault tree method. Central to the optimisation algorithm are newly developed fault tree modification patterns (FTMPs). They are employed here to construct one fault tree representing all possible designs investigated, from the initial system design specified along with the design choices. This is then altered to represent the individual designs in question during the optimisation process. Failure probabilities for specified design cases are quantified by employing Binary Decision Diagrams (BDDs).
A computer programme has been developed to automate the application of the optimisation approach to standard engineering safety systems. Its practicality is demonstrated through the consideration of two systems of increasing complexity; first a High Integrity Protection System (HIPS) followed by a Fire Water Deluge System (FWDS). The technique is then further-developed and applied to solve problems of multi-phased mission systems. Two systems are considered; first an unmanned aerial vehicle (UAV) and secondly a military vessel. The final part of this thesis focuses on continuing the development process by adapting the method to solve design optimisation problems for multiple multi-phased mission systems. Its application is demonstrated by considering an advanced UAV system involving multiple multi-phased flight missions.
The applications discussed prove that the technique progressively developed in this thesis enables design optimisation problems to be solved for systems with different levels of complexity. A key contribution of this thesis is the development of a novel generic optimisation technique, embedding newly developed FTMPs, which is capable of optimising the reliability design for potentially any engineering system. Another key and novel contribution of this work is the capability to analyse and provide optimal design solutions for multiple multi-phase mission systems.
Keywords: optimisation, system design, multi-phased mission system, reliability, genetic algorithm, fault tree, binary decision diagra
A bibliography on formal methods for system specification, design and validation
Literature on the specification, design, verification, testing, and evaluation of avionics systems was surveyed, providing 655 citations. Journal papers, conference papers, and technical reports are included. Manual and computer-based methods were employed. Keywords used in the online search are listed
Modelling and Resolution of Dynamic Reliability Problems by the Coupling of Simulink and the Stochastic Hybrid Fault Tree Object Oriented (SHyFTOO) Library
Dependability assessment is one of the most important activities for the analysis of complex systems. Classical analysis techniques of safety, risk, and dependability, like Fault Tree Analysis or Reliability Block Diagrams, are easy to implement, but they estimate inaccurate dependability results due to their simplified hypotheses that assume the components’ malfunctions to be independent from each other and from the system working conditions. Recent contributions within the umbrella of Dynamic Probabilistic Risk Assessment have shown the potential to improve the accuracy of classical dependability analysis methods. Among them, Stochastic Hybrid Fault Tree Automaton (SHyFTA) is a promising methodology because it can combine a Dynamic Fault Tree model with the physics-based deterministic model of a system process, and it can generate dependability metrics along with performance indicators of the physical variables. This paper presents the Stochastic Hybrid Fault Tree Object Oriented (SHyFTOO), a Matlab® software library for the modelling and the resolution of a SHyFTA model. One of the novel features discussed in this contribution is the ease of coupling with a Matlab® Simulink model that facilitates the design of complex system dynamics. To demonstrate the utilization of this software library and the augmented capability of generating further dependability indicators, three di erent case studies are discussed and solved with a thorough description for the implementation of the corresponding SHyFTA models
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A method for temporal fault tree analysis using intuitionistic fuzzy set and expert elicitation
YesTemporal fault trees (TFTs), an extension of classical Boolean fault trees, can model time-dependent failure behaviour of dynamic systems. The methodologies used for quantitative analysis of TFTs include algebraic solutions, Petri nets (PN), and Bayesian networks (BN). In these approaches, precise failure data of components are usually used to calculate the probability of the top event of a TFT. However, it can be problematic to obtain these precise data due to the imprecise and incomplete information about the components of a system. In this paper, we propose a framework that combines intuitionistic fuzzy set theory and expert elicitation to enable quantitative analysis of TFTs of dynamic systems with uncertain data. Experts’ opinions are taken into account to compute the failure probability of the basic events of the TFT as intuitionistic fuzzy numbers. Subsequently, for the algebraic approach, the intuitionistic fuzzy operators for the logic gates of TFT are defined to quantify the TFT. On the other hand, for the quantification of TFTs via PN and BN-based approaches, the intuitionistic fuzzy numbers are defuzzified to be used in these approaches. As a result, the framework can be used with all the currently available TFT analysis approaches. The effectiveness of the proposed framework is illustrated via application to a practical system and through a comparison of the results of each approach.This work was supported in part by the Mobile IOT: Location Aware project (grant no. MMUE/180025) and Indoor Internet of Things (IOT) Tracking Algorithm Development based on Radio Signal Characterisation project (grant no. FRGS/1/2018/TK08/MMU/02/1). This research also received partial support from DEIS H2020 project (grant no. 732242)
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