The main objective of a reliability study should always be to provide information as a basis for decisions, e.g. concept choice, design requirements, investment, choice of suppliers, design changes or guaranty claims. The choice of reliability method depends on the time allocated for the reliability study, the design stage, the problem at hand and the competence and resources available.

During a reliability study the engineer focuses on providing a graphical means of evaluating the relationships between different parts of the system, gathering or assessing the reliability data for the components and interpreting the results of the analyses. Even though the commercial software tools available claim to provide answers to most reliability questions, choosing which method is best suited is not an easy task. Often several methods can be applied and none of them will fit the purpose perfectly.

This paper presents a guideline for choosing the best suited reliability method in early design phases from two aspects: objective and system characteristics. The methods studied are the most common methods available in commercial software tools: Reliability Block Diagram (RBD), Fault Tree (FT), Event Tree (ET), Markov Analysis (MA) and Stochastic Petri Network (SPN). The guideline considers two aspects: the characteristics of the system studied and the scope of the analysis. The applicability of each of the five chosen methods is assessed for all possible combinations of system characteristics and objective. A study has been made at Saab Aeronautics to evaluate the practical use of the analysed methods and how this guideline can improve the selection of appropriate reliability methods in early design phases.

When developing a safety critical system, there are many aspects that need to be balanced against each other in order to reach an optimal design such as safety requirements, reliability goal, performance specifications and budget constraints. In an early design stage, it is vital to be able to screen the design space for a set of promising design alternatives for further studies. This paper proposes an approach capable of investigating the trade-offs described above, combining the techniques for system safety and reliability analysis with optimization methods. Markov analysis is employed for modeling the system safety and reliability characteristics and a Genetic Algorithm is used for optimization. The proposed method is applied to the design of an electric supply system for an aircraft, involving selection of components from different suppliers. First a model is built for each objective, i.e. cost, safety, and reliability. The models are validated and optimization is performed. The obtained result is the selection of suppliers for each component in the system in order to achieve a balance between system safety, reliability, and other design objectives.

System Safety and Reliability are fundamental to system design and involve a quantitative assessment prior to system development. An accurate prediction of reliability and system safety in a new product before it is manufactured and marketed is necessary as it allows us to forecast accurately the support costs, warranty costs, spare parts requirements, etc. On the other hand, it can be argued that an accurate prediction implies knowledge about failures that is rarely there in early design phases. Furthermore, while predictions of system performance can be made with credible precision, within reasonable tolerances, reliability and system safety are seldom predicted with high accuracy and confidence.

How well a product meets its performance requirements depends on various characteristics such as quality, reliability, availability, safety, and efficiency. But to produce a reliable product we may have to incur increased cost of design and manufacturing. Balancing such requirements, that are often contradictory, is also a necessary step in product development. This step can be performed using different optimization techniques.

This thesis is an attempt to develop a methodology for analysis and optimization of system safety and reliability in early design phases. A theoretical framework and context are presented in the first part of the thesis, including system safety and reliability methods and optimization techniques. Each of these topics is presented in its own chapter. The second and third parts are dedicated to contributions and papers. Three papers are included in the third part; the first evaluates the applicability of reliability methods in early design phases, the second is a proposed guideline for how to choose the right reliability method, and the third suggests a method to balance the safety requirements, reliability goals, and costs.

One important challenge in the early phases of product development is to apply reliability methods for estimating the safety and reliability of the system when information about the chosen equipment and components is limited. For systems consisting of units with several degraded states, and not only “up” and “down”, the results from reliability and system safety analysis are often difficult to interpret and use. The main contribution of this paper is to evaluate the applicability of different reliability methods for analyzing an overall system concept in early development stages. Furthermore, the paper constitutes the first step of a methodology intended to address the issues outlined above from a practical point of view. In the paper, two static methods, Reliability Block Diagram and Fault Tree Analysis, and one dynamic method, Markov Analysis, have been applied to conceptual design of an aircraft electrical system. These three methods have been evaluated regarding usefulness, modeling possibilities and applicability in the conceptual design. Each method is, from a practical point of view, dependent on the limitations of the software that is used. In order to overcome this issue the calculations and partly the modeling have been performed in three different software tools.

Two iterations have been performed for Markov Analysis, and the results are used to evaluate the method regarding applicability and possibilities of modeling the system and to find out what results can be gained by extending the model.