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A Wide-Deep-Sequence Model-Based Quality Prediction Method in Industrial Process Analysis

Ren, Lei ; Meng, Zihao ; Wang, Xiaokang ; Lu, Renquan ; Yang, Laurence T

IEEE transaction on neural networks and learning systems, 2020-09, Vol.31 (9), p.3721-3731

IEEE

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  • Título:
    A Wide-Deep-Sequence Model-Based Quality Prediction Method in Industrial Process Analysis
  • Autor: Ren, Lei ; Meng, Zihao ; Wang, Xiaokang ; Lu, Renquan ; Yang, Laurence T
  • Assuntos: Analytical models ; Data mining ; Data models ; Feature extraction ; Industrial artificial intelligence (AI) ; industrial big data ; Industrial Internet of Things ; Predictive models ; Product design ; product quality prediction ; Quality assessment ; wide-deep-sequence (WDS) model
  • É parte de: IEEE transaction on neural networks and learning systems, 2020-09, Vol.31 (9), p.3721-3731
  • Notas: ObjectType-Article-1
    SourceType-Scholarly Journals-1
    ObjectType-Feature-2
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  • Descrição: Product quality prediction, as an important issue of industrial intelligence, is a typical task of industrial process analysis, in which product quality will be evaluated and improved as feedback for industrial process adjustment. Data-driven methods, with predictive model to analyze various industrial data, have been received considerable attention in recent years. However, to get an accurate prediction, it is an essential issue to extract quality features from industrial data, including several variables generated from supply chain and time-variant machining process. In this article, a data-driven method based on wide-deep-sequence (WDS) model is proposed to provide a reliable quality prediction for industrial process with different types of industrial data. To process industrial data of high redundancy, in this article, data reduction is first conducted on different variables by different techniques. Also, an improved wide-deep (WD) model is proposed to extract quality features from key time-invariant variables. Meanwhile, an long short-term memory (LSTM)-based sequence model is presented for exploring quality information from time-domain features. Under the joint training strategy, these models will be combined and optimized by a designed penalty mechanism for unreliable predictions, especially on reduction of defective products. Finally, experiments on a real-world manufacturing process data set are carried out to present the effectiveness of the proposed method in product quality prediction.
  • Editor: IEEE
  • Idioma: Inglês
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  • Realização: ABCD-USP USP   Logos de Redes Sociais: Freepik

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