The success of data-driven maintenance is strongly dependant on effective use of AI and multi-structured data sources. Introducing and integrating an AI-enhanced methodology in reliability-centred maintenance study of complex production systems leads to reducing failure rates and optimizing availability. In manufacturing enterprises, information about machine failures and expert knowledge are often stored in digital shift books (DSB). This paper introduces a transferable and scalable AI-enhanced methodology for DSB in automotive industry, which enhances Overall Equipment Efficiency (OEE) by optimizing availability through reducing the Mean Failure Detection Time (MFDT). Experimental investigations in the use-case suggest an OEE increase by over 5%.

数据驱动维护的成功在很大程度上取决于人工智能和多结构数据源的有效使用。在复杂生产系统的以可靠性为中心的维护研究中引入和集成人工智能增强方法可以降低故障率并优化可用性。在制造企业中，有关机器故障的信息和专家知识通常存储在数字班次簿 (DSB) 中。本文介绍了一种用于汽车行业 DSB 的可转移且可扩展的 AI 增强方法，该方法通过减少平均故障检测时间 (MFDT) 来优化可用性，从而提高整体设备效率 (OEE)。用例中的实验研究表明 OEE 增加了 5% 以上。