###### Abstract
Second, thanks to the advances in technologies, corrective interventions are often conducted through remote maintenance, including the time needed for recognizing the problem before guiding the operator to the machine maintenance. Machine producers are interested in the fault behaviors of machines operating in different environments. Indeed, they can use collected information to improve the machine design phase (i.e., proactive maintenance). Thus, a properly designed PHM methodology could (1) avoid inspections and support the optimal spare parts management, thanks to the continuous monitoring and prognostics; (2) speed up the process of remote maintenance, thanks to the streaming anomaly/fault detection; and (3) allow structured data collection for the continuous improvement of existing diagnostic and prognostic models. On the other hand, clients could (1) always know the health condition of their machines; (2) recognize anomalous behaviors as they occur and, if possible, intervene without the involvement of the machine producer to reduce the time to repair; (3) benefit from a most robust analysis conducted by the machine producer. Regarding the second point, there are two main issues. First, machine producers constantly design new types of machinery, for which historical data for model training are not available; moreover, clients are not always willing to share their data, as they feel their market share is threatened by those producers who could decide to enter the same market as their own business. In particular, they tend to hide confidential data, such as the number of pieces produced every day, machinery settings, and the production process or materials, which often represent the labels (target value) needed for training the  
Figure 1: (**a**) Condition-Based Maintenance (Diagnostic) (based on [3]); (**b**) Predictive Maintenance (Prognostic) (based on [12]). diagnostic and prognostic models. As a result, the data collected from clients are in most cases are unlabeled, meaning that they do not record information about environmental conditions, operating conditions, fault modes (if any), maintenance interventions, or regulations carried out on the machinery. Second, the amount of data generated by machinery is difficult to manage. The weight of high-frequency data is in the order of gigabytes per hour. It is impossible to acquire and store raw signals from all machinery in a continuous way. Therefore, data are usually collected intermittently, often during short tests conducted by machine producers in very controlled environments. Thus, data under fault conditions are trickier to obtain than data under healthy conditions, resulting in very unbalanced datasets; also, the dataset may not include data for different operating conditions, resulting in the impossibility to train accurate diagnostic and prognostic models. Given these issues, the main objective for machine producers is to find a PHM architecture that (1) can start from scratch, with very little prior knowledge about machinery behavior; (2) supports the exchange of data between producers and clients, paying attention to privacy issues; (3) helps to collect the data in a structured way, with labels associated with each observation, i.e., low-frequency data associated with high-frequency data; (4) provides an early indication of anomalous and novel behaviors; (5) can learn from experience in different contexts; (6) performs different functions depending on the specific component and the whole machinery. In this paper, a PHM methodology for the industrial field is presented. The methodology design consists of the definition of a framework and functional and logical architectures. First, the stakeholders and their requirements are identified in the machine producers; then, an edge-cloud-based framework and a novel architecture are introduced to adapt the existing architectures to the stakeholders' expectations and the framework.