The safety case and the lessons learned for the reliability and maintainability case

This paper examine the safety case and the lessons learned for the reliability and maintainability case

Complex System Reliability Analysis Method: Goal‐Oriented Methodology

Goal‐oriented (GO) methodology is a success‐oriented method for complex system reliability analysis based on modeling the normal operating sequence of a system and all possible system states. Recently, GO method has been applied in reliability and safety analysis of a number of systems, spanning defense, transportation, and power systems. This chapter provides a new approach for reliability analysis of complex systems, first, by providing its development history, its engineering applications, and the future directions. Then, the basic theory of GO method is expounded. Finally, the comparison of GO method, fault tree analysis and Monte‐Carlo simulation is discussed

Reliability analysis of complex technical systems using the fault tree modularization technique

Originally presented as the first author's thesis, (Ph. D.)--in the M.I.T. Dept. of Nuclear Engineering, 1980Includes bibliographical reference

System design optimisation involving phased missions

The performance of a phased mission is defined as a succession of non-overlapping phases that constitute towards a continuous mission. The focus of this paper is to develop a method to construct an optimal design structure for a phased mission system when available resources are restricted and to ensure a minimal system failure probability throughout the whole mission. The implemented optimisation method employs fault tree analysis to represent the causes of failure in the system for each phase. Binary decision diagrams are used to quantify the failure probability of each phase and the whole mission, and a single objective genetic algorithm is chosen to solve the optimisation problem. Analysis of the optimisation process of a military vessel design during a training mission is presented and the obtained results are discussed

A hybrid load flow and event driven simulation approach to multi-state system reliability evaluation

Structural complexity of systems, coupled with their multi-state characteristics, renders their reliability and availability evaluation difficult. Notwithstanding the emergence of various techniques dedicated to complex multi-state system analysis, simulation remains the only approach applicable to realistic systems. However, most simulation algorithms are either system specific or limited to simple systems since they require enumerating all possible system states, defining the cut-sets associated with each state and monitoring their occurrence. In addition to being extremely tedious for large complex systems, state enumeration and cut-set definition require a detailed understanding of the system׳s failure mechanism. In this paper, a simple and generally applicable simulation approach, enhanced for multi-state systems of any topology is presented. Here, each component is defined as a Semi-Markov stochastic process and via discrete-event simulation, the operation of the system is mimicked. The principles of flow conservation are invoked to determine flow across the system for every performance level change of its components using the interior-point algorithm. This eliminates the need for cut-set definition and overcomes the limitations of existing techniques. The methodology can also be exploited to account for effects of transmission efficiency and loading restrictions of components on system reliability and performance. The principles and algorithms developed are applied to two numerical examples to demonstrate their applicability

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.

Uncertainty in Engineering

This open access book provides an introduction to uncertainty quantification in engineering. Starting with preliminaries on Bayesian statistics and Monte Carlo methods, followed by material on imprecise probabilities, it then focuses on reliability theory and simulation methods for complex systems. The final two chapters discuss various aspects of aerospace engineering, considering stochastic model updating from an imprecise Bayesian perspective, and uncertainty quantification for aerospace flight modelling. Written by experts in the subject, and based on lectures given at the Second Training School of the European Research and Training Network UTOPIAE (Uncertainty Treatment and Optimization in Aerospace Engineering), which took place at Durham University (United Kingdom) from 2 to 6 July 2018, the book offers an essential resource for students as well as scientists and practitioners

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

Improved dynamic dependability assessment through integration with prognostics

The use of average data for dependability assessments results in a outdated system-level dependability estimation which can lead to incorrect design decisions. With increasing availability of online data, there is room to improve traditional dependability assessment techniques. Namely, prognostics is an emerging field which provides asset-specific failure information which can be reused to improve the system level failure estimation. This paper presents a framework for prognostics-updated dynamic dependability assessment. The dynamic behaviour comes from runtime updated information, asset inter-dependencies, and time-dependent system behaviour. A case study from the power generation industry is analysed and results confirm the validity of the approach for improved near real-time unavailability estimations

An efficient phased mission reliability analysis for autonomous vehicles

Autonomous systems are becoming more commonly used, especially in hazardous situations. Such systems are expected to make their own decisions about future actions when some capabilities degrade due to failures of their subsystems. Such decisions are made without human input, therefore they need to be well-informed in a short time when the situation is analysed and future consequences of the failure are estimated. The future planning of the mission should take account of the likelihood of mission failure. The reliability analysis for autonomous systems can be performed using the methodologies developed for phased mission analysis, where the causes of failure for each phase in the mission can be expressed by fault trees. Unmanned autonomous vehicles (UAVs) are of a particular interest in the aeronautical industry, where it is a long term ambition to operate them routinely in civil airspace. Safety is the main requirement for the UAV operation and the calculation of failure probability of each phase and the overall mission is the topic of this paper. When components or subsystems fail or environmental conditions throughout the mission change, these changes can affect the future mission. The new proposed methodology takes into account the available diagnostics data and is used to predict future capabilities of the UAV in real time. Since this methodology is based on the efficient BDD method, the quickly provided advice can be used in making decisions. When failures occur appropriate actions are required in order to preserve safety of the autonomous vehicle. The overall decision making strategy for autonomous vehicles is explained in this paper. Some limitations of the methodology are discussed and further improvements are presented based on experimental results