Nebot, M.; Mugica, F.; Escobet, A. International Conference on Simulation and Modeling Methodologies, Technologies and Applications p. 501-507 Presentation's date: 2015-07-22 Presentation of work at congresses
Wine classification is a difficult task since taste is the least understood of the human senses. In this research we propose to use hybrid fuzzy logic techniques to predict human wine test preferences based on
physicochemical properties from wine analyses. Data obtained from Portuguese white wines are used in this study. The fuzzy inductive reasoning technique achieved promising results, outperforming not only the other fuzzy approaches studied but also other data mining techniques previously applied to the same dataset, such are neural networks, support vector machines and multiple regression. Modeling wine preferences may be useful not only for marketing purposes but also to improve wine production or support the oenologist wine tasting evaluations.
Comasolivas, R.; Quevedo, J.; Escobet, T.; Escobet, A.; Romera, J. Mediterranean Conference on Control and Automation p. 1207-1213 DOI: 10.1109/MED.2015.7158912 Presentation's date: 2015-06-19 Presentation of work at congresses
This paper presents the low level control of an holonomic robot with four omnidirectional wheels. A robust control technique named Quantitative Feedback Theory (QFT), based on an uncertain linear model has been selected to design the PID speed controllers for the four-wheeled robot. A piecewise model has been estimated by means of the least squares estimation approach based on experimental results of the robot in closed loop. In particular, the control is designed
using this piecewise model. The performances of the proposed approach are analyzed in real time domain.
In this work, a fault diagnosis methodology termed VisualBlock-Fuzzy Inductive Reasoning, i.e. VisualBlock-FIR, based on fuzzy and pattern recognition approaches is presented and applied to PEM fuel cell power systems. The innovation of this methodology is based on the hybridization of an artificial intelligence methodology that combines fuzzy approaches with well known pattern recognition techniques. To illustrate the potentiality of VisualBlock-FIR, a non-linear fuel cell simulator that has been proposed in the literature is employed. This simulator includes a set of five fault scenarios with some of the most frequent faults in fuel cell systems. The fault detection and identification results obtained for these scenarios are presented in this paper. It is remarkable that the proposed methodology compares favorably to the model-based methodology based on computing residuals while detecting and identifying all the proposed faults much more rapidly. Moreover, the robustness of the hybrid fault diagnosis methodology is also studied, showing good behavior even with a level of noise of 20 dB.
The object of this paper is to provide a flowmeter data validation/reconstruction methodology that determines the annual economic efficiency of a water transport network. In this paper, the case of Aigües Ter Llobregat (ATLL) company, which manages 80% of the overall water transport network in Catalonia (Spain), will be used for illustrating purposes. Economic network efficiency is based on daily data set collected by the company using about 200 flowmeters of the network. Data collected using these sensors are used by remote control and information storage systems and they are stored in a relational database. All information provided by ATLL is analysed to detect inconsistent data using an automatic data validation method deployed in parallel with the network efficiency evaluation. As a result of the validation process, corrections of flow measurements and of billed water volume are introduced. Results from ATLL water transport network corresponding to year 2010 will be used to illustrate the approach proposed in this paper.
En este artículo se presenta el desarrollo de un equipo didáctico para la enseñanza práctica de la teoría de control. El equipo permite la realización de experimentos en lazo abierto o cerrado, y puede ser controlado con dispositivos industriales o microprocesadores, adaptando las señales adecuadamente. El trabajo experimental propuesto abarca aspectos de análisis del proceso, diseño de controladores utilizando técnicas de asignación de polos o técnicas de sintonía empírica, y su implementación en base a un microprocesador.
Aquesta tesi tracta sobre la metodologia del raonament inductiu difús (FIR, de l’anglès fuzzy inductive reasoning) aplicada a
sistemes de detecció i diagnòstic de fallades. La metodologia FIR sorgeix de l’enfocament del general systems problem
solver (GSPS) proposat per Klir l’any 1989 i és una eina per analitzar i estudiar els modes de comportament dels sistemes
dinàmics. FIR és una metodologia de modelització i simulació qualitativa de sistemes basada principalment en l’observació
del comportament del sistema. Aquesta metodologia ha anat evolucionant al llarg del temps amb l'objectiu d'ampliar la
classe de problemes que es poden abordar amb FIR.
El treball desenvolupat en aquesta tesi té el propòsit de contribuir a reduir els esforços de modelització i simulació de
sistemes industrials reals complexos. En aquesta línia, s’ha aconseguit augmentar, mitjançant diferents aportacions, la
robustesa de FIR i desenvolupar una nova metodologia que permeti crear sistemes de detecció i diagnòstic de fallades
robustos i eficients.
L’objectiu principal d’aquesta tesi és reduir tant com sigui possible la sensibilitat de la metodologia FIR, és a dir,
maximitzar-ne la robustesa, de manera que esdevingui una eina cabdal per desenvolupar sistemes de detecció i diagnòstic
de fallades eficients.
Les contribucions principals de la tesi són:
•Incrementar la robustesa del FIR creant una nova eina, anomenada Visual-FIR, que permet identificar models i predir
comportaments futurs de sistemes dinàmics en un entorn molt senzill d’utilitzar i molt eficient.
•Desenvolupar una nova metodologia per crear sistemes de detecció i diagnòstic de fallades basats en FIR. S’ha
desenvolupat una tècnica de detecció, anomenada envolupant, i una mesura per al diagnòstic, anomenada mesura
d’acceptabilitat del model, que han permès millorar i fer més sòlids els processos de detecció i diagnòstic de fallades del
FIRFDDS (sistema de detecció i diagnòstic de fallades basat en FIR).
