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

被引:1
|
作者
Perez-Benitez, J. A. [1 ]
Padovese, L. R. [2 ]
机构
[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
基金
巴西圣保罗研究基金会;
关键词
MBN decorrelation; Plastic deformation; Carbon content; Non-destructive methods; NEURAL-NETWORK; ALGORITHM; SIGNALS;
D O I
10.1016/j.engappai.2012.09.006
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
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.
引用
收藏
页码:974 / 983
页数:10
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