Peter Iwanek

A methodology for the improvement of dependability of self-optimizing systems

The conceivable development of communication and information technology opens up fascinating perspectives which move far beyond current standards of mechatronics: mechatronic systems having inherent partial intelligence. We call such systems self-optimizing systems. Self-optimizing systems react autonomously and flexibly on changing environmental conditions. The design of dependable self-optimizing systems is challenging. The main reasons are the involvement of different domains and the integration of partial intelligence which leads to non-deterministic behavior. In particular, it has to be ensured that the self-optimization works dependable itself. In order to accomplish this, dependability engineering methods have to be used which are suitable to the underlying development task. In such cases the developers face a great number of methods, from which they have to manually select the appropriate ones. This selection is tedious and error-prone. In this contribution we introduce a methodology for the improvement of dependability of self-optimizing systems. It consists of a method database, a guide for selection and planning of dependability engineering methods and a software tool. The methodology supports the developers by search, selection and planning of dependability engineering methods (e.g. Fault Tree Analysis), which are suitable for their particular development task.

Methods of Improving the Dependability of Self-optimizing Systems

Various methods have been developed in the Collaborative Research Center 614 which can be used to improve the dependability of self-optimizing systems. These methods are presented in this chapter. They are sorted into two categories with regard to the development process of self-optimizing systems. On one hand, there are methods which can be applied during the Conceptual Design Phase. On the other hand, there are methods that are applicable during Design and Development.

Methods for the Domain-Spanning Conceptual Design

The development of self-optimizing systems is a highly interdisciplinary task, as several domains are involved. Existing design methodologies do not adress this issue, as they focus on the respective domain; a holistic domain-spanning consideration of the system occurs - if at all - only rudimentally. The partial solutions developed by the respective domains may be optimal from the point of view of this domain. However, it does not automatically mean, that the sum of the optimal domain-specific solutions forms the best possible overall solution: ”the whole is more than the sum of its parts”. This especially holds true for the early design phase, the conceptual design. Its result is the so-called principle solution, which is further refined in the domain-specific design and development. Thus, a great need for methods arises which support the domain-spanning conceptual design for self-optimizing systems in a holistic manner. In this chapter we will introduce such methods. In particular, we will explain the specification technique for the domain-spanning description of the principle solution of a self-optimizing system. Furthermore, methods are explained which support the creation of the principle solution. This includes a method to ensure the consistency of application scenarios, a method for the design of the system of objectives, which is crucial for a self-optimizing system, as well as a method for the re-use of proven solutions for recurring problems (solution patterns). Finally, some analysis methods are explained that are performed on the specification of the principle solution. These are: the early analysis of the reliability and the analysis of the economic efficiency.

The Paradigm of Self-optimization

Machines are ubiquitous. They produce, they transport. Machines facilitate and assist with work. The increasing fusion of mechanical engineering with information technology has brought about considerable benefits. This situation is expressed by the term mechatronics, which means the close interaction of mechanics, electrics/electronics, control engineering and software engineering to improve the behavior of a technical system. The integration of cognitive functions into mechatronic systems enables systems to have inherent partial intelligence. The behavior of these future systems is formed by the communication and cooperation of the intelligent system elements. From an information processing point of view, we consider these distributed systems to be multi-agent-systems. These capabilities open up fascinating prospects regarding the design of future technical systems. The term self-optimization characterizes this perspective: the endogenous adaptation of the system’s objectives due to changing operational conditions. This resuls in an autonomous adjustment of system parameters or system structure and consequently of the system’s behavior. In this chapter self-optimizing systems are described in detail. The long term aim of the Collaborative Research Centre 614 ”Self-Optimizing Concepts and Structures in Mechanical Engineering” is to open up the active paradigm of self-optimization for mechanical engineering and to enable others to develop these systems. For this, developers have to face a number of challenges, e.g. the multidisciplinarity and the complexity of the system. This book povides a design methodology that helps to master these challenges and to enable third parties to develop self-optimizing systems by themselves.

Introduction to Self-optimization and Dependability

This chapter gives an introduction to self-optimizing mechatronic systems and the risks and possibilities that arise with these. Self-optimizing mechatronic systems have capabilities that go far beyond those of traditional mechatronic systems. They are able to autonomously adapt their behavior and so react to outer influences, which can originate e.g. from the environment, changed user requirements or the current system status. The basic process of self-optimization, the procedures employed within and the main components of a self-optimizing system are explained here.