This work aims to provide useful insights into the course of action and the challenges faced by machine manufacturers when dealing with the actual application of Prognostics and Health Management procedures in industrial environments. Taking into account the computing capabilities and connectivity of the hardware available for smart manufacturing, we propose a particular solution that allows meeting one of the essential requirements of intelligent production processes, i.e., autonomous health management. Indeed, efficient and fast algorithms, that does not require a high computational cost and can be appropriately performed on machine controllers, i.e., on edge, are combined with others, which can handle large amounts of data and calculations, executed on remote powerful supervisory platforms, i.e., on the cloud. In detail, new condition monitoring algorithms based on Model-of-Signals techniques are developed and implemented on local controllers to process the raw sensor readings and extract meaningful and compact features, according to System Identification rules and guidelines. These results are then transmitted to remote supervisors, where Particle Filters are exploited to model components degradation and predict their Remaining Useful Life. Practitioners can use this information to optimise production planning and maintenance policies. The proposed architecture allows keeping the communication traffic between edge and cloud in the nowadays affordable “Big data” range, preventing the unmanageable “Huge data” scenario that would follow from the transmission of raw sensor data. Furthermore, the robustness and effectiveness of the proposed method are tested considering a meaningful benchmark, the PRONOSTIA dataset, allowing reproducibility and comparison with other approaches.

这项工作旨在提供有用的见解，以了解在工业环境中实际应用预测和健康管理程序时机器制造商所面临的行动过程和面临的挑战。考虑到智能制造可用的计算能力和硬件的连接性，我们提出了一种特殊的解决方案，该解决方案可以满足智能生产过程的一项基本要求，即自主健康管理。实际上，高效，快速的算法不需要很高的计算成本，并且可以在机器控制器上（即在边缘上）适当地执行，并且可以与其他算法结合使用，这些算法可以处理大量数据和计算，可以在强大的远程监控平台上执行，即在云上。详细，根据系统识别规则和准则，基于信号模型技术的新状态监视算法已开发并在本地控制器上实施，以处理原始传感器读数并提取有意义且紧凑的功能。然后将这些结果传输到远程主管，在此处，可以使用粒子过滤器对组件退化建模并预测其剩余使用寿命。从业者可以使用此信息来优化生产计划和维护策略。所提出的架构允许将边缘与云之间的通信流量保持在当今负担得起的“大数据”范围内，从而避免了原始传感器数据的传输带来的难以管理的“大数据”场景。此外，