Interactive visual analytics-based diagnosis and traceback of shape quality anomalies in multi-category heavy plates
The quality of the shape of heavy plates is crucial for the product's performance and market competitiveness.Due to the complexity of the heavy plate production process and the characteristics of the production data,which are high-dimensional,multi-source,heterogeneous,and large-scale,effectively analyzing,diagnosing,and tracing the causes of shape quality anomalies is a highly challenging task.Based on data visualization and human-computer interaction technologies,this paper proposes an interactive visual analytics-based approach and system for diagnosing and tracing shape quality anomalies in multi-specification heavy plates.The system provides users with an intuitive and in-depth industrial big data analysis and exploration environment through multiple perspectives and multi-level data presentation methods.It enables rapid identification of shape quality anomalies through human-computer interaction,analyzes the inherent association between production process data and shape quality changes,and traces back to the potential causes of shape anomalies.Integrated experiments conducted with actual heavy plate production data from a domestic steel company confirm the effectiveness and practicality of the proposed approach and system.
plate shape qualityquality anomalydiagnosis and trackingvisual analyticshuman-computer interactionindustrial big data
万方数据
