Root Cause Analysis of Actuator Fault

This chapter develops a two-level fault diagnosis (FD) and root cause analysis (RCA) scheme for a class of interconnected invertible dynamic systems and aims at detecting and identifying actuator fault and the causes. By considering actuator as an individual dynamic subsystem connected with process dynamic subsystem in cascade, an interconnected system is then constituted. Invertibility of the interconnected system in faulty model is studied. An interconnected observer is introduced and aims at monitoring the performance of the interconnected system and providing information of actuator fault occurrence. A local fault filter algorithm is then triggered to identify the root causes of the detected actuator faults. According to real plant, outputs of the actuator subsystem are assumed inaccessible and are reconstructed by measurements of the global system, thus providing a means for monitoring and diagnosing the plant at both local and global level

MODELISATION STOECHIOMETRIQUE DES PRODUCTIONS MICROBIENNES PAR DESCRIPTEUR METABOLIQUE AU MOYEN DU CALCUL FORMEL. VALIDATION SUR LE MODELE KLUYVEROMYCES MARXIANUS

TOULOUSE-INSA (315552106) / SudocSudocFranceF

Multi-parameter fault isolation using trajectory-based envelope

International audienceParameter interval based fault isolation for single parameter fault has ideal isolation speed, the fault parameter value is estimated when fault is isolated. Analogous scheme can be built for multi-parameter fault isolation using envelope to replace scalar threshold for interval judgment. However wrapping effect should be considered when envelope is used. In this paper a multi-parameter fault isolation scheme is built using a trajectory-based envelope which is without of wrapping effect

Fault-tolerant control based on insufficient fault information

International audienc

Contribution à l'élaboration d'algorithmes d'isolation et d'identification de défauts dans les systèmes non linéaires

INIST-CNRS (INIST), under shelf-number: RP 17272 / SudocSudocFranceF

Les réseaux de neurones pour la modélisation et la commande des procédés biotechnologiques

Dans ce travail nous réalisons une étude sur l'utilisation de réseaux de neurones pour la modélisation, la classification et la prédiction appliquées aux procédés de fermentation. Les modèles de type boîte noire (et nous classifions ici les réseaux de neurones) sont utiles pour la modélisation des procédés ou des phénomènes pour lesquels des modèles analytiques ne peuvent pas être déduits à partir de considérations physiques. Parmi les avantages des modèles neuronaux par rapport aux autres modèles boîte noire, nous mentionnons le fait qu'ils sont des approximateurs universels, leurs fonctions de base sont adaptatives, leur structure répétitive permet une facile implémentation logicielle et matérielle et ils ont la propriété de la régularisation implicite. Ceux-ci, combinés avec les caractéristiques de procédés biologiques (procédés non-linéaires et non-stationnaires dont la dynamique et peu connue), fournissent la raison pour laquelle les réseaux de neurones sont un outil très apprécié pour la modélisation des procédés biologiques, ou des procédés de fermentation, dans notre cas. Nous avons donc utilisé des structures de modèles neuronaux déjà existants et proposé aussi de nouvelles structures pour les cas ciblés de fermentations alcoolique et lactique. Nous présentons deux approches pour la caractérisation de la dynamique d un procédé de fermentation: la modélisation du taux de croissance en biomasse, le paramètre dynamique principal du procédé et la caractérisation globale du type de la dynamique du procédé à l aide d un classifieur neuronal. Les deux approches sont testées en simulation et sur des données expérimentales pour une fermentation lactique et une fermentation alcoolique. La caractérisation globale de la dynamique d un procédé de fermentation représente un outil potentiel pour la supervision des procédés en détectant les changements dans la dynamique du système où une aide à la modélisation des procédés de fermentation en mode discontinu. Nous avons considéré aussi la prédiction de la biomasse pour une fermentation en mode continu et les modèles neuronaux de prédiction ont été testés dans une stratégie de commande prédictive. Les résultats sont comparés avec la même stratégie prédictive mais utilisant une approche adaptative et l'approche neuronale a un succès incontestable pour les cas ou la dynamique du procédé change dans le temps. Finalement nous nous sommes intéressés à la prédiction du quotient respiratoire, proposant un modèle neuronal de prédiction. Il est réalisé en vue d'une commande prédictive du procédé pour la maintenance d'un certain régime de fonctionnement (oxydatif ou fermentaire)In this work we realize a study on the use of the neural nets for the modeling, classification and the control of fermentation processes.The black-box models (we consider a neural net like a black box model) are of great help for processes or phenomena modeling when analytical models cannot be deduced from physical considerations. Some of the advantages of the neural nets when compared to other black-box models are: they are universal approximators using a small number of parameters, their basis functions are adaptive, their repetitive structure permits an easy implementation both software and hardware and they have the property of implicit regularization. These, combined with the characteristics of the biological processes (which are non-linear, non-stationary processes whose dynamics isn t entirely known), are the reason for which the neural nets are used for the modeling of such processes. We have thus used existing neural models and proposed new ones for the cases of lactic and alcoholic fermentations. We have presented two approaches for the characterization of the fermentation process dynamics: the modeling of the specific biomass growth rate, the most important dynamic parameter of a fermentation process and the global characterization of the process dynamics using a neural classifier. The two approaches have been tested in simulation and on real data for lactic or alcoholic fermentation processes. The use of a classifier of the process dynamics represents a potential tool for process supervision by means of detecting the changes in the process dynamics as well as an aid for the process modeling in the case of batch processes. The prediction of the biomass concentration has also been considered for a continuous fermentation process. The neural models have been tested in a predictive control strategy and compared with a similar strategy using adaptive modeling. The neural prediction has been an incontestable winner for the cases where the process dynamics changes in time. The last issue of our study has been the prediction of the respiratory quotient for a alcoholic fermentation for which we proposed a neural model. It has been proposed in view of a predictive control strategy for the maintenance of a certain regime (fermentative or oxidative)INIST-CNRS (INIST), under shelf-number: RP 17272 / SudocSudocFranceF

