Abstract The advancements in machine learning (ML) techniques open new opportunities for analysing production system dynamics and augmenting the domain expert's decision-making. A common problem for domain experts on the shop floor is detecting throughput bottlenecks, as they constrain the system throughput. Detecting throughput bottlenecks is necessary to prioritise maintenance and improvement actions and obtain greater system throughput. The existing literature provides many ways to detect bottlenecks from machine data, using statistical-based approaches. These statistical-based approaches can be best applied in environments where the statistical descriptors of machine data (such as distribution of machine data, correlations and stationarity) are known beforehand. Computing statistical descriptors involves statistical assumptions. When the machine data doesn't comply with these assumptions, there is a risk of the results being disconnected from actual production system dynamics. An alternative approach to detecting throughput bottlenecks is to use ML- based techniques. These techniques, particularly unsupervised ML techniques, require no prior statistical information on machine data. This paper proposes a generic, unsupervised ML-based hierarchical clustering approach to detect throughput bottlenecks. The proposed approach is the outcome of systematic and careful selection of ML techniques. It begins by generating a time series of the chosen bottleneck detection metric and then clustering the time series using a dynamic time-wrapping measure and a complete-linkage agglomerative hierarchical clustering technique. The results are clusters of machines with similar production dynamic profiles, revealed from the historical data and enabling the detection of bottlenecks. The proposed approach is demonstrated in two real-world production systems. The approach integrates the concept of humans in-loop by using the domain expert's knowledge.

中文翻译：

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一种用于检测制造瓶颈的通用层次聚类方法

摘要 机器学习 (ML) 技术的进步为分析生产系统动态和增强领域专家的决策开辟了新的机会。车间领域专家的一个常见问题是检测吞吐量瓶颈，因为它们限制了系统吞吐量。检测吞吐量瓶颈对于优先维护和改进操作并获得更大的系统吞吐量是必要的。现有文献提供了许多使用基于统计的方法从机器数据中检测瓶颈的方法。这些基于统计的方法最适用于预先知道机器数据的统计描述符（例如机器数据的分布、相关性和平稳性）的环境。计算统计描述符涉及统计假设。当机器数据不符合这些假设时，结果就有可能与实际生产系统动态脱节。检测吞吐量瓶颈的另一种方法是使用基于 ML 的技术。这些技术，尤其是无监督机器学习技术，不需要机器数据的先验统计信息。本文提出了一种通用的、无监督的基于 ML 的层次聚类方法来检测吞吐量瓶颈。所提出的方法是系统和仔细选择 ML 技术的结果。它首先生成所选瓶颈检测指标的时间序列，然后使用动态时间包装度量和完整链接凝聚层次聚类技术对时间序列进行聚类。结果是具有相似生产动态配置文件的机器集群，从历史数据中揭示出来并能够检测瓶颈。所提出的方法在两个真实世界的生产系统中得到了证明。该方法通过使用领域专家的知识整合了人在回路中的概念。