Philippe Bouché

A Global Method for Modelling and Performance Analysis of Production Flows

In our increasingly competitive world, today companies are implementing improvement strategies in every department and, in particular, in their manufacturing systems. This paper discusses the use of a global method based on a knowledge-based approach for the development of a software tool for modelling and analysis of production flows. This tool will help promote the companies competitiveness by guaranteeing the efficiency of their production lines and, therefore, the quality and traceability of the manufactured products. Different kind of techniques will be used: graphic representation of the production, identification of specific behaviour and research of correlations among events on the production line. Most of these techniques are based on statistic and probabilistic analyses.

Building a Functionnal Model from a Sequence of Alarms: The Example of APACHE

Abstract This paper aims at showing for a real case the results obtained with the stochastic approach to the analysis of a sequence of alarms produced by the Apache system, a Sachem-like system that is dedicated to monitoring, diagnosing and controlling the zinc baths used by the Arcelor Group. Sachem is the Arcelors generic extensive large-scale real time knowledge based system designed to monitor, diagnose and control processes. The paper shows how the knowledge of the behavior of a continuous process can be formalized in terms of relations between discrete events that are deduced from a sequence of Apaches alarms by the mean ofa stochastic approach. The stochastic approach is based on the representation of a sequence of alarms under the dual form of a homogeneous Markov chain and its corresponding superposition of Poisson processes. The paper shows how a functional model can be derived from a behavioral model produced according to the stochastic approach.

Towards a Discrete Event Formalization of Sachem's Perception Based Monitoring

Abstract This paper aims at introducing the basis of the formalization of the Sachem perception based monitoring and diagnosis approach. Sachem is an extensive large-scale real time knowledge based system designed to monitor and diagnose blast furnaces. In this paper, Sachem is considered as a monitoring cognitive agent that analyzes a continuous dynamic process in order to generate discrete events (alarms) that inform about unsatisfactory process states occurences. The paper shows how the knowledge about the behavior of a continuous process can be formalized in terms of relation between discrete events so that a recursive recognition process of signatures can be used in order to design monitoring cognitive agents. The paper concludes on the main property of the discrete event representation of continuous process, the compactness.

Discovering internal fraud models in a stream of banking transactions

Internal frauds in the banking industry represent a huge cost and this problem is particularly difficult to solve because, by construction, swindlers being very imaginative persons, the fraud schemata evolves continuously. Fraud detection systems must then learn from the continuously new fraud schematas, making them difficult to design. This paper proposes a new theoretical and practical approach to detect internal frauds and to model fraud schematas. This approach is based on a particular method of abstraction that reduces the complexity of the problem from O(n2) to O(n) making its implementation in a an Java program that detects and models the frauds in real time and online with a simple professional personal computer. The results of this program are presented with its application on a real-world fraud provided by a world wide French bank.