Functional safety analysis (FSA), that is checking whether a designed artifact will perform safely even under the presence of failing components, has gained significant importance in different areas, including aeronautic and automotive systems. The same applies to failure-modes-and-effects analysis (FMEA) and fault-tree analysis (FTA) as the major contributing processes. FSA is labor- and time-consuming as well as error- prone, and would benefit from computer-based tool-support. Work on qualitative model-based systems has developed principled solutions, particularly to FMEA, but did not achieve the step to industrial practice. Rather than novel technical contributions, this paper discusses reasons for this fact and describes the qSafe* project, which aims at overcoming the obstacles and at making a major step towards producing tools that can support current practice.

This paper presents a methodology for model-based fault localization and identification that exploits both numerical (Modelica) models and a qualitative model-based approach to diagnosis. It has been applied to diagnosis of an air handling unit based on data recorded by a building management system. The main steps from model development to diagnosis based on the recorded data are discussed.

The paper describes a model-based approach to developing a general tool for localizing faults in applications of data warehouse technology. A model of the application is configured from a library of generic models of standard (types of) modules and exploited by a consistency-based diagnosis algorithm, originally used for diagnosing physical devices. Observing intermediate results can require high efforts or even be impossible, which limits the discriminability between different faults in a sequence of data processing steps. To compensate for this, fault models are used. This becomes a feasible solution for standard modules of a data warehouse application along with a stratification of the data. Fault models capture the potential impact of faults of process steps and data transfer on the data strata as well as on sets of data. Reflecting the nature of the initial symptoms and of the potential checks, these descriptions are stated at a qualitative level. The solution has been validated in customer report generation of a provider of mobile phone services.

The six papers in this special issue cover different approaches and different applications to model-based diagnostics.

We present models of various elements of a plant that involves the transportation of lumped material. An application context is provided by a project on diagnosing disturbances in food packaging plants and, more specifically, bottling plants. While there exist models of flow of homogeneous matters, such as liquid material in a hydraulic system, based on simultaneous equations of Kirchhoff/Ohm type, in our project we need to cope with non-negligible transportation time of objects and capture phenomena like the tailback of units (if transportation is blocked) or the propagation of gaps in the flow of units. Because the application context requires compositionality of the model, i.e. local, context-free models of the individual transportation elements, we are also facing the problem that whether or not a single element produces an output flow (or accepts an input flow) cannot be determined solely by the model of this element, but only through modeling the interaction with the subsequent element, which may block the output (or the previous one not providing the input). This issue is addressed by modeling the potential of an existing flow distinctly from the actual occurrence of a flow, an idea which also can enhance models of continuous flow.