Fault isolation and identification based on adaptive parameter intervals for nonlinear dynamic systems

International audienceA fault isolation and identification method for nonlinear dynamic systems is proposed. The method is based on parameter intervals with adaptive thresholds. Its isolation speed is fast. It provides immediately the estimate of the system faulty parameter value after the fault isolation. The superior and inferior bounds of the faulty parameter are also given. Along with the operations of the correspondent adaptive observer, the estimate of the system faulty parameter becomes more and more precise

Multi-objective optimization of fuzzy MPPT using improved strength Pareto evolutionary algorithm

International audienceWith the exception of a few simple applications, all photovoltaic (PV) conversion systems have a maximum power point tracking (MPPT) unit which allows an optimal extraction of power. In this paper, the fuzzy logic controller and evolutionary methods are combined to obtain an efficient MPPT unit with a fast response in transient state and minimal errors in steady state. The proposed combination shows a good performance in simulation and offers a varied set of MPPT controllers

Contribution à l'étude du diagnostic et de la commande tolérante aux fautes par l'approche structurelle (application aux procédés biologiques)

TOULOUSE3-BU Sciences (315552104) / SudocSudocFranceF

Fault Tolerant Control Strategy Using Two-Layer Multiple Adaptive Models for Plant Fault

International audienceFault tolerant control (FTC) is always a popular research direction in the domain of automatic control. Inspired by the concept of adaptive model and corresponding approaches in [1], this paper proposed an FTC design strategy for plant fault by introducing these adaptive models into the two-layer multiple model structure. The two-layer multiple model structure describes a hyper-system which considers the nominal and faulty situations of a complex system. A group of local models are selected to present the system in its full range of operation and this is the first layer multiple model. At the second layer, we create a group of model bank to describe the system in nominal and each faulty situations. By checking the validity of the second layer model banks, information of corresponding local models are used to initialize the adaptive models to have a precise approaching to the real system. Besides, model predictive control (MPC) is designed for the reference model of the adaptive process to generate proper reference input for achieving control goals while dealing the FTC problem. Simulations are given to show the validity of the proposed method