Antoni Escobet

Visual-FIR: A tool for model identification and prediction of dynamical complex systems

Abstract A new platform for the fuzzy inductive reasoning (FIR) methodology has been designed and developed under the MATLAB environment. The new tool, named Visual-FIR, allows the identification of dynamic systems models in a user-friendly environment. FIR offers a pattern-based approach to modeling and predicting either univariate or multivariate time series, obtaining very good results when applied to various areas such as control, biology, and medicine. However, the available implementation of FIR was such that new code had to be developed for each new application studied. Visual-FIR resolves this limitation and offers a high-efficiency implementation. Furthermore, the Visual-FIR platform presents a new vision of the methodology based on process blocks and adds new features, increasing the overall capabilities of the FIR methodology. The DAMADICS benchmark problem is addressed in this research using the Visual-FIR approach.

Fault diagnosis system based on fuzzy logic: Application to a valve actuator benchmark

This paper describes a fault diagnosis system (FDS) for non-linear plants based on fuzzy logic. The proposed approach, named VisualBlock-FIR, runs under the Simulink framework and enables early fault detection, isolation, and identification. During fault detection, the FDS should recognize that the plant behavior is abnormal, and therefore, that the plant is not working properly. During fault isolation/identification, the FDS should conclude, which type of failure has occurred. The enveloping and acceptability measures introduced in VisualBlock-FIR enhance the robustness of the overall process. The proposed approach is used for tackling faults of the DAMADICS benchmark, and the results are compared with those obtained by other FDS.

PEM fuel cell fault diagnosis via a hybrid methodology based on fuzzy and pattern recognition techniques

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 20dB.

Leakage localization in water networks using fuzzy logic

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 too.

Fault detection and identification using FIRFMS

This paper deals with two of the main tasks of fault monitoring systems (FMS): fault detection and fault identification. During fault detection, the FMS should recognize that the plant behavior is abnormal, and therefore, that the plant is not working properly. During fault identification, the FMS should conclude which type of failure has occurred. The main goal of this work is to present, in the context of the Fuzzy Inductive Reasoning Fault Monitoring System (FIRFMS), a new fault detection technique called enveloping and an enhancement of the fault identification method based on the model acceptability measure. Both contributions allow a more robust and reliable FIRFMS fault detection and identification processes. The enveloping technique and the model acceptability measure are applied to three applications of quite different areas. The first one corresponds to an electric circuit model previously used for such purpose in the literature. The second one is a biomedical system, the human central nervous system (CNS) control. It is the first attempt to apply the FIRFMS to support medical decisions. The third and last one corresponds to a water demand distribution system. The electric circuit is used to show that the enhanced FIRFMS outperforms the previous FIRFMS. The biomedical and water demand distribution systems are presented to show the good performance of the new FIRFMS.

Low level control of an omnidirectional mobile robot

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.

VisualBlock-FIR for fault detection and identification: application to the DAMADICS benchmark problem

This paper describes a fault diagnosis system (FDS) for non-linear plants based on fuzzy logic. The proposed scheme, named VisualBlock-FIR, runs under the Simulink framework and enables early fault detection and identification. During fault detection, the FDS should recognize that the plant behavior is abnormal, and therefore, that the plant is not working properly. During fault identification, the FDS should conclude which type of failure has occurred. The enveloping and acceptability measures introduced in VisualBlock-FIR enhance the robustness of the overall process. The final part of this research shows how the proposed approach is used for tackling faults of the DAMADICS benchmark.

Modeling Wine Preferences from Physicochemical Properties using Fuzzy Techniques

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.