Abstract The fourth industrial revolution is derived from advances in digitization and prognostic and health management (PHM) disciplines to make plants smarter and more efficient. However, an adapted approach for data-driven PHM process implementation in small and medium-sized enterprises (SMEs) has not been yet discussed. This research gap is due to the specificities of SMEs and the lack of documentation. In this paper, we examine existing standards for implementing PHM in the industrial field and discuss the limitations within SMEs. Based on that, a novel strategy to implement a data-driven PHM approach in SMEs is proposed. Accordingly, the data management process and the impact of data quality are reviewed to address some critical data problems in SMEs (e.g., data volume and data accuracy). A first set of simulations was carried out to study the impact of the data volume and percentage of missing data on classification problems in PHM. A general model of the evolution of the results accuracy in function of data volume and missing data is then generated, and an economic data volume notion is proposed for data infrastructure resizing. The proposed strategy and the developed models are then applied to the Scoder enterprise, which is a French SME. The feedback on the first results of this application is reported and discussed.

中文翻译：

---

面向中小企业的 PHM 的工业数据管理战略

摘要 第四次工业革命源于数字化和预测与健康管理 (PHM) 学科的进步，使植物更智能、更高效。然而，尚未讨论在中小型企业 (SME) 中实施数据驱动 PHM 流程的适应性方法。这种研究空白是由于中小企业的特殊性和缺乏文件造成的。在本文中，我们研究了在工业领域实施 PHM 的现有标准，并讨论了中小企业内部的局限性。在此基础上，提出了一种在中小企业中实施数据驱动的 PHM 方法的新策略。因此，审查数据管理过程和数据质量的影响，以解决中小企业的一些关键数据问题（例如，数据量和数据准确性）。进行第一组模拟以研究数据量和缺失数据百分比对 PHM 分类问题的影响。然后生成结果精度随数据量和缺失数据变化的一般模型，并提出经济数据量概念来调整数据基础设施的大小。然后将提议的策略和开发的模型应用于 Scoder 企业，这是一家法国 SME。报告和讨论了有关此应用程序的第一个结果的反馈。然后将提议的策略和开发的模型应用于 Scoder 企业，这是一家法国 SME。报告和讨论了有关此应用程序的第一个结果的反馈。然后将提议的策略和开发的模型应用于 Scoder 企业，这是一家法国 SME。报告和讨论了有关此应用程序的第一个结果的反馈。