•Desenvolupar una eina que permeti crear de manera senzilla i altament eficient FIRFDDS per a aplicacions específiques.
S’ha desenvolupat una plataforma, anomenada VisualBlock-FIR, que permet que l’usuari creï, d’una manera senzilla,
sistemes de detecció i diagnòstic de fallades basats en el FIR.
Per validar la metodologia i les eines desenvolupades es mostren un parell de casos d’estudi. El primer correspon al
problema de referència de la vàlvula automàtica de Damadics, en què es proposen quatre fallades de petita i mitjana
magnitud que es detecten i s’aïllen/s’identifiquen d’una manera molt ràpida i eficient. En el segon es posa a prova el
VisualBlock-FIR en una pila de combustible simulada a la qual s’apliquen cinc fallades diferents, les quals són detectades i
identificades correctament. Finalment, es comprova la robustesa afegint soroll blanc, en diferents magnituds, a les sortides
de la pila de combustible.
This thesis deals with the Fuzzy Inductive Reasoning (FIR) methodology applied to fault detection and diagnosis systems.
FIR, based on the General Systems Problem Solver (GSPS) proposed by Klir in 1989, is a methodological tool for data-driven
construction of dynamical systems and for studying their conceptual modes of behavior. FIR is a qualitative modeling and
simulation methodology that is based on observation of the input¿output behavior of the system to be modeled, rather than
on structural knowledge about its internal composition. This methodology has evolved over time with the aim of enlarging the
class of problems that can be dealt with by FIR.
The work presented in this thesis aims to contribute to reducing modeling and simulation efforts of real industrial complex
systems. Several methodological contributions have been made to increase FIR robustness as well as to develop a new
methodology to create robust and efficient fault detection and diagnosis systems.
The main objective of this thesis is to reduce as much as possible the sensitivity of the FIR methodology, by maximizing its
robustness, in such a way that it becomes a fundamental tool for developing efficient fault detection and diagnosis systems.
The main contributions of this thesis are:
¿To improve the robustness of FIR by creating a new tool, Visual-FIR, that identifies patterns and predicts future behavior of
dynamical systems in a very efficient and simple to use environment.
¿To develop a new methodology for creating fault detection and diagnosis systems based on FIR. We have developed a
detection technique, the enveloping, and a diagnosis measure, known as the acceptability measure, that allow improving and
making more robust the fault detection and diagnosis processes of the FIRFDDS (fault detection and diagnosis system
based on FIR).
¿To develop a tool that allows to easily create highly efficient FIRFDDS for specific applications. A platform, named
VisualBlock-FIR, has been developed that allows the user to create, in a simple way, fault detection and diagnosis systems
based on FIR.
In order to validate the methodological contributions and the developed tools a couple of case studies have been presented
in this dissertation. The first corresponds to the benchmark problem of the Damadics automatic valve system, which
proposes four failures of small and medium sizes that are detected and isolated / identified in a quick and highly efficient
way. The second is a simulated fuel cell where five different faults are applied. The five faults are detected and identified
correctly. Finally, we check the robustness of the FIRFDDS by adding white noise, at different magnitudes, to the outputs of
the fuel cell.
This paper presents a methodology for leakage
localization using FIR (Fuzzy Inductive Reasoning). A real water network situated in Barcelona has been subdivided in zones which could contain a leakage. Two sensors measure
pressures on two separated points of the network. A faulty fuzzy model for each zone and sensor is generated. Test data have been used for classification of leakages in order to evaluate how this methodology helps in leakage localization. Results are compared with another isolation methodology. All the work has been done using simulations carried out by EPANET connected with Matlab. FIR applications used are programmed in Matlab
In this work a fault diagnosis system for non-linear plants based on fuzzy logic, called VisualBlock-FIR, is presented and applied to an energy generation system based on fuel cells. VisualBlock-FIR runs under the Simulink framework and enables early fault detection and identification. During fault detection, the fault diagnosis system should recognize that the system is not working properly. During fault identification, it should conclude which type of failure has occurred. The diagnosis results for some of the most frequent faults in fuel cell systems are presented.
MILAGRO project was conducted in Mexico City during March 2006 with the main objective of study the local and global impact of pollution generated by megacities. The research presented in this paper is framed in MILAGRO project and is focused on the study and development of modeling methodologies that allow the forecasting of daily ozone concentrations. The present work aims to develop Fuzzy Inductive Reasoning (FIR) models using the Visual-FIR platform. FIR offers a model-based approach to modeling and predicting either univariate or multivariate time series. Visual-FIR offers an easy-friendly environment to perform this task. In this research, long term prediction of maximum ozone concentration in the downtown of Mexico City Metropolitan Area is performed. The data were registered every hour and include missing values. Two modeling perspectives are analyzed, i.e. monthly and seasonal models. The results show that the developed models are able to predict the diurnal variation of ozone, including its maximum daily value in an accurate manner.
Air pollution is one of the most important environmental problems in urban areas, being extremely critical in Mexico City. The main air pollution problem that has been identified in Mexico City metropolitan area is the formation of photochemical smog, primarily ozone. The study and development of modeling methodologies that allow the capturing of time series behavior becomes an important task. The present work aims to develop Fuzzy Inductive Reasoning (FIR) models using the Visual-FIR platform. FIR offers a model-based approach to modeling and predicting either univariate or multivariate time series. Visual-FIR offers an easy-friendly environment to perform this task. In this research, long term prediction of maximum ozone concentration in the centre region of Mexico City metropolitan area is performed. The data were registered every hour and include missing values. Two modeling perspectives are analyzed, i.e. monthly and seasonal models. The results show that the models identified capture the dynamic behavior of ozone contaminant in an accurate manner.