Visually enhanced situation awareness for complex manufacturing facility monitoring in smart factories. (February 2018)

• Record Type: Journal Article
• Authors: Zhou, Fangfang; Lin, Xiaoru; Luo, Xiaobo; Zhao, Ying; Chen, Yi; Chen, Ning; Gui, Weihua
• Is Part Of: Journal of visual languages & computing. Volume 44(2018)
• Page Start: 58
• Page End: 69
• Publication Date: 2018-02
• Subjects: Visual analytics -- Situation awareness -- Manufacturing industry -- Smart factory -- Roller Hearth Kiln; Visual programming languages (Computer science) -- Periodicals; Visual programming (Computer science) -- Periodicals; Programming languages (Electronic computers) -- Semantics -- Periodicals; Langages de programmation visuelle -- Périodiques; Programmation visuelle -- Périodiques; Langages de programmation -- Sémantique -- Périodiques; Programming languages (Electronic computers) -- Semantics; Visual programming (Computer science); Visual programming languages (Computer science); Periodicals; Electronic journals; 005
• DOI: 10.1016/j.jvlc.2017.11.004
• Languages: English
• ISSNs: 1045-926X

Abstract

Abstract: With the widespread application of networked information-based technologies throughout industry manufacturing, modern manufacturing facilities give rise to unprecedented levels of process data generation. Data-rich manufacturing environments provide a broad stage on which advanced data analytics play leading roles in creating manufacturing intelligence to support operational efficiency and process innovation. In this paper, we introduce a process data analysis solution that integrates the technologies of situation awareness and visual analytics for the routine monitoring and troubleshooting of roller hearth kiln (RHK), a complex key manufacturing facility for lithium battery cathode materials. Guided by a set of detailed scenarios and requirement analyses, we first propose a qualitative and quantitative situation assessment model to generate the comprehensive description of RHK's operating situation. An informative visual analysis system then is designed and implemented to enhance the users' abilities of situation perception and understanding for insightful anomaly root cause reasoning and efficient decision making. We conduct case studies and a user interview together with the managers and operators from manufacturing sites as system evaluation. The result demonstrates its effectiveness and prospects its possible inspiration for other similar scenarios about complex manufacturing facility monitoring in smart factories.