Small and medium manufacturing enterprises (SMEs) often lack skills and resources required to perform in-house PHM analytics. While cloud-based services provide SMEs the option to outsource PHM analytics in the cloud, a critical limiting factor to such arrangement is the data owner’s unwillingness to share data due to data privacy concerns. In this paper, we showcase how homomorphic encryption, a cryptographic technique that allows direct computation on encrypted data, can enable a secure PHM outsourcing with high precision for SMEs. We first outline a two-party collaborative framework for a secure outsourcing of PHM analytics for SMEs. Next, we introduce a frequency-based peak detection algorithm (H-FFT-C) that generates a machine health diagnosis and prescription report, while keeping the machine data private. We demonstrate the secure PHM outsourcing scenario on a lab-scale fiber extrusion device. Our demonstration is comprised of key functionalities found in many PHM applications. Finally, the extensibility and limitation of the approach used in this study is summarized.

中小型制造企业 (SME) 通常缺乏执行内部 PHM 分析所需的技能和资源。虽然基于云的服务为中小企业提供了在云中外包 PHM 分析的选项，但这种安排的一个关键限制因素是数据所有者由于数据隐私问题而不愿共享数据。在本文中，我们展示了同态加密（一种允许对加密数据进行直接计算的加密技术）如何为中小企业实现高精度的安全 PHM 外包。我们首先概述了为中小企业安全外包 PHM 分析的两方协作框架。接下来，我们介绍一种基于频率的峰值检测算法 (H-FFT-C)，该算法生成机器健康诊断和处方报告，同时保持机器数据的私密性。我们在实验室规模的纤维挤出设备上演示了安全的 PHM 外包方案。我们的演示包含许多 PHM 应用程序中的关键功能。最后，总结了本研究中使用的方法的可扩展性和局限性。