Visual Data Science for Industrial Applications

Abstract

Advances in sensor and data acquisition technology and in methods of data analysis pose many research challenges but also promising application opportunities in many domains. The need to cope with and leverage large sensor data streams is particularly urgent for industrial applications due to strong business competition and innovation pressure. In maintenance, for example, sensor readings of machinery or products may allow to predict at which point in time maintenance will be required and allow to schedule service operations respectively. Another application is the discovery of the relationships between production input parameters on the quality of the output products. Analysis of respective industrial data typically cannot be done in an out-of-the-box manner but requires to incorporate background knowledge from fields such as engineering, operation research, and business to be effective. Hence, approaches for interactive and visual data analysis can be particularly useful for analyzing complex industrial data, combining the advantages of modern automatic data analysis with domain knowledge and hypothesis generation capabilities of domain experts. In this chapter, we introduce some of the main principles of visual data analysis. We discuss how techniques for data visualization, data analysis, and user interaction can be combined to analyze data, generate and verify hypotheses about patterns in data, and present the findings. We discuss this in the light of important requirements and applications in the analysis of industrial data and based on current research in the area. We provide examples for visual data analysis approaches, including conditionmonitoring, quality control, and production planning.