Optimizing maintenance practices is a continuous process that must take into account the evolving state of the equipment, resources, workers, and more. To help streamline this process, facilities need a concise procedure for identifying critical tasks and assets that have major impact on the performance of maintenance activities. This work provides a process for making data investigations more effective by discovering influential equipment, actions, and other environmental factors from tacit knowledge within maintenance documents and reports. Traditional application of text analysis focuses on prediction and modeling of system state directly. Variation in domain data, quality, and managerial expectations prevent the creation of a generic method to do this with real industrial data. Instead, text analysis techniques can be applied to discover key factors within a system, which function as indicators for further, in-depth analysis. These factors can point investigators where to find good or bad behaviors, but do not explicitly perform any anomaly detection. This paper details an adaptable procedure tailored to maintenance and industrial settings for determining important named entities within natural language documents. The procedure in this paper utilizes natural language processing techniques to extract these terms or concepts from maintenance work orders and measure their influence on Key Performance Indicators (KPIs) as defined by managers and decision makers. We present a case study to demonstrate the developed workflow (algorithmic procedure) to identify terms associated with concepts or systems which have strong relationships with a selected KPI, such as time or cost. This proof of concept uses the length of time a Maintenance Work Order (MWO) remains open from creation to completion as the relevant performance indicator. By identifying tasks, assets, and environments that have significant relevance to KPIs, planners and decision makers can more easily direct investigations to identify problem areas within a facility, better allocate resources, and guide more effective analysis for both monitoring and improving a facility. The output of the analysis workflow presented in this paper is not intended as a direct indicator of good or bad practices and assets, but instead is intended to be used to help direct and improve the effectiveness of investigations determining those. This workflow provides a preparatory investigation that both conditions the data, helps guide investigators into more productive and effective investigations of the latent information contained in human generated work logs, specifically the natural language recorded in MWOs. When this information preparing and gathering procedure is used in conjunction with other tacit knowledge or analysis tools it gives a more full picture of the efficiency and effectiveness of maintenance strategies. When properly applied, this methodology can identify pain points, highlight anomalous patterns, or verify expected outcomes of a facility’s maintenance strategy.

优化维护实践是一个连续的过程，必须考虑到设备，资源，工人等的发展状况。为了帮助简化此过程，工厂需要一个简明的程序来识别对维护活动的性能有重大影响的关键任务和资产。这项工作通过从维护文档和报告中的默认知识中发现有影响的设备，动作和其他环境因素，提供了使数据调查更加有效的过程。文本分析的传统应用直接关注系统状态的预测和建模。领域数据，质量和管理期望的变化会阻止创建用于处理实际工业数据的通用方法。反而，文本分析技术可用于发现系统中的关键因素，这些关键因素可作为进一步深入分析的指标。这些因素可以指示调查人员在哪里发现好行为或坏行为，但未明确执行任何异常检测。本文详细介绍了一种适用于维护和工业环境的适应性程序，用于确定自然语言文档中的重要命名实体。本文中的过程利用自然语言处理技术从维护工作订单中提取这些术语或概念，并衡量它们对经理和决策者定义的关键绩效指标（KPI）的影响。我们提供一个案例研究，以演示已开发的工作流程（算法过程），以识别与与选定的KPI有密切关系的概念或系统相关的术语，例如时间或成本。该概念证明使用了维护工单（MWO）从创建到完成的开放时间，作为相关的绩效指标。通过确定与KPI密切相关的任务，资产和环境，计划人员和决策者可以更轻松地指导调查，以识别设施内的问题区域，更好地分配资源，并指导更有效的分析，以监控和改善设施。本文中介绍的分析工作流程的输出并不旨在作为衡量良好或不良做法和资产的直接指标，而是旨在用来帮助指导和提高确定这些问题的调查的有效性。该工作流程提供了准备工作，既可以对数据进行条件处理，又可以帮助指导调查员对人工生成的工作日志（尤其是MWO中记录的自然语言）中包含的潜在信息进行更有效率和更有效的调查。当此信息准备和收集过程与其他默认知识或分析工具结合使用时，它将更全面地介绍维护策略的效率和有效性。如果正确应用，此方法可以识别痛点，突出异常模式或验证设施维护策略的预期结果。该工作流程提供了准备工作，既可以对数据进行条件处理，又可以帮助指导调查员对人工生成的工作日志（尤其是MWO中记录的自然语言）中包含的潜在信息进行更有效率和更有效的调查。当此信息准备和收集过程与其他默认知识或分析工具结合使用时，它将更全面地介绍维护策略的效率和有效性。如果正确应用，此方法可以识别痛点，突出异常模式或验证设施维护策略的预期结果。该工作流程提供了准备工作，既可以对数据进行条件处理，又可以帮助指导调查员对人工生成的工作日志（尤其是MWO中记录的自然语言）中包含的潜在信息进行更有效率和更有效的调查。当此信息准备和收集过程与其他默认知识或分析工具结合使用时，它将更全面地介绍维护策略的效率和有效性。如果正确应用，此方法可以识别痛点，突出异常模式或验证设施维护策略的预期结果。特别是MWO中记录的自然语言。当此信息准备和收集过程与其他默认知识或分析工具结合使用时，它将更全面地介绍维护策略的效率和有效性。如果正确应用，此方法可以识别痛点，突出异常模式或验证设施维护策略的预期结果。特别是MWO中记录的自然语言。当此信息准备和收集过程与其他默认知识或分析工具结合使用时，它将更全面地介绍维护策略的效率和有效性。如果正确应用，此方法可以识别痛点，突出异常模式或验证设施维护策略的预期结果。