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2019 | OriginalPaper | Buchkapitel

Control Chart Pattern Recognition Based on Convolution Neural Network

verfasst von : Zhihong Miao, Mingshun Yang

Erschienen in: Smart Innovations in Communication and Computational Sciences

Verlag: 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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Metadaten
Titel
Control Chart Pattern Recognition Based on Convolution Neural Network
verfasst von
Zhihong Miao
Mingshun Yang
Copyright-Jahr
2019
Verlag
Springer Singapore
DOI
https://doi.org/10.1007/978-981-10-8971-8_9