Stacked denoising autoencoder‐based feature learning for out‐of‐control source recognition in multivariate manufacturing process

In multivariate statistical process control (MSPC), regular multivariate control charts (eg, T2) are shown to be effective in detecting out‐of‐control signals based upon an overall statistic. But these charts do not relieve the need for multivariate process pattern recognition (MPPR). MPPR would be...

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Vydané v:Quality and reliability engineering international Ročník 35; číslo 1; s. 204 - 223
Hlavní autori: Yu, Jianbo, Zheng, Xiaoyun, Wang, Shijin
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Bognor Regis Wiley Subscription Services, Inc 01.02.2019
Predmet:
Control charts
deep learning
feature learning
Machine learning
multivariate manufacturing process
Noise reduction
Pattern recognition
Process controls
process pattern recognition
Signal processing
stacked denoising autoencoder
Statistical analysis
Statistical process control
ISSN:0748-8017, 1099-1638
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Shrnutí:In multivariate statistical process control (MSPC), regular multivariate control charts (eg, T2) are shown to be effective in detecting out‐of‐control signals based upon an overall statistic. But these charts do not relieve the need for multivariate process pattern recognition (MPPR). MPPR would be very useful for quality operators to locate the assignable causes that give rise to out‐of‐control situation in multivariate manufacturing process. Deep learning has been widely applied and obtained many successes in image and visual analysis. This paper presents an effective and reliable deep learning method known as stacked denoising autoencoder (SDAE) for MPPR in manufacturing processes. This study will concentrate on developing a SDAE model to learn effective discriminative features from the process signals through deep network architectures. Feature visualization is performed to explicitly present feature representations of the proposed SDAE model. The experimental results illustrate that the proposed SDAE model is capable of implementing detection and recognition of various process patterns in complicated multivariate processes. Analysis from this study provides the guideline in developing deep learning‐based MSPC systems.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0748-8017
1099-1638
DOI:10.1002/qre.2392