14,087 research outputs found
Process operating mode monitoring : switching online the right controller
This paper presents a structure which deals with
process operating mode monitoring and allows the control law reconfiguration
by switching online the right controller. After a short
review of the advances in switching based control systems during
the last decade, we introduce our approach based on the definition
of operating modes of a plant. The control reconfiguration
strategy is achieved by online selection of an adequate controller,
in a case of active accommodation. The main contribution lies
in settling up the design steps of the multicontroller structure
and its accurate integration in the operating mode detection and
accommodation loop. Simulation results show the effectiveness
of the operating mode detection and accommodation (OMDA)
structure for which the design steps propose a method to study the
asymptotic stability, switching performances improvement, and
the tuning of the multimodel based detector
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
FAST : a fault detection and identification software tool
The aim of this work is to improve the reliability and safety of complex critical control systems by contributing to the systematic application of fault diagnosis. In order to ease the utilization of fault detection and isolation (FDI) tools in the industry, a systematic approach is required to allow the process engineers to analyze a system from this perspective. In this way, it should be possible to analyze this system to find if it provides the required fault diagnosis and redundancy according to the process criticality. In addition, it should be possible to evaluate what-if scenarios by slightly modifying the process (f.i. adding sensors or changing their placement) and evaluating the impact in terms of the fault diagnosis and redundancy possibilities.
Hence, this work proposes an approach to analyze a process from the FDI perspective and for this purpose provides the tool FAST which covers from the analysis and design phase until the final FDI supervisor implementation in a real process. To synthesize the process information, a very simple format has been defined based on XML. This format provides the needed information to systematically perform the Structural Analysis of that process. Any process can be analyzed, the only restriction is that the models of the process components need to be available in the FAST tool. The processes are described in FAST in terms of process variables, components and relations and the tool performs the structural analysis of the process obtaining: (i) the structural matrix, (ii) the perfect matching, (iii) the analytical redundancy relations (if any) and (iv) the fault signature matrix.
To aid in the analysis process, FAST can operate stand alone in simulation mode allowing the process engineer to evaluate the faults, its detectability and implement changes in the process components and topology to improve the diagnosis and redundancy capabilities. On the other hand, FAST can operate on-line connected to the process plant through an OPC interface. The OPC interface enables the possibility to connect to almost any process which features a SCADA system for supervisory control. When running in on-line mode, the process is monitored by a software agent known as the Supervisor Agent.
FAST has also the capability of implementing distributed FDI using its multi-agent architecture. The tool is able to partition complex industrial processes into subsystems, identify which process variables need to be shared by each subsystem and instantiate a Supervision Agent for each of the partitioned subsystems. The Supervision Agents once instantiated will start diagnosing their local components and handle the requests to provide the variable values which FAST has identified as shared with other agents to support the distributed FDI process.Per tal de facilitar la utilitzaciĂł d'eines per la detecciĂł i identificaciĂł de fallades (FDI) en la indĂşstria, es requereix un enfocament sistemĂ tic per permetre als enginyers de processos analitzar un sistema des d'aquesta perspectiva. D'aquesta forma, hauria de ser possible analitzar aquest sistema per determinar si proporciona el diagnosi de fallades i la redundĂ ncia d'acord amb la seva criticitat. A mĂ©s, hauria de ser possible avaluar escenaris de casos modificant lleugerament el procĂ©s (per exemple afegint sensors o canviant la seva localitzaciĂł) i avaluant l'impacte en quant a les possibilitats de diagnosi de fallades i redundĂ ncia. Per tant, aquest projecte proposa un enfocament per analitzar un procĂ©s des de la perspectiva FDI i per tal d'implementar-ho proporciona l'eina FAST la qual cobreix des de la fase d'anĂ lisi i disseny fins a la implementaciĂł final d'un supervisor FDI en un procĂ©s real. Per sintetitzar la informaciĂł del procĂ©s s'ha definit un format simple basat en XML. Aquest format proporciona la informaciĂł necessĂ ria per realitzar de forma sistemĂ tica l'AnĂ lisi Estructural del procĂ©s. Qualsevol procĂ©s pot ser analitzat, nomĂ©s hi ha la restricciĂł de que els models dels components han d'estar disponibles en l'eina FAST. Els processos es descriuen en termes de variables de procĂ©s, components i relacions i l'eina realitza l'anĂ lisi estructural obtenint: (i) la matriu estructural, (ii) el Perfect Matching, (iii) les relacions de redundĂ ncia analĂtica, si n'hi ha, i (iv) la matriu signatura de fallades. Per ajudar durant el procĂ©s d'anĂ lisi, FAST pot operar aĂŻlladament en mode de simulaciĂł permetent a l'enginyer de procĂ©s avaluar fallades, la seva detectabilitat i implementar canvis en els components del procĂ©s i la topologia per tal de millorar les capacitats de diagnosi i redundĂ ncia. Per altra banda, FAST pot operar en lĂnia connectat al procĂ©s de la planta per mitjĂ d'una interfĂcie OPC. La interfĂcie OPC permet la possibilitat de connectar gairebĂ© a qualsevol procĂ©s que inclogui un sistema SCADA per la seva supervisiĂł. Quan funciona en mode en lĂnia, el procĂ©s estĂ monitoritzat per un agent software anomenat l'Agent Supervisor. Addicionalment, FAST tĂ© la capacitat d'implementar FDI de forma distribuĂŻda utilitzant la seva arquitectura multi-agent. L'eina permet dividir sistemes industrials complexes en subsistemes, identificar quines variables de procĂ©s han de ser compartides per cada subsistema i generar una instĂ ncia d'Agent Supervisor per cadascun dels subsistemes identificats. Els Agents Supervisor un cop activats, començaran diagnosticant els components locals i despatxant les peticions de valors per les variables que FAST ha identificat com compartides amb altres agents, per tal d'implementar el procĂ©s FDI de forma distribuĂŻda.Postprint (published version
Data-based fault detection in chemical processes: Managing records with operator intervention and uncertain labels
Developing data-driven fault detection systems for chemical plants requires managing uncertain data labels and dynamic attributes due to operator-process interactions. Mislabeled data is a known problem in computer science that has received scarce attention from the process systems community. This work introduces and examines the effects of operator actions in records and labels, and the consequences in the development of detection models. Using a state space model, this work proposes an iterative relabeling scheme for retraining classifiers that continuously refines dynamic attributes and labels. Three case studies are presented: a reactor as a motivating example, flooding in a simulated de-Butanizer column, as a complex case, and foaming in an absorber as an industrial challenge. For the first case, detection accuracy is shown to increase by 14% while operating costs are reduced by 20%. Moreover, regarding the de-Butanizer column, the performance of the proposed strategy is shown to be 10% higher than the filtering strategy. Promising results are finally reported in regard of efficient strategies to deal with the presented problemPeer ReviewedPostprint (author's final draft
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