###### Abstract
According to this process, raw signals are first transformed into synthetic and meaningful information (signal processing or feature extraction); then, the relationships between the collected data and the actual condition are identified (diagnostics); finally, the end of life of the component/machinery is predicted (prognostics) [5]. In the literature, the problem of the implementation of PHM is addressed at two levels [6]. The first level deals with the methodology design for defining the characteristic elements of a PHM system for a specific sector, e.g., aviation, industrial, wind energy, and others. The second level concerns the architecture design for designing the best data analytics techniques for specific components and systems, e.g., rolling bearings, gearboxes, industrial robots, or Hard Disk Drive (HDD). In other words, the first level addresses the problem of how a complete PHM system can be designed for a specific context, while the second level addresses the issue of how to transform raw signals into information and knowledge for supporting the maintenance decision process.? At the methodology level, a relevant contribution is provided by [PERSON], [PERSON], and [PERSON] [7; 8; 9], who introduced an architecture definition process for PHM applications based on an 'RFLP' method, which consists of the definition of Requirements, Functional, Logical, and Physical architectures, together with the framework, i.e., a layered structure of a system for a set of functions in a conceptual view [8]. Although these papers are mainly focused on the aviation sector, the 'RFLP' method for PHM applications can also be applied to the industrial sector. However, to the best of our knowledge, that method has never been investigated for the industrial sector. Many existing studies focus on PHM approach design for specific complex systems, such as industrial robots operating in several industrial sectors [10] or machine-tools [11], from the user point of view. Thus, existing studies aim to find the most suitable feature extraction, diagnostic, and prognostic models for a specific use case. However, very little attention is given to the general methodology design for machine monitoring and health assessment from the double perspective, including that of the machine producer. At the architecture level, a relevant number of papers on the application of Machine Learning (ML) techniques for PHM have been published [12]. In particular, critical components, such as bearings, gearboxes, wind turbines, lithium-ion batteries [13; 14], and HDD [15], are tested under different health conditions in order to build accurate models for fault classification and prognostic. The existing approaches assume to have a high amount of labeled data for the training phase [16] and knowledge of all the operating conditions since they strongly affect the degradation trends or the machine's general behavior [17]. Thanks to the collaboration with leading producers of automatic and grinding machines located in the well-known district of the Emilia Romagna region, it was possible to understand the benefits of a PHM methodology and the issues in the implementation of the existing PHM architectures. Regarding the first point, machine producers could benefit from a PHM methodology because the post-selling service represents the differentiation key from their competitors for many machine producers. Indeed, machine producers usually adopt traditional preventive maintenance models to schedule maintenance interventions for their clients. This strategy often implies inspections that result in either an intervention or its rescheduling, depending on the actual detected condition. In both cases, maintenance inspections represent a waste of resources for machine producers and production losses for clients. In addition, the spare parts management results are compromised.