Rotary Indexing Machines (RIMs) are widely used in manufacturing due to their ability to perform multiple production steps on a single product without manual repositioning, reducing production time and improving accuracy and consistency. Despite their advantages, little research has been done on diagnosing faults in RIMs, especially from the perspective of the actual production steps carried out on these machines. Long downtimes due to failures are problematic, especially for smaller companies employing these machines. To address this gap, we propose a diagnosis algorithm based on the product perspective, which focuses on the product being processed by RIMs. The algorithm traces the steps that a product takes through the machine and is able to diagnose possible causes in case of failure. We also analyze the properties of RIMs and how these influence the diagnosis of faults in these machines. Our contributions are three-fold. Firstly, we provide an analysis of the properties of RIMs and how they influence the diagnosis of faults in these machines. Secondly, we suggest a diagnosis algorithm based on the product perspective capable of diagnosing faults in such a machine. Finally, we test this algorithm on a model of a rotary indexing machine, demonstrating its effectiveness in identifying faults and their root causes.

翻译：旋转分度机床因其能够在单一产品上执行多个生产步骤而无需手动重新定位，从而减少生产时间并提高精度和一致性，在制造业中得到了广泛应用。尽管具有这些优势，但从这些机器实际执行的生产步骤角度出发，对其故障诊断的研究却较为匮乏。因故障导致的长时间停机尤其给使用这些机器的小型企业带来困扰。为弥补这一不足，我们提出了一种基于产品视角的诊断算法，该算法聚焦于旋转分度机床所加工的产品。该算法追踪产品在机器中经过的步骤，并能在故障发生时诊断出可能的原因。我们还分析了旋转分度机床的特性及其如何影响这些机器的故障诊断。我们的贡献有三方面：首先，分析了旋转分度机床的特性及其对故障诊断的影响；其次，提出了一种基于产品视角的诊断算法，能够诊断此类机器中的故障；最后，我们在旋转分度机床模型上测试了该算法，证明了其在识别故障及其根本原因方面的有效性。