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Nowadays, complex manufacturing processes have to be dynamically modeled to estimate their reliability. Moreover the results computed with classical methods need to be reinforced by managing the uncertainty. To address these difficulties, this paper presents a new method for modeling and analyzing the system reliability based on dynamic evidential networks(More)
—There are many process mining algorithms and representations, making it difficult to choose which algorithm to use or compare results. Process mining is essentially a machine learning task, but little work as been done on systematically analysing algorithms to understand their fundamental properties, such as how much data is needed for confidence in(More)
Process mining uses event logs to learn and reason about business process models. Existing algorithms for mining the control-flow of processes in general do not take into account the probabilistic nature of the underlying process, which affects the behaviour of algorithms and the amount of data needed for confidence in mining. We contribute a first step(More)
The aim of Fault Tolerant Control (FTC) is to preserve the ability of the system to reach performances as close as possible to those which were initially assigned to it. The main goal of this paper consists in the development of a FTC strategy, based on both reliability and life cost of components. Once a fault has been detected and isolated and when it is(More)
Automatic control systems with sophisticated control algorithm can be very large and complex. In order to improve the automatic process control, it is important to develop fault diagnosis strategy. A hierarchical scheme of fault detection and isolation based on Decision Support System (DSS) is presented. For fault diagnosis, a knowledge based procedure is(More)
In this paper, a method to implement a platform of failure diagnosis and prognosis, and health monitoring based on data using Input Output Hidden Markov Models (IOHMM) is proposed. Several sensors on a diesel generator system give information such as on-line operating conditions. The goal of this work is to use on-line collected data in order to determine(More)
This paper presents a fault detection method based on a classical transfer function parameter estimation algorithm in the discrete time domain. Non persistently exciting inputs plant an important problem for the convergence of the estimator. Here, the forgetting factor is adapted on-line in order to improve the convergence. Redundant discrete time transfer(More)