The digitalization of manufacturing involves machines equipped with sensors that collect, produce, and exchange data machine-to-machine and machine-to-human in real-time. As the data generated within a production process can be massive and overwhelming for human users, support is needed to understand and explore this data, and drive decisions from it. First, the data has to be monitored and recorded using methods that can handle massive datasets. Next, the collected data has to be analyzed (often in real-time) to, e.g., (i) identify undetected process correlations, (ii) forecast the product quality, and (iii) perform root-cause analysis of failures or problems. The analysis becomes even more valuable when the production process is divided into repeating tasks, producing a vast amount of comparable data. For instance, in automotive durability tests, engineers investigate an engine's condition using multiple sensors, recording data from repeating test cycles. Tests can span dozens or hundreds of cycles, and thousands of runtime hours, making it difficult for engineers to collect and monitor each iteration's data to detect interesting data, such as anomalies. We propose an interactive visual analytics approach that displays the iterations of durability tests as a collection of color-encoded cycle glyphs to tackle this issue. With our approach, domain users including test engineers can readily monitor the test, detect potential anomalies, and intuitively analyze, report and document the detected anomalies. This research is conducted in close collaboration with our partner from the automotive sector and shows the effectiveness and efficiency of a prototype with a pair analytics evaluation study. We open up directions for future work, including a visual interactive labeling concept for anomaly classification.
