Online Diagnosis based on Chronicle Recognition of a Coil Winding Machine

This paper falls under the problems of the diagnosis of Discrete Event System (DES) such as coil winding machine. Among the various techniques used for the on-line diagnosis, we are interested in the chronicle recognition and fault tree. The Chronicle can be defined as temporal patterns that represent system possible evolutions of an observed system. Starting from the model of the system to be diagnosed, the proposed method based on the P-time Petri net allows to generate the chronicles necessary to the diagnosis. Finally, to demonstrate the effectiveness and accuracy of the monitoring approach, an application to a coil winding unit is outlined

Online Diagnosis based on Chronicle Recognition of a Coil Winding Machine

This paper falls under the problems of the diagnosis of Discrete Event System (DES) such as coil winding machine. Among the various techniques used for the on-line diagnosis, we are interested in the chronicle recognition and fault tree. The Chronicle can be defined as temporal patterns that represent system possible evolutions of an observed system. Starting from the model of the system to be diagnosed, the proposed method based on the P-time Petri net allows to generate the chronicles necessary to the diagnosis. Finally, to demonstrate the effectiveness and accuracy of the monitoring approach, an application to a coil winding unit is outlined

AI Solutions for MDS: Artificial Intelligence Techniques for Misuse Detection and Localisation in Telecommunication Environments

This report considers the application of Articial Intelligence (AI) techniques to the problem of misuse detection and misuse localisation within telecommunications environments. A broad survey of techniques is provided, that covers inter alia rule based systems, model-based systems, case based reasoning, pattern matching, clustering and feature extraction, articial neural networks, genetic algorithms, arti cial immune systems, agent based systems, data mining and a variety of hybrid approaches. The report then considers the central issue of event correlation, that is at the heart of many misuse detection and localisation systems. The notion of being able to infer misuse by the correlation of individual temporally distributed events within a multiple data stream environment is explored, and a range of techniques, covering model based approaches, `programmed' AI and machine learning paradigms. It is found that, in general, correlation is best achieved via rule based approaches, but that these suffer from a number of drawbacks, such as the difculty of developing and maintaining an appropriate knowledge base, and the lack of ability to generalise from known misuses to new unseen misuses. Two distinct approaches are evident. One attempts to encode knowledge of known misuses, typically within rules, and use this to screen events. This approach cannot generally detect misuses for which it has not been programmed, i.e. it is prone to issuing false negatives. The other attempts to `learn' the features of event patterns that constitute normal behaviour, and, by observing patterns that do not match expected behaviour, detect when a misuse has occurred. This approach is prone to issuing false positives, i.e. inferring misuse from innocent patterns of behaviour that the system was not trained to recognise. Contemporary approaches are seen to favour hybridisation, often combining detection or localisation mechanisms for both abnormal and normal behaviour, the former to capture known cases of misuse, the latter to capture unknown cases. In some systems, these mechanisms even work together to update each other to increase detection rates and lower false positive rates. It is concluded that hybridisation offers the most promising future direction, but that a rule or state based component is likely to remain, being the most natural approach to the correlation of complex events. The challenge, then, is to mitigate the weaknesses of canonical programmed systems such that learning, generalisation and adaptation are more readily facilitated

Kardio and Calicot: a comparison of two cardiac arrhythmia classifiers

proceedings on line : http://www.cs.ru.nl/~peterl/mbqr-aime03.pdfThis paper gives a comparison of two different systems that induce cardiac arrhythmia rules by symbolic learning: Kardio and Calicot. In particular, it proposes a detailed methodology to compare them and gives some results of this comparison

An extended chronicle discovery approach to find temporal patterns between sequences

Sequences of events describing the behavior and actions of users or systems can be collected in sev eral domains. An episode is a collection of events that occurs relatively close to each other in a given partial order. Also, chronicles are a special type of temporal patterns, where temporal orders of events are quantified with numerical bounds and reflect the temporal evolution of the system over the time. In this paper, the problem of finding rules for de scribing or predicting the behavior of the sequences with the intention of characterizing some interest ing tasks is considered. Obtaining these patterns is the main objective of this work, where an automatic method to learn relevant and discriminating chron icles is proposed. The method extends existing al gorithms that have been proposed to find frequent episodes/chronicles in a single event sequence to the case of multiple sequences.Ministerio de Economía y Competitividad TIN2009-14378-C02-01 (ARTEMISA)Junta de Andalucía TIC-8052 (Simon

Final report on the farmer's aid in plant disease diagnoses

This report is the final report on the FAD project. The FAD project was initiated in september 1985 to test the expert system shell Babylon by developing a prototype crop disease diagnosis system in it. A short overview of the history of the project and the main problems encountered is given in chapter 1. Chapter 2 describes the result of an attempt to integrate JSD with modelling techniques like generalisation and aggregation and chapter 3 concentrates on the method we used to elicit phytopathological knowledge from specialists. Chapter 4 gives the result of knowledge acquisition for the 10 wheat diseases most commonly occurring in the Netherlands. The user interface is described briefly in chapter 5 and chapter 6 gives an overview of the additions to the implementation we made to the version of FAD reported in our second report. Chapter 7, finally, summarises the conclusions of the project and gives recommendations for follow-up projects

GHOST: experimenting countermeasures for conflicts in the pilot's activity

An approach for designing countermeasures to cure conflict in aircraft pilots’ activities is presented, both based on Artificial Intelligence and Human Factors concepts. The first step is to track the pilot’s activity, i.e. to reconstruct what he has actually done thanks to the flight parameters and reference models describing the mission and procedures. The second step is to detect conflict in the pilot’s activity, and this is linked to what really matters to the achievement of the mission. The third step is to design accu- rate countermeasures which are likely to do bet- ter than the existing onboard devices. The three steps are presented and supported by experimental results obtained from private and professional pi- lots

Handling Breakdowns in Unmanned Aircraft Systems

International audienceThis work is devoted to activity recognition in the setting of data analysis in aeronautics. Formal methods are applied to the cer-tification and safety analysis processes of Unmanned Aircraft Systems in breakdown situations. The behaviour of these systems in case of a failure is entirely modeled and implemented. A temporal language — the Chronicle language — describes arrangements of events which are employed to detail undesired circumstances that would lead to breaches in safety. A C++ chronicle recognition tool is used to recognise all the possible occurrences of these situations as soon as they occur

Discrete Event Model-Based Approach for Fault Detection and Isolation of Manufacturing Systems

International audienceThis paper presents a discrete event model-based approach for Fault Detection and Isolation of manufacturing systems. This approach considers a system as a set of independent plant elements. Each plant element is composed of a set of interrelated Parts of Plant (PoPs) modeled by a Moore automaton. Each PoP model is only aware of its local behavior. The degraded and faulty behaviors are added to each PoP model in order to obtain extended PoP ones. An extrapolation of Gaussian learning is realized to obtain acceptable temporal intervals between the time occurrences of correlated events. Finally based on the PoP extended models and the links between them, a fault candidates' tree is established for each plant element. This candidates' tree corresponds to a local on-line fault event occurrence observer, called diagnoser. Thus, the diagnosis decision is distributed on each plant element. An application example is used to illustrate the approach