Nelly Olivier-Maget

Model based fault diagnosis for hybrid systems : application on chemical processes

The complexity and the size of the industrial chemical processes induce the monitoring of a growing number of process variables. Their knowledge is generally based on the measurements of system variables and on the physico-chemical models of the process. Nevertheless, this information is imprecise because of process and measurement noise. So the research ways aim at developing new and more powerful techniques for the detection of process fault. In this work, we present a method for the fault detection based on the comparison between the real system and the reference model evolution generated by the extended Kalman filter. The reference model is simulated by the dynamic hybrid simulator, PrODHyS. It is a general object-oriented environment which provides common and reusable components designed for the development and the management of dynamic simulation of industrial systems. The use of this method is illustrated through a didactic example relating to the field of Chemical Process System Engineering.

Using Dynamic Simulation for Risk Assessment: Application to an Exothermic Reaction

In this paper, a solution strategy based on an optimization formulation is proposed for the design of Water Allocation and Heat Exchange Networks (WAHEN) in the process industries. Such typical large problems involve many processes, regeneration units and multi-contaminants. For this purpose, a two-stage methodology is proposed. The first step is the Water Allocation Network (WAN) design by multi-objective optimization, based on the minimization of the number of network connections and of the global equivalent cost (which includes three criteria, i.e., freshwater, regenerated water and wastewater). The ɛ-constraint method is used to deal with the multi-criteria problem. In a second step, the Heat Exchange Network (HEN) is solved by two approaches, Pinch analysis and mathematical programming (MP). In both cases the HEN structure is found when the minimal energy requirement and the total annual cost are minimized for Pinch and MP, respectively. These results are compared and the best HEN network is then coupled to the WAN to verify the feasibility of the network. A case study including a change of phase among the streams is solved. The results show that this two-step methodology can be useful for the treatment of large problems.

Robust fault detection for the hybrid dynamic systems

Abstract PrODHyS is a dynamic hybrid simulation environment which provides common and reusable object oriented components designed for the development and the management of dynamic simulation of industrial systems. In this work, we present a method for the fault detection based on the comparison between the real system and the reference model evolution generated by the extended Kalman filter. The reference model is simulated by PrODHyS.

Fault detection and isolation based on the model-based approach: Application on chemical processes

Abstract In this paper, we present a method for the fault detection and isolation based on the residual generation. The main idea is to reconstruct the outputs of the system from the measurement using the extended Kalman filter. The estimations are compared to the values of the reference model and so, deviations are interpreted as possible faults. The reference model is simulated by the dynamic hybrid simulator, PrODHyS. The use of this method is illustrated through an application in the field of chemical process.

Monitoring of Hybrid Dynamic Systems: Application to Chemical Process

These works present a fault detection and isolation methodology for the monitoring of Hybrid Dynamic Systems. The developed methodology rests on a mixed approach which combines a model-based method for the fault detection and an approach based on data (pattern matching) for the identification of fault(s). It is divided into three parts: The first part concerns the reconstruction of the state of the system, thanks to the extended Kalman filter and the generation of the residuals by comparison between the predicted behaviour (obtained thanks to the simulation of the reference model) and the real observed behaviour (estimated by the extended Kalman filter).The second part exploits these residuals for the generation of a synthetic structure: the non-binary signatures. The last part deals with the diagnosis of the fault and is based on a problem of pattern matching: the signature obtained in the previous part is compared with the theoretical fault signatures by means of distance. Its use is illustrated by the studies of diagnosis problems in the field of Chemical Process System Engineering.

Fault detection and diagnosis based on a hybrid dynamic simulator

Abstract The complexity and the size of the industrial chemical processes induce the monitoring of a growing number of process variables. Their knowledge is generally based on the measurements of system variables and on the physico-chemical models of the process. Nevertheless, this information is imprecise because of process and measurement noise. So the research ways aim at developing new and more powerful techniques for the detection of process faults. This paper presents a method for the fault detection based on the comparison between the real system and the reference model evolution generated by the extended Kalman filter. The reference model is simulated by the dynamic hybrid simulator, PrODHyS. It is a general object-oriented environment which provides common and reusable components designed for the development and the management of dynamic simulation of industrial systems. The use of this method is illustrated through a didactic example relating to the field of Chemical Process System Engineering.

Fault diagnosis and process monitoring through model-based and case based reasoning

In this paper, we present a method for the fault detection and isolation based on the residual generation coupled with a case based reasoning approach. The main idea is to reconstruct the outputs of the system from the measurement using the extended Kalman filter. The estimations completed with qualitative information are included in a Case Based Reasoning system in order to discriminate the possible faults and to have a reliable diagnosis. The reference model is simulated by the dynamic hybrid simulator, PrODHyS. The use of this method is illustrated through an application in the field of chemical process

Model-Based Fault Diagnosis Using a Hybrid Dynamic Simulator: Application to a Chemical Process

Abstract This work presents a fault detection and isolation methodology for the monitoring of Hybrid Dynamic Systems. This methodology rests on a mixed approach, which combines a model-based method for the fault detection and an approach based on data (pattern matching) for the identification of fault(s). This methodology is integrated within the simulation platform PrODHyS , through the development of the module PrODHySAEM . The goal of this paper is to underline the potentialities of our approach for the diagnosis of the system. This methodology is illustrated by the studies of diagnosis problems in the field of Chemical Process System Engineering.