This paper presents fluid flow system simulation using the MCIRQ qualitative simulator. MCIRQ was designed as an electrical simulator, however this work exploits the close analogy between fluid flow and electrical current at the level of qualitative behaviour. The core qualitative flow algorithm is applicable to both domains but there are differences in the systems structures and assumptions that require additional modelling. The concepts of multiple source networks, and explicit propagation of multiple substances through a net-work, are necessary to model important characteristics of fluid flow networks. Both of these characteristics are devel-oped on top of the MCIRQ simulator with the aim to produce an automated FMEA for aircraft fuel systems similar to pre-viously developed automated electrical FMEA.

This paper presents a structured power and energy-flow-based qualitative modelling approach that is applicable to a variety of system types including electrical and fluid flow. The modelling is split into two parts. Power flow is a global phenomenon and is therefore naturally represented and analysed by a network comprised of the relevant structural elements from the components of a system. The power flow analysis is a platform for higher-level behaviour prediction of energy related aspects using local component behaviour models to capture a state-based representation with a global time. The primary application is Failure Modes and Effects Analysis (FMEA) and a form of exaggeration reasoning is used, combined with an order of magnitude representation to derive the worst case failure modes. The novel aspects of the work are an order of magnitude(OM) qualitative network analyser to represent any power domain and topology, including multiple power sources, a feature that was not required for earlier specialised electrical versions of the approach. Secondly, the representation of generalised energy related behaviour as state-based local models is presented as a modelling strategy that can be more vivid and intuitive for a range of topologically complex applications than qualitative equation-based representations. The two-level modelling strategy allows the broad system behaviour coverage of qualitative simulation to be exploited for the FMEA task, while limiting the difficulties of qualitative ambiguity explanation that can arise from abstracted numerical models. We have used the method to support an automated FMEA system with examples of an aircraft fuel system and domestic a heating system discussed in this paper. 1.

The ASTRAEA project is a £32M UK initiative to develop the safety case for unmanned aerial vehicles flying in commercial airspace. It is addressing both the issue of what needs to be covered by such a safety case, and how such a safety case can be constructed efficiently. One of the key areas within the remit of ASTRAEA is that of generating diagnostics capable of correctly identifying the causes of all possible failures of the vehicle. This paper describes how model-based simulation can be employed to automatically generate the systemlevel effects of all possible failures on systems within the aircraft. The results of the simulation can be used in several ways. They can be used to produce a system-level FMEA for aircraft systems. They can be used to identify the sensors necessary to discriminate remotely between different failures on the aircraft. Once a set of sensors have been chosen for placement on the vehicle, the simulation results can also be used to generate diagnostic and prognostic software for deployment on the vehicle. Using the automated safety analysis software developed on the ASTRAEA project is more efficient than doing the same work without the software, and also provides a guaranteed level of performance. 1.

A new combinatorial-probabilistic diagnostic entropy has been introduced. It describes the pair-wise sum of probabilities of system conditions that have to be distinguished during the diagnosing process. The proposed measure describes the uncertainty of the system conditions, and at the same time complexity of the diagnosis problem. Treating the assumed combinatorialdiagnostic entropy as a primary notion, the information delivered by the symptoms has been defined. The relationships have been derived to facilitate explicit, quantitative assessment of the information of a single symptom as well as that of a symptoms set. It has been proved that the combinatorial-probabilistic information shows the property of additivity. The presented measures are focused on diagnosis problem, but they can be easily applied to other disciplines such as decision theory and classification. Index Terms-- entropy, fault diagnosis, information, multi-valued model, uncertainty I.