The electronic version of my PhD thesis is available free of charge.
You can also purchase a hardcopy at amazon or on the publisher's web-page ;)
or on the google books.
Error propagation analysis is an important part of a system development process. This thesis addresses a probabilistic description of the spreading of data errors through a mechatronic system. An error propagation model for these types of systems must use a high abstraction layer that allows the proper mapping of the mutual interaction of heterogeneous system components such as software, hardware, and physical parts.
A literature overview reveals the most appropriate error propagation model that is based on Markovian representation of control flow. However, despite the strong probabilistic background, this model has a significant disadvantage. It implies that data errors always propagate through the control flow. This assumption limits model application to the systems, in which components can be triggered in arbitrary order with non-sequential data flow.
A motivational example, discussed in this thesis, shows that control and data flows must be considered separately for an accurate description of an error propagation process. For this reason, a new concept of system analysis is introduced. The central idea is a synchronous examination of two directed graphs: a control flow graph and a data flow graph. The structures of these graphs can be derived systematically during system development. The knowledge about an operational profile and properties of individual system components allow the definition of additional parameters of the error propagation model.
A discrete time Markov chain is applied for the modeling of faults activation, errors propagation, and errors detection during operation of the system. A state graph of this Markov chain can be generated automatically using the discussed dual-graph representation. A specific approach to computation of this Markov chain makes it possible to obtain the probabilities of all erroneous and error-free system execution scenarios. This information plays a valuable role in development of dependable systems. For instance, it can help to define an effective testing strategy, to perform accurate reliability estimation, and to speed up error detection and fault localization processes.
This thesis contains a comprehensive description of a mathematical frame- work of the new dual-graph error propagation model, several methods for error propagation analysis, and a case study that demonstrates key features of the application of the presented error propagation model to a typical mecha- tronic system. A numerical evaluation of the mechatronic system in question proves applicability of the introduced concept.