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

J. A. Perez-Benitez, L. R. Padovese

Research output: Contribution to journalArticleResearchpeer-review

1 Citation (Scopus)

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. © 2012 Elsevier Ltd. All rights reserved.
Original languageAmerican English
Pages (from-to)974-983
Number of pages10
JournalEngineering Applications of Artificial Intelligence
DOIs
StatePublished - 1 Mar 2013

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Time series
Feature extraction
Neural networks
Online systems
Recycling
Topology
Feedback
Steel

Cite this

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title = "A system for classification of time-series data from industrial non-destructive device",
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. {\circledC} 2012 Elsevier Ltd. All rights reserved.",
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A system for classification of time-series data from industrial non-destructive device. / Perez-Benitez, J. A.; Padovese, L. R.

In: Engineering Applications of Artificial Intelligence, 01.03.2013, p. 974-983.

Research output: Contribution to journalArticleResearchpeer-review

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