Big data driven jobs remaining time prediction in discrete manufacturing system: a deep learning-based approach. Issue 9 (2nd May 2020)

• Record Type: Journal Article
• Authors: Fang, Weiguang; Guo, Yu; Liao, Wenhe; Ramani, Karthik; Huang, Shaohua
• Is Part Of: International journal of production research. Volume 58:Issue 9(2020)
• Page Start: 2751
• Page End: 2766
• Publication Date: 2020-05-02
• Subjects: big data -- job shop -- jobs remaining time prediction -- stacked sparse autoencoder -- deep learning; Factory management -- Periodicals; 658.57
• DOI: 10.1080/00207543.2019.1602744
• Languages: English
• ISSNs: 0020-7543

Abstract

Abstract : Implementing advanced big data (BD) analytic is significant for successful incorporation of artificial intelligence in manufacturing. With the widespread deployment of smart sensors and internet of things (IOT) in the job shop, there is an increasing need for handling manufacturing BD for predictive manufacturing. In this study, we conceive the jobs remaining time (JRT) prediction during manufacturing execution based on deep learning (DL) with production BD. We developed a procedure for JRT prediction that includes three parts: raw data collection, candidate dataset design and predictive modelling. First, the historical production data are collected by the widely deployed IOT in the job shop. Then, the candidate dataset is formalised to capture various contributory factors for JRT prediction. Further, a DL model named stacked sparse autoencoder (S-SAE) is constructed to learn representative features from high dimensional manufacturing BD to make robust and accurate JRT prediction. Our work represents the first DL model for the JRT prediction at run time during production. The proposed methods are applied in a large-scale job shop that is equipped with 44 machine tools and produces 13 types of parts. Lastly, the experimental results show the S-SAE model has higher accuracy than previous linear regression, back-propagation network, multi-layer network and deep belief network in JRT prediction.