Rafal Dorociak

Integrated tool-based approach for the conceptual design of advanced mechatronic systems

This contribution addresses a new approach for the domain-spanning system specification for the design of advanced mechatronic systems. Therefore two basic methods will be introduced: a language for the holistic specification of advanced mechatronic systems and a concept for the reuse of once successfully proven solutions in form of solution patterns. For both methods software tools have been developed. Moreover, a concept for the integrated tool support for the conceptual design of advanced mechatronic systems based on the coupling of these both methods and their respective software tools will be presented.

Computer-Aided Modeling of the Principle Solution of Mechatronic Systems: A Domain-Spanning Methodology for the Conceptual Design of Mechatronic Systems

Mechatronics — the symbiotic cooperation of mechanics, electrics/electronics, control engineering and software engineering — opens up more and more fascinating perspectives for the development of future mechanical engineering products. Still, development of mechatronic systems remains a challenge. To cope with this challenge a new domain-spanning design methodology for mechatronic systems is needed. This contribution presents a design methodology, which consists of a specification technique for the domain-spanning description of the principle solution of an advanced mechatronic system, a procedure model, which defines the constituent steps of the conceptual design, their results and their order, as well as software tool, which provides means for managing the design complexity and intuitive modeling of the principle solution. The advantages of the design methodology are demonstrated in a case study from the development of an autonomous miniature robot.Copyright

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.

Securing the reliability of tomorrow's systems with Self-Optimization

The conceivable development of communication and information technology opens up more and more fascinating perspectives, which move far beyond current standards of mechatronics: mechatronic systems having inherent partial intelligence. We call these systems “Self-Optimizing Systems”. Self-Optimizing Systems can react autonomously and flexibly on changing operation conditions: During the operation of the system an endogenous adjustment of its objectives and of the corresponding behavior takes place in a closed-loop process. With Self-Optimization tomorrows mechatronic system can thus be made more reliable. In this contribution a method for the design of the system of objectives - the backbone of Self-Optimization - in the early engineering phase of the conceptual design is presented. Using the method objectives of the system, their relationships to each other and potential conflicts are identified based on the principal solution of the system. In particular, objectives are found, which may be in conflict with the objective “max. reliability” during the operation of the system. These are analyzed, recognized and mitigated and the system is made more reliable. Altogether the method supports developers by the design of the information processing of the system, which then realizes the Self-Optimization. The advantages of the method are shown in a case example from the field of railway technology - the autonomous railway vehicle RailCab.

Early probabilistic reliability analysis of mechatronic systems

Mechatronic systems are based on the close interaction of several engineering domains and are characterized by a high complexity; the assurance of their reliability is therefore very challenging. Established reliability methods are typically conducted on detailed system designs. As a consequence, a great number of failures are first recognized very late in the product engineering process. Yet: the later failures are discovered, the more does it cost to eliminate or mitigate them. Therefore there is a need for reliability analysis methods and tools, which are used from early on, so iteration loops in the system integration are avoided. In this contribution a method for the early probabilistic analysis of the reliability of an advanced mechatronic system based on its principal solution is presented; it addresses the aforementioned need. The method supports modeling of the failure propagation within the specification of the principal solution of an advanced mechatronic system, which is the result of the engineering phase of conceptual design. For analysis purposes, the model of the failure propagation is translated into a Bayesian network, which enables sophisticated probability analyses. Using our method the critical weak points of the system under consideration are identified and respective countermeasures can be defined; the principal solution is made more reliable. The advantages of the method are shown in a case study from the field of railway technology - the innovative autonomous railway vehicle RailCab.

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.