Industry 4.0 has led to a huge increase in data coming from machine maintenance. At the same time, advances in Natural Language Processing (NLP) and Large Language Models provide new ways to analyse this data. In our research, we use NLP to analyse maintenance work orders, and specifically the descriptions of failures and the corresponding repair actions. Many NLP studies have focused on failure descriptions for categorising them, extracting specific information about failure, or supporting failure analysis methodologies (such as FMEA). Whereas, the analysis of repair actions and its relationship with failure remains underexplored. Addressing this gap, our study makes three significant contributions. Firstly, we focused on the Italian language, which presents additional challenges due to the dominance of NLP systems that are mainly designed for English. Secondly, it proposes a method for automatically subdividing a repair action into a set of sub-tasks. Lastly, it introduces an approach that employs association rule mining to recommend sub-tasks to maintainers when addressing failures. We tested our approach with a case study from an automotive company in Italy. The case study provides insights into the current barriers faced by NLP applications in maintenance, offering a glimpse into the future opportunities for smart maintenance systems.

中文翻译：

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使用自然语言处理将维护操作分解为子任务：意大利汽车公司的案例研究


工业 4.0 导致机器维护数据大幅增加。与此同时，自然语言处理 (NLP) 和大型语言模型的进步提供了分析这些数据的新方法。在我们的研究中，我们使用自然语言处理来分析维修工单，特别是故障的描述和相应的维修动作。许多 NLP 研究重点关注故障描述，以便对其进行分类、提取有关故障的特定信息或支持故障分析方法（例如 FMEA）。然而，对修复操作及其与故障的关系的分析仍有待探索。为了解决这一差距，我们的研究做出了三项重大贡献。首先，我们专注于意大利语，由于主要为英语设计的 NLP 系统占据主导地位，意大利语带来了额外的挑战。其次，它提出了一种自动将修复操作细分为一组子任务的方法。最后，它介绍了一种在解决故障时采用关联规则挖掘向维护人员推荐子任务的方法。我们通过意大利一家汽车公司的案例研究测试了我们的方法。该案例研究深入了解了当前 NLP 应用在维护方面面临的障碍，并让我们一睹智能维护系统的未来机遇。