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.

制造业的数字化涉及配备传感器的机器，这些传感器可以实时收集、生产和交换机器对机器和机器对人的数据。由于生产过程中生成的数据对于人类用户来说可能是海量且难以承受的，因此需要支持来理解和探索这些数据，并从中推动决策。首先，必须使用可以处理海量数据集的方法来监控和记录数据。接下来，必须对收集到的数据进行分析（通常是实时的），例如，(i) 识别未检测到的过程相关性，(ii) 预测产品质量，以及 (iii) 执行故障或问题的根本原因分析。当生产过程被划分为重复的任务，产生大量的可比数据时，分析变得更加有价值。例如，在汽车耐久性测试中，工程师使用多个传感器调查发动机的状况，记录重复测试循环的数据。测试可能跨越数十个或数百个周期，以及数千个运行小时，这使得工程师很难收集和监控每次迭代的数据以检测异常数据等有趣的数据。我们提出了一种交互式可视化分析方法，将耐久性测试的迭代显示为颜色编码循环字形的集合，以解决这个问题。使用我们的方法，包括测试工程师在内的域用户可以轻松监控测试，检测潜在异常，并直观地分析、报告和记录检测到的异常。这项研究是与我们汽车行业的合作伙伴密切合作进行的，并通过配对分析评估研究展示了原型的有效性和效率。我们为未来的工作开辟了方向，包括用于异常分类的视觉交互式标签概念。