ViDX: Visual Diagnostics of Assembly Line Performance in Smart Factories

Visual analytics plays a key role in the era of connected industry (or industry 4.0, industrial internet) as modern machines and production (assembly) lines can generate large amount of data, and effective visual exploration techniques are needed for troubleshooting, process optimization and decision making. However, developing effective visual analytics solutions for this application domain is a challenging task due to the sheer volume and the complexity of the data generated in the sophisticated manufacturing processes. In this paper, we report the design and implementation of a comprehensive visual analytics system, ViDX. It supports both real-time tracking of the assembly line performance, and exploration of historical data to identify the inefficiencies, locate the abnormalities, and form hypotheses about their causes and effects in assembly lines. The system is designed based on a set of requirements gathered through discussions with the managers and operators from manufacturing sites. It features interlinked views displaying data at different level-of-details. In particular, we apply and extend Marey’s graph by introducing a time-aware outlier-preserving visual aggregation technique to support effective troubleshooting in manufacturing processes. We also introduce two novel interaction techniques, namely quantiles brush and samples brush, for the users to interactively steer the outlier detection algorithms. We evaluate the system with example use cases and an in-depth user interview, both conducted together with the managers and operators from manufacturing plants. The result demonstrates its effectiveness and usability, and reports a successful pilot application of visual analytics for manufacturing in smart factories.