Prioritising maintenance activities in throughput bottlenecks increases the throughput from the production system. To facilitate the planning and execution of maintenance activities, throughput bottlenecks in the production system must be identified and diagnosed. Various research efforts have developed data-driven approaches using real-time machine data to identify throughput bottlenecks in the system. However, these efforts have mainly focused on identifying bottlenecks and only offer limited maintenance-related diagnostics for them. Moreover, these research efforts have been proposed from an academic perspective using rigorous scientific methods. A number of challenges must be addressed, if existing data-driven approaches are to be adapted to real-world practice. These include identifying relevant data types, data pre-processing and data modelling. Such challenges can be better addressed by including maintenance-practitioner input when developing data-driven approaches. The aim of this paper is therefore to demonstrate a data-driven approach to diagnosing throughput bottlenecks, using the combined knowledge of the maintenance and data-science domains. Diagnostic insights into throughput bottlenecks are obtained using unsupervised machine-learning techniques. The demonstration uses real-world machine datasets extracted from the production line. The novelty of the research presented in this paper is that it shows how inputs from maintenance practitioners can be used to develop data-driven approaches for diagnosing throughput bottlenecks having more practical relevance. By gaining these diagnostic insights, maintenance practitioners can better understand shop-floor throughput bottleneck behaviours from a maintenance perspective and thus prioritise various maintenance actions.

中文翻译：

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从维护角度诊断吞吐量瓶颈的数据驱动方法

在吞吐量瓶颈中优先维护活动会增加生产系统的吞吐量。为了便于维护活动的规划和执行，必须识别和诊断生产系统中的吞吐量瓶颈。各种研究工作已经开发出数据驱动的方法，使用实时机器数据来识别系统中的吞吐量瓶颈。然而，这些努力主要集中在识别瓶颈上，并且只为它们提供有限的与维护相关的诊断。此外，这些研究工作是从学术角度使用严格的科学方法提出的。如果要使现有的数据驱动方法适应现实世界的实践，则必须解决许多挑战。这些包括识别相关数据类型，数据预处理和数据建模。通过在开发数据驱动的方法时包含维护从业人员的意见，可以更好地解决此类挑战。因此，本文的目的是展示一种数据驱动的方法来诊断吞吐量瓶颈，使用维护和数据科学领域的综合知识。使用无监督机器学习技术获得对吞吐量瓶颈的诊断见解。该演示使用从生产线中提取的真实机器数据集。本文提出的研究的新颖之处在于，它展示了如何使用来自维护从业人员的输入来开发数据驱动的方法，以诊断具有更多实际相关性的吞吐量瓶颈。通过获得这些诊断见解，