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This paper proposes a new fault tolerant control methodology using Fuzzy Internal Model Control (IMC) for nonlinear systems. The models (direct and inverse plant models) used in the IMC controller are generated by an adaptive neural network called ANFIS, which implements a fuzzy inference system of Takagi-Sugeno type. The inverse model of the IMC controller is reconfigured by exploiting information...
This paper presents a new system for fault diagnosis based in a neural network approach to Principal Component Analysis (PCA). An index set is defined based on neural PCA in order to detect and characterize faults, which has been tested on a hydraulic three-thanks system and on an electrical engine obtaining high success ratio of fault detection and characterization.
This paper deals with the develop of a new recurrent neuro-fuzzy model for complex systems from input-output data. In this paper a recurrent fuzzy neural network, called RFasArt (Recurrent FasArt), has been applied to model a complex biotechnological process: a wastewater treatment plant. This network is based on the Adaptative Resonance Theory (ART) but introducing formalisms from the fuzzy set theory...
A new Fault Tolerant Control System (FTCS) is proposed in this paper. The FTCS consists of three elements: the basic controller, which in this paper is a multivariable model-based predictive control (specially the EPSAC: Extended Prediction Self-Adaptative Control with constraints), a FDI scheme that is based in a linear model of the system and thresholds verification and finally a reconfiguration...
The aim of this paper is to propose a general methodology to improve the linguistic-accuracy trade-off of fuzzy models, applicable to any rule-based fuzzy model. Here, the neuro-fuzzy system FasArt (Fuzzy Adaptive System ART based) is used to obtain rule-based fuzzy models, as shown in previous papers and works. FasArt, however, has the usual drawbacks, from the linguistic point of view, of most (precise)...
In this paper a method that integrates neural networks (NN) and independent component analysis (ICA) is used to detect faults in non-linear plants. The neural networks are used to calculate a non-linear and dynamic model of the process in normal operation conditions. ICA is used to monitor and to detect faults in the process using, instead of the measured variables of the process, the residuals calculated...
Fuzzy modeling is one of the best known techniques to model systems and processes. In most cases, as in data-driven fuzzy modeling, these fuzzy models reach a high accuracy, but show poor performance in complexity or interpretability, which are key aspects of Fuzzy Logic.
In the work presented in this paper Statistical Process Control (SPC) techniques are applied to a model-based Fault Detection and Isolation (FDI) approach. The residuals, produced as outputs from the FDI system, are manipulated with typical SPC charts to improve the overall diagnosis process. The charts explained in this work: Shewhart control chart, Cumulative Sum (CUSUM) control chart and Exponentially...
Fuzzy modeling is one of the most known and used techniques in different areas to emulate the behavior of systems and processes. In most cases, as in data-driven fuzzy modeling, these fuzzy models reach a high performance from the point of view of accuracy, but from other points of view, such as complexity or interpretability, the models can present a poor performance. Several approaches are found...
This paper presents a global monitoring and fault detection approach considering the different operation points, start-ups and transitory states that can appear during plant operation. Stationary states have been monitored using the classical PCA approach and start-ups, while grade transition due to, for example changes in the set-point, are monitored using batch and semi-batch PCA-based methods....
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