## 3 The Proposed PHM System
### The Framework  
In this section, a framework to address the stakeholders' requirements is proposed. The framework consists of three layers: the edge storage and analytics layer, the cloud storage and analytics layer, and the knowledge integration layer, as shown in Figure 3.  
The edge storage and analytics layer is a network of unlinked edge nodes installed close to the machines. Here, data processing (i.e., feature extraction and selection, HI extraction) and real-time analytics are performed (i.e., operating condition recognition, anomaly detection, RUL prediction), allowing the continuous monitoring of machines. In particular, the edge layer provides machine users with information regarding the health status of the equipment at any point in time. From the machine producers' point of view, this level also allows the collection of low-frequency data and event data, addressing the \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Stakeholders’ Requirements** & **Framework** & **Functional Architecture** & **Logical Architecture** \\ \hline Remote monitoring of several machines & Distributed edge devices connected to a centralized cloud sever & Feature extraction, fault detection, diagnosis and prognosis (edge DP, FDA, PA) & Transmission into the cloud of features and HIs at defined instants; event-data into in real-time \\ \hline Data collection of machinery settings & Streaming analysis of distributed edge devices distributed edge devices & Novelty detection (OCR) of system-level features & Transmission into the cloud of system-level features with the associated label \\ \hline Collection of structured and labeled data & Real-time computing at the edge and batch analysis in the cloud & Fault diagnosis and prognosis (edge and FDA and PA) of component-level features & Simultaneous streaming inference by trained models, and anomaly and novelty detection of component-level features \\ \hline System-level and component-level PHM & Model training in the cloud & System-level and component-level feature extraction (cloud DP) & Streaming extraction of features selected during the training phase \\ \hline Integration of databases & Cloud server accessible by local databases & Fault diagnosis and RUL prediction (cloud FDA and edge PA) & Integration of information collected by the PHM system into local databases \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Framework and architecture connections with stakeholders’ requirements. Figure 3: The proposed framework. first and third requirements. Indeed, the reduced amount of data obtained from streaming data processing allow producers to monitor several machines. When they need to know the health condition of a given component, they can access the information extracted at the edge node during the machine's functioning (i.e., features and HI values). Here, the current health condition and the implemented setting, the information related to the occurrence of anomalous behaviors, setting changes, and activities carried out by the operator on the machine are temporarily stored. Finally, sensitive information (such as the parameters that determine the machinery setting) is hidden from the producer since only processed data are accessible. At this level, there is no information aggregation. Results of data processing and real-time analytics are shown for each component of the machine so that the machine user can easily recognize where the anomaly is and intervene when necessary to restore the correct functioning. On the other side, the machine producer can select the component of interest. In addition, as the setting is also recorded, the data related to each component under different operating conditions are separated, i.e., there is a label representing the implemented setting. This way, the fourth requirement is fulfilled. The cloud storage and analytics layer represents the link between users and producers. In this layer, relevant information extracted at each edge node through streaming analysis is stored permanently.