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Methods of Improving the Dependability of Self-optimizing Systems

  • Rafal Dorociak
  • Juergen Gausemeier
Part of the Lecture Notes in Mechanical Engineering book series (LNME)

Abstract

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.

There are domain-spanning methods as well as methods that have been specifically developed for particular domains, e.g., software engineering or control engineering. The methods address different attributes of dependability, such as reliability, availability or safety.

Each section is prefaced with a short overview of the classification of the described method regarding the corresponding domain(s), as well as its dependability attributes, to provide the reader with a brief outline of the methods’ areas of application. Information about independently applicable methods or existing relationships and interactions with other methods or third-party literature is also provided.

The development process for self-optimizing mechatronic systems which was introduced in Chap. 2 consists of two main phases: Conceptual Design and Design and Development. The main result of the Conceptual Design is the Principle Solution, which includes all information required for the concrete development during the second phase.

Keywords

Virtual Machine Bayesian Network Pareto Front Multiobjective Optimization Mechatronic System 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  1. 1.Product Engineering, Heinz Nixdorf InstituteUniversity of PaderbornPaderbornGermany

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