Tesis doctoral de Isaac Monroy Chora
Plant safety is the most important concern of chemical industries. Process faults can cause economic loses as well as human and environmental damages. Most of the operational faults are normally considered in the process design phase by applying methodologies such as hazard and operability analysis (hazop). However, it should be expected that failures may occur in an operating plant. For this reason, it is of paramount importance that plant operators can promptly detect and diagnose such faults in order to take the appropriate corrective actions. In addition, preventive maintenance needs to be considered in order to increase plant safety. fault diagnosis has been faced with both analytic and data-based models and using several techniques and algorithms. However, there is not yet a general fault diagnosis framework that joins detection and diagnosis of faults, either registered or non- registered in records. Even more, less efforts have been focused to automate and implement the reported approaches in real practice. according to this background, this thesis proposes a general framework for data- driven fault detection and diagnosis (fdd), applicable and susceptible to be automated in any industrial scenario in order to hold the plant safety. Thus, the main requirement for constructing this system is the existence of historical process data. In this sense, promising methods imported from the machine learning field are introduced as fault diagnosis methods. The learning algorithms, used as diagnosis methods, have proved to be capable to diagnose not only the modeled faults, but also novel faults. Furthermore, risk-based maintenance (rbm) techniques, widely used in petrochemical industry, are proposed to be applied as part of the preventive maintenance in all industry sectors. The proposed fdd system together with an appropriate preventive maintenance program would represent a potential plant safety program to be implemented. thus, chapter one presents a general introduction to the thesis topic, as well as the motivation and scope. Then, chapter two reviews the state of the art of the related fields. Fault detection and diagnosis methods found in literature are reviewed. In this sense a taxonomy that joins both artificial intelligence (ai) and process systems engineering (pse) classifications is proposed. The fault diagnosis assessment with performance indices is also reviewed. Moreover, it is exposed the state of the art corresponding to risk analysis (ra) as a tool for taking corrective actions to faults and the maintenance management for the preventive actions. Finally, the benchmark case studies against which fdd research is commonly validated are examined in this chapter. the second part of the thesis, integrated by chapters three to six, addresses the methods applied during the research work. Chapter three deals with the data pre- processing, chapter four with the feature processing stage and chapter five with the diagnosis algorithms. On the other hand, chapter six introduces the risk-based maintenance techniques for addressing the plant preventive maintenance.
Datos académicos de la tesis doctoral «An investigation on automatic systems for fault diagnosis in chemical processes«
- Título de la tesis: An investigation on automatic systems for fault diagnosis in chemical processes
- Autor: Isaac Monroy Chora
- Universidad: Politécnica de catalunya
- Fecha de lectura de la tesis: 03/02/2012
Dirección y tribunal
- Director de la tesis
- Moises Graells Sobre
- Tribunal
- Presidente del tribunal: bernt Lie
- kris roger Elie villez (vocal)
- raul Benitez iglesias (vocal)
- gonzalo Guillen gosalbez (vocal)