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

Author(s)

Perez-Benitez, J. A. ^[1] ; Padovese, L. R. ^[2]

Affiliation

^[1] Inst Politecn Nacl, IPN ESIME SEPI, Lab Evaluac Nodestruct Electromagnet LENDE, Mexico City, DF - Mexico

^[2] Univ Sao Paulo, Escola Politecn, Dept Engn Mecan, BR-05508900 Sao Paulo - Brazil

• Document type: Journal article
• Source: ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE; v. 26, n. 3, p. 974-983, MAR 2013.
• Web of Science Citations: 1

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. (c) 2012 Elsevier Ltd. All rights reserved. (AU)

• FAPESP's process: 08/10859-0 - Expert System for Magnetic Non Destructive Testing
• Grantee: José Alberto Pérez Benitez
• Support Opportunities: Scholarships in Brazil - Post-Doctoral