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Stacked denoising autoencoder‐based feature learning for out‐of‐control source recognition in multivariate manufacturing process
Author(s) -
Yu Jianbo,
Zheng Xiaoyun,
Wang Shijin
Publication year - 2019
Publication title -
quality and reliability engineering international
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.913
H-Index - 62
eISSN - 1099-1638
pISSN - 0748-8017
DOI - 10.1002/qre.2392
Subject(s) - artificial intelligence , multivariate statistics , autoencoder , computer science , discriminative model , pattern recognition (psychology) , process (computing) , feature (linguistics) , deep learning , statistical process control , process control , feature extraction , visualization , machine learning , data mining , linguistics , philosophy , operating system
In multivariate statistical process control (MSPC), regular multivariate control charts (eg, T 2 ) 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.