Manually defined control limits remain a common strategy for quality control in manufacturing due to their ease of deployment on the shop floor compared to more advanced data analysis approaches. Despite their continued importance, there is no systematic method of defining these control limits. However, sub-optimal control limits can lead to undetected faults or cause unnecessary interruption to production. This manuscript presents an algorithm that systematizes this manual process into an efficient search task. We conceptualized the search task as a sequence of sub-problems that are based on the conventional steps taken by process experts when defining control limits. This algorithm can be integrated into an expert tool for shop floor personnel to automate the definition of control limits in annotated time series data. We demonstrate the efficacy of the control limits found by our algorithm by comparing them to those manually defined by process experts in real-world process data from the automotive industry. Furthermore, we show that our algorithm generalizes to traditional time series classification problems and achieves state-of-the-art performance on selected benchmark datasets. Our work is the first effort in automating the otherwise manual definition of control limits for fault detection.

与更先进的数据分析方法相比，手动定义的控制限制仍然是制造中质量控制的常见策略，因为它们易于在车间部署。尽管它们仍然很重要，但没有定义这些控制限制的系统方法。然而，次优控制限制可能导致未检测到的故障或导致不必要的生产中断。这份手稿提出了一种算法，该算法将这个手动过程系统化为一个有效的搜索任务。我们将搜索任务概念化为一系列子问题，这些子问题基于流程专家在定义控制限制时采取的常规步骤。该算法可以集成到车间人员的专家工具中，以自动定义带注释的时间序列数据中的控制限。我们通过将我们的算法发现的控制限与来自汽车行业的实际过程数据中的过程专家手动定义的控制限进行比较来证明它们的有效性。此外，我们表明我们的算法可以推广到传统的时间序列分类问题，并在选定的基准数据集上实现最先进的性能。我们的工作是自动化故障检测控制限制的手动定义的第一次努力。