A system for classification of time-series data from industrial non-destructive device

Authors:
J. A. Perez-Benitez;L. R. Padovese
Affiliations:
Laboratorio de Evaluacion No-Destructiva Electromagnética (LENDE), IPN-ESIME-SEPI, Zacantenco Edif. Z-4 Instituto Politécnico Nacional México, D.F., Mexico;Departamento de Engenharia Mecínica, Escola Politécnica, Universidade de São Paulo. Av. Prof. Mello Moraes, 2231, 05508-900 São Paulo, Brazil
Venue:
Engineering Applications of Artificial Intelligence
Year:
2013

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Abstract

This work proposes a system for classification of industrial steel pieces by means of magnetic nondestructive device. The proposed classification system presents two main stages, online system stage and off-line system stage. In online stage, the system classifies inputs and saves misclassification information in order to perform posterior analyses. In the off-line optimization stage, the topology of a Probabilistic Neural Network is optimized by a Feature Selection algorithm combined with the Probabilistic Neural Network to increase the classification rate. The proposed Feature Selection algorithm searches for the signal spectrogram by combining three basic elements: a Sequential Forward Selection algorithm, a Feature Cluster Grow algorithm with classification rate gradient analysis and a Sequential Backward Selection. Also, a trash-data recycling algorithm is proposed to obtain the optimal feedback samples selected from the misclassified ones.