从维护工单中提取 KPI——专家标注、文本分类与 AI 辅助标注用于计算风力涡轮机故障率的比较
KPI Extraction from Maintenance Work Orders -- A Comparison of Expert Labeling, Text Classification and AI-Assisted Tagging for Computing Failure Rates of Wind Turbines
AI总结：
本文比较专家标注、文本分类和 AI 辅助标注三种维护工单 KPI 提取方法，验证其在风力涡轮机故障率与可靠性分析中的准确率和效率。
摘要：
维护工单通常用于记录风力涡轮机运行和维护相关信息。其中包括主动和被动风力涡轮机停机的详细情况，例如预防性维护和纠正性维护。然而，维护工单中包含的信息通常是非结构化的，难以分析，这给希望利用这些信息优化运行和维护的决策者带来了挑战。为解决这一问题，本研究比较了三种不同方法，用于依据维护工单中的绩效指标计算可靠性。第一种方法由领域专家对维护工单进行人工标注，使用工业指南中定义的模式相应地分配标签。第二种方法开发一个模型，利用文本分类方法自动标注维护工单。通过该方法，我们能够分别取得 0.75 和 0.85 的宏平均 F1 分数与加权平均 F1 分数。第三种技术使用 AI 辅助标注工具对原始维护信息进行标注和结构化，并结合一种新颖的基于规则的方法，提取与故障率计算相关的维护工单。在我们的实验中，与另外两种方法相比，AI 辅助工具使标注时间下降了 88%，而专家标注和文本分类在 KPI 提取方面更加准确。总体而言，我们的研究结果提高了从维护工单中提取维护信息的效率，使可靠性关键绩效指标评估成为可能，从而支持风力涡轮机运行和维护的优化。
英文摘要：
Maintenance work orders are commonly used to document information about wind turbine operation and maintenance. This includes details about proactive and reactive wind turbine downtimes, such as preventative and corrective maintenance. However, the information contained in maintenance work orders is often unstructured and difficult to analyze, presenting challenges for decision-makers wishing to use it for optimizing operation and maintenance. To address this issue, this work compares three different approaches to calculate reliability by performance indicators from maintenance work orders. The first approach involves manual labeling of the maintenance work orders by domain experts, using the schema defined in an industrial guideline to assign the label accordingly. The second approach involves the development of a model that automatically labels the maintenance work orders using text classification methods. Through this method, we are able to achieve macro average and weighted average F1-Scores of 0.75 and 0.85 respectively. The third technique uses an AI-assisted tagging tool to tag and structure the raw maintenance information, together with a novel rule-based approach for extracting relevant maintenance work orders for failure rate calculation. In our experiments the AI-assisted tool leads to a 88% drop in tagging time in comparison to the other two approaches, while expert labeling and text classification are more accurate in KPI extraction. Overall, our findings make extracting maintenance information from maintenance work orders more efficient, enable the assessment of reliability key performance indicators and therefore support the optimization of wind turbine operation and maintenance.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
