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2019 | OriginalPaper | Chapter

Control Chart Pattern Recognition Based on Convolution Neural Network

Authors : Zhihong Miao, Mingshun Yang

Published in: Smart Innovations in Communication and Computational Sciences

Publisher: Springer Singapore

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Abstract

Quality control chart pattern recognition plays an extremely important role in controlling the products quality. By means of real-time monitoring control, the abnormal status of the product during production can be timely observed. A method of control pattern recognition based on convolution neural network is proposed. Firstly, the control chart patterns (CCPs) are analyzed, the statistical characteristics and shape features of the control charts are considered, and the appropriate characteristics to distinguish the different abnormal patterns are selected; secondly, deep learning convolution neural network is trained and learned; finally, the feasibility and effectiveness of the control chart pattern recognition are verified through Monte Carlo simulation.

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Metadata
Title
Control Chart Pattern Recognition Based on Convolution Neural Network
Authors
Zhihong Miao
Mingshun Yang
Copyright Year
2019
Publisher
Springer Singapore
Book
Smart Innovations in Communication and Computational Sciences

Print ISBN: 978-981-10-8970-1

Electronic ISBN: 978-981-10-8971-8

Copyright Year: 2019

DOI
https://doi.org/10.1007/978-981-10-8971-8_9

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