Process situation assessment: From a fuzzy partition to a finite state machine

Kempowsky, Tatiana; Subias, Albert Corominas; Aguilar-Martín, J.

doi:10.1016/j.engappai.2005.12.012

Cited by 44 publications

(24 citation statements)

References 4 publications

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“…Classical automatic methods are based on a genetic algorithm [28] or clustering [29], [30]. However, these kinds of methods need large number of training samples to succeed.…”

Section: A Input Fuzzification Stepmentioning

confidence: 99%

Improving Fuzzy Rule Classifier by Extracting Suitable Features From Capacities With Respect to the Choquet Integral

Schmitt

Bombardier

Wendling

2008

IEEE Trans. Syst., Man, Cybern. B

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Abstract-In this paper, an iterative method to select suitable features in an industrial pattern recognition context is proposed. It combines a global method of feature selection and a fuzzy linguistic rule classifier. It is applied to an industrial fabric textile context. The aim of the global vision system is to identify textile fabric defects. From the related industrial process, the training data sets are small, and some are incomplete. Moreover, the recognition step must be compatible with the time constant of the system, which generally imposes low complexity for the system. The choice of the most relevant features and the reduction of their number are important to respect these constraints. The feature selection method is based on the analysis of indexes extracted on the lattice defined from training in relation with the Choquet integral. This selection step is embedded in an iterative algorithm to discard weaker features in order to decrease the number of rules while keeping good recognition rates. The recognition step is done with a fuzzy reasoning classifier that is well adapted for this application case. The proposed method is quite efficient with small learning data sets because of the generalization capacity of both the feature selection and recognition steps. The experimental study shows the wanted behavior of this approach: the feature number decreases, whereas the recognition rate increases. Thus, the total number of generated fuzzy rules is reduced.

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“…Classical automatic methods are based on a genetic algorithm [28] or clustering [29], [30]. However, these kinds of methods need large number of training samples to succeed.…”

Section: A Input Fuzzification Stepmentioning

confidence: 99%

Improving Fuzzy Rule Classifier by Extracting Suitable Features From Capacities With Respect to the Choquet Integral

Schmitt

Bombardier

Wendling

2008

IEEE Trans. Syst., Man, Cybern. B

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“…A partir de los resultados de clasificación obtenidos en entrenamiento, el grafo experimental de la Fig. 9 fue construido con base en los estados funcionales y su evolución en el proceso, utilizando la denominada matriz de transiciones (Kempowsky, 2006), ver Tabla 4.…”

Section: Validación De Clases (Estados Funcionales)unclassified

Diseño de un Clasificador Difuso para el Establecimiento de los Estados Funcionales de un Sistema de Producción de Aire Medicinal

2017

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ResumenEste artículo presenta el diseño de un clasificador difuso para establecer los estados funcionales de un sistema de producción de aire medicinal. La implementación de la lógica difusa en el clasificador permite que un objeto pueda pertenecer a múltiples clases, previamente definidas en la sintonización del clasificador. Para el diseño y sintonización del clasificador difuso se utilizó el histórico de datos del proceso y se hizo un pre-procesamiento de la información eliminando información redundante. Las clases obtenidas fueron sometidas a un análisis gráfico multivariable. La clasificación obtenida en el entrenamiento permitió identificar todos los estados funcionales útiles para el monitoreo del proceso. Todos los datos de prueba fueron clasificados acertadamente. El establecimiento de estados funcionales a partir de la clasificación difusa se presenta como una alternativa de la inteligencia artificial que permitirá en otra fase la programación de acciones en la ingeniería de mantenimiento a partir del diagnóstico en los procesos. Palabras clave: clasificador difuso; estados funcionales; producción de aire medicinal Design of a Fuzzy Classifier for Establishing the Functional States of a Medical Air Production System AbstractThis article presents the design of a fuzzy classifier to establish the functional states of a medical air production system. The implementation of fuzzy logic in the classifier allows an object to belong to multiple classes, previously defined, in the tuning of the classifier. For the design of the classifier, the process historical data was used and a pre-processing of the information was done by eliminating redundant information. The classes obtained were subjected to a multivariable graphical analysis. The classification obtained in the training allowed identifying all functional states useful for the monitoring of the process. All test data were accurately classified. The establishment of functional states based on the fuzzy classification is presented as an alternative of the use of artificial intelligence for the scheduling of actions in the maintenance engineering, from the diagnosis of the processes.

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“…Additionally, fuzzy membership degrees facilitate analyzing simultaneous memberships among functional states. In [10], [11], [12], the authors show how fuzzy clustering facilitates industrial processes monitoring; but, new sensors and data gathering equipments have enabled processes data to be collected with greater frequency and quality, creating huge amounts of measurements to analyze [13]. Thus, dealing with large amounts of process data hinders the identification of abnormal conditions, because they are usually highly correlated, and have with nonlinear behaviors [14].…”

Section: Introductionmentioning

confidence: 99%

Unsupervised feature selection based on fuzzy partition optimization for industrial processes monitoring

Uribe

Isaza

2011

2011 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications (CIMSA) Proceedings

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Industrial processes have enormous volumes of complex and high dimensional data available, with poorly defined domains and redundant, noisy or inaccurate measures with unknown parameters. Therefore, using just relevant and informative variables will decrease the high dimensionality in the data and will facilitate the use of data-based methods for developing monitoring and fault detection systems. In this paper, a new unsupervised feature selection method based on partition optimization for fuzzy clustering based monitoring systems is proposed. Application on monitoring an intensification reactor, the 'open plate reactor (OPR)' is studied. Results show fewer variables are needed to classify process data into accurate functional states.

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Process situation assessment: From a fuzzy partition to a finite state machine

Cited by 44 publications

References 4 publications

Improving Fuzzy Rule Classifier by Extracting Suitable Features From Capacities With Respect to the Choquet Integral

Improving Fuzzy Rule Classifier by Extracting Suitable Features From Capacities With Respect to the Choquet Integral

Diseño de un Clasificador Difuso para el Establecimiento de los Estados Funcionales de un Sistema de Producción de Aire Medicinal

Unsupervised feature selection based on fuzzy partition optimization for industrial processes monitoring

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