Abductive and Consistency-Based Diagnosis Revisited: a Modeling Perspective

Diagnostic reasoning has been characterized logically as consistency-based reasoning or abductive reasoning. Previous analyses in the literature have shown, on the one hand, that choosing the (in general more restrictive) abductive definition may be appropriate or not, depending on the content of the knowledge base [Console&Torasso91], and, on the other hand, that, depending on the choice of the definition the same knowledge should be expressed in different form [Poole94]. Since in Model-Based Diagnosis a major problem is finding the right way of abstracting the behavior of the system to be modeled, this paper discusses the relation between modeling, and in particular abstraction in the model, and the notion of diagnosis.Comment: 5 pages, 8th Int. Workshop on Nonmonotonic Reasoning, 200

On the construction of hierarchic models

One of the main problems in the field of model-based diagnosis of technical systems today is finding the most useful model or models of the system being diagnosed. Often, a model showing the physical components and the connections between them is all that is available. As systems grow larger and larger, the run-time performance of diagnostic algorithms decreases considerably when using these detailed models. A solution to this problem is using a hierarchic model. This allows us to first diagnose the system using an abstract model, and then use this solution to guide the diagnostic process using a more detailed model. The main problem with this approach is acquiring the hierarchic model. We give a generic hierarchic diagnostic algorithm and show how the use of certain classes of hierarchic models can increase the performance of this algorithm. We then present linear time algorithms for the automatic construction of these hierarchic models, using the detailed model and extra information about cost of probing points and invertibility of components

Diagnostic reasoning techniques for selective monitoring

An architecture for using diagnostic reasoning techniques in selective monitoring is presented. Given the sensor readings and a model of the physical system, a number of assertions are generated and expressed as Boolean equations. The resulting system of Boolean equations is solved symbolically. Using a priori probabilities of component failure and Bayes' rule, revised probabilities of failure can be computed. These will indicate what components have failed or are the most likely to have failed. This approach is suitable for systems that are well understood and for which the correctness of the assertions can be guaranteed. Also, the system must be such that changes are slow enough to allow the computation

Model-based reconfiguration: Diagnosis and recovery

We extend Reiter's general theory of model-based diagnosis to a theory of fault detection, identification, and reconfiguration (FDIR). The generality of Reiter's theory readily supports an extension in which the problem of reconfiguration is viewed as a close analog of the problem of diagnosis. Using a reconfiguration predicate 'rcfg' analogous to the abnormality predicate 'ab,' we derive a strategy for reconfiguration by transforming the corresponding strategy for diagnosis. There are two obvious benefits of this approach: algorithms for diagnosis can be exploited as algorithms for reconfiguration and we have a theoretical framework for an integrated approach to FDIR. As a first step toward realizing these benefits we show that a class of diagnosis engines can be used for reconfiguration and we discuss algorithms for integrated FDIR. We argue that integrating recovery and diagnosis is an essential next step if this technology is to be useful for practical applications

CBR and MBR techniques: review for an application in the emergencies domain

The purpose of this document is to provide an in-depth analysis of current reasoning engine practice and the integration strategies of Case Based Reasoning and Model Based Reasoning that will be used in the design and development of the RIMSAT system. RIMSAT (Remote Intelligent Management Support and Training) is a European Commission funded project designed to: a.. Provide an innovative, 'intelligent', knowledge based solution aimed at improving the quality of critical decisions b.. Enhance the competencies and responsiveness of individuals and organisations involved in highly complex, safety critical incidents - irrespective of their location. In other words, RIMSAT aims to design and implement a decision support system that using Case Base Reasoning as well as Model Base Reasoning technology is applied in the management of emergency situations. This document is part of a deliverable for RIMSAT project, and although it has been done in close contact with the requirements of the project, it provides an overview wide enough for providing a state of the art in integration strategies between CBR and MBR technologies.Postprint (published version

On a Uniform Causality Model for Industrial Automation

The increasing complexity of Cyber-Physical Systems (CPS) makes industrial automation challenging. Large amounts of data recorded by sensors need to be processed to adequately perform tasks such as diagnosis in case of fault. A promising approach to deal with this complexity is the concept of causality. However, most research on causality has focused on inferring causal relations between parts of an unknown system. Engineering uses causality in a fundamentally different way: complex systems are constructed by combining components with known, controllable behavior. As CPS are constructed by the second approach, most data-based causality models are not suited for industrial automation. To bridge this gap, a Uniform Causality Model for various application areas of industrial automation is proposed, which will allow better communication and better data usage across disciplines. The resulting model describes the behavior of CPS mathematically and, as the model is evaluated on the unique requirements of the application areas, it is shown that the Uniform Causality Model can work as a basis for the application of new approaches in industrial automation that focus on machine learning

Knowledge and model based reasoning for power system protection performance analysis

Technological advances within the field of power systems has led to engineers, at all levels, being confronted with an ever increasing amount of data to be analysed. This coincides with greater pressure on engineers to work more efficiently and cost effectively, due to the increasingly competitive nature of the electricity supply industry. As a result, there is now the requirement for intelligent systems to interpret the available data and provide information which is relevant, manageable and readily assimilated by engineers. This thesis concerns the application of intelligent systems to the data interpretation tasks of protection engineers. An on-line decision support system is discussed which integrates two expert system paradigms in order to perform power system protection performance analysis. Knowledge based system techniques are used to interpret the data from supervisory, control and data acquisition systems, whereas a model based diagnosis approach to the comprehensive validation of protection performance, using the more detailed data which is available from fault records or equivalent, is assessed. Such a decision support system removes the requirement for time consuming manual analysis of data. An assessment of power system protection performance is provided in an on-line fashion, quickly alerting the engineers to failures or problems within the protection system. This improves efficiency and maximises the benefit of having an abundance of data available.Technological advances within the field of power systems has led to engineers, at all levels, being confronted with an ever increasing amount of data to be analysed. This coincides with greater pressure on engineers to work more efficiently and cost effectively, due to the increasingly competitive nature of the electricity supply industry. As a result, there is now the requirement for intelligent systems to interpret the available data and provide information which is relevant, manageable and readily assimilated by engineers. This thesis concerns the application of intelligent systems to the data interpretation tasks of protection engineers. An on-line decision support system is discussed which integrates two expert system paradigms in order to perform power system protection performance analysis. Knowledge based system techniques are used to interpret the data from supervisory, control and data acquisition systems, whereas a model based diagnosis approach to the comprehensive validation of protection performance, using the more detailed data which is available from fault records or equivalent, is assessed. Such a decision support system removes the requirement for time consuming manual analysis of data. An assessment of power system protection performance is provided in an on-line fashion, quickly alerting the engineers to failures or problems within the protection system. This improves efficiency and maximises the benefit of having an abundance of data available