Functional Reasoning and Functional Modelling

A car that will not start on a cold winter day and one that will not start on a hot summer day usually indicate two very different situations. When pressed to explain the difference, we would give a winter account- Oil is more viscous in cold conditions, and that causes . . .\u27\u27 -and a summer story- Vapor lock is a possibility in hot weather and is usually caused by . . .\u27\u27 How do we build such explanations? One possibility is that understanding how the car works as a device gives us a basis for generating the explanations. But that raises another question: how do people understand devices? Model-based reasoning is a subfield of artificial intelligence focusing on device understanding issues. In any model-based-reasoning approach, the goal is to model\u27\u27 a device in the world as a computer program. Unfortunately, model\u27\u27 is a loaded term-different listeners understand the word to mean very different concepts. By extrapolation, model-based reasoning\u27\u27 can suggest several different approaches, depending on the embedded meaning of model.\u27\u2

Functional Modelling for Fault Diagnosis and its application for NPP.

The paper presents functional modelling and its application for diagnosis in nuclear power plants. Functional modelling is defined and its relevance for coping with the complexity of diagnosis in large scale systems like nuclear plants is explained. The diagnosis task is analyzed and it is demonstrated that the levels of abstraction in models for diagnosis must reflect plant knowledge about goals and functions which is represented in functional modelling. Multilevel flow modelling (MFM), which is a method for functional modelling, is introduced briefly and illustrated with a cooling system example. The use of MFM for reasoning about causes and consequences is explained in detail and demonstrated using the reasoning tool, the MFMSuite. MFM applications in nuclear power systems are described by two examples: a PWR; and an FBR reactor. The PWR example show how MFM can be used to model and reason about operating modes. The FBR example illustrates how the modelling development effort can be managed by proper strategies including decomposition and reuse

Using Abstraction in Modular Verification of Synchronous Adaptive Systems

Self-adaptive embedded systems autonomously adapt to changing environment conditions to improve their functionality and to increase their dependability by downgrading functionality in case of fail- ures. However, adaptation behaviour of embedded systems significantly complicates system design and poses new challenges for guaranteeing system correctness, in particular vital in the automotive domain. Formal verification as applied in safety-critical applications must therefore be able to address not only temporal and functional properties, but also dynamic adaptation according to external and internal stimuli. In this paper, we introduce a formal semantic-based framework to model, specify and verify the functional and the adaptation behaviour of syn- chronous adaptive systems. The modelling separates functional and adap- tive behaviour to reduce the design complexity and to enable modular reasoning about both aspects independently as well as in combination. By an example, we show how to use this framework in order to verify properties of synchronous adaptive systems. Modular reasoning in com- bination with abstraction mechanisms makes automatic model checking efficiently applicable

Feature-based validation reasoning for intent-driven engineering design

Feature based modelling represents the future of CAD systems. However, operations such as modelling and editing can corrupt the validity of a feature-based model representation. Feature interactions are a consequence of feature operations and the existence of a number of features in the same model. Feature interaction affects not only the solid representation of the part, but also the functional intentions embedded within features. A technique is thus required to assess the integrity of a feature-based model from various perspectives, including the functional intentional one, and this technique must take into account the problems brought about by feature interactions and operations. The understanding, reasoning and resolution of invalid feature-based models requires an understanding of the feature interaction phenomena, as well as the characterisation of these functional intentions. A system capable of such assessment is called a feature-based representation validation system. This research studies feature interaction phenomena and feature-based designer's intents as a medium to achieve a feature-based representation validation system. [Continues.

Failure modes and effects analysis through knowledge modelling

Failure Mode and Effect Analysis (FMEA) is a widely used quality improvement and risk assessment tool in manufacturing. Design and process failures recorded through FMEA provides valuable knowledge for future product and process design. However, the way the knowledge is captured poses considerable difficulties for reuse. This research aims to contribute to the reuse of FMEA knowledge through a knowledge modelling approach. FMEA activities are shifted to the conceptual design stage to avoid costly and difficult design changes at later stages of the design process. An object-oriented approach has been used to create an FMEA model. Functional diagrams have been used for the conceptual model. The FMEA model uses functional reasoning techniques to enable automatic FMEA generation from historical data. The reasoning technique also provides a means for the creation of new knowledge. The automatic generation replaces the traditional brainstorming process for FMEA report creation. The sources of the historical data can be from the previous FMEA, failure reports or from the individual designers

RE-PREF : support for REassessment of PREFerences of non-functional requirements for better decision-making in self-adaptive systems

Modelling and reasoning with prioritization of non-functional requirements (NFRs) is a research field that needs more attention. We demonstrate RE-PREF, an approach that supports the modelling of NFRs and their preferences, and discovery of possible scenarios where badly chosen preferences can either make the runtime system miss or suggest unnecessary adaptations that may degrade the behavior of a self-adaptive system (SAS). Specifically, we showcase how RE-PREF is used in a remote data mirroring (RDM) system. The model of NFRs and the analysis of their preferences are enabled by using dynamic decision network (DDNs) and Bayesian Surprise

Challenges for qualitative electrical reasoning in automotive circuit simulation

Qualitative reasoning about electrical systems has reached a level of achievement which allows it to be used for applications on realistic automotive circuits. The type of circuits for which it is most effective can be characterised as circuits with a single steady state for each combination of inputs. Many automotive circuits with more complex overall behaviour can be approximated using this type of modelling by representing the behaviour of more complex components only at a functional level, or by judicious use of simplifying assumptions. This paper will consider examples of circuitry in modern cars where such approximations of behaviour are unsatisfactory, and will examine the modelling issues that are thrown up by these cases, in order to identify challenges for qualitative electrical reasoning against which future advances in the field can be assessed

Morphological and volumetrical feature-based designer's intents

Features are claimed to be the carriers of Designer's Intents (DI's) which are seldom defined, identified and represented in Design-by-Features (DbF) systems. This paper presents an interpretation of Designer's Intents for the Feature-based Modelling (FBM) context and emphasis will be given to the Morphological Functional and Volumetrical Geometrical DI’s which express the basic behaviour of a DbF system. DI's are also an important part of a validation system capable of reasoning about the semantics of using features in a particular design. If features' characterisations via DI's are well established and measurable the representation could be assessed as to its conformity with feature's meaning and their semantics could be validated. It is considered that the better Designer's Intents are understood and specified, the more useful Feature-based Modelling will become

Semantic model-driven development of service-centric software architectures

Service-oriented architecture (SOA) is a recent architectural paradigm that has received much attention. The prevalent focus on platforms such as Web services, however, needs to be complemented by appropriate software engineering methods. We propose the model-driven development of service-centric software systems. We present in particular an investigation into the role of enriched semantic modelling for a modeldriven development framework for service-centric software systems. Ontologies as the foundations of semantic modelling and its enhancement through architectural pattern modelling are at the core of the proposed approach. We introduce foundations and discuss the benefits and also the challenges in this context