###### Abstract  
P prognostic Health Management (PHM) is a predictive maintenance strategy, which is based on Condition Monitoring (CM) data and aims to predict the future states of machinery. The existing literature reports the PHM at two levels: methodological and applicative. From the methodological point of view, there are many publications and standards of a PHM system design. From the applicative point of view, many papers address the improvement of techniques adopted for realizing PHM tasks without covering the whole process. In these cases, most applications rely on a large amount of historical data to train models for diagnostic and prognostic purposes. Industries, very often, are not able to obtain these data. Thus, the most adopted approaches, based on batch and off-line analysis, cannot be adopted. In this paper, we present a novel framework and architecture that support the initial application of PHM from the machinery producers' perspective. The proposed framework is based on an edge-cloud infrastructure that allows performing streaming analysis at the edge to reduce the quantity of the data to store in permanent memory, to know the health status of the machinery at any point in time, and to discover novel and anomalous behaviors. The collection of the data from multiple machines into a cloud server allows training more accurate diagnostic and prognostic models using a higher amount of data, whose results will serve to predict the health status in real-time at the edge. The so-built PHM system would allow industries to monitor and supervise a machinery network placed in different locations and can thus bring several benefits to both machinery producers and users. After a brief literature review of signal processing, feature extraction, diagnostics, and prognostics, including incremental and semi-supervised approaches for anomaly and novelty detection applied to data streams, a case study is presented. It was conducted on data collected from a test rig and shows the potential of the proposed framework in terms of the ability to detect changes in the operating conditions and abrupt faults and storage memory saving. The outcomes of our work, as well as its major novel aspect, is the design of a framework for a PHM system based on specific requirements that directly originate from the industrial field, together with indications on which techniques can be adopted to achieve such goals. P +
Footnote †: journal:products and processes, and the safety of industrial environments while reducing the costs related to maintenance interventions and spare parts supply and management [2]. Due to those advantages, industries moved towards preventive strategies, whose aim is to find the optimal time interval between two consecutive interventions. However, this policy may not allow the exploitation of the whole life of components, which are replaced regardless of their actual health condition [3]. Thanks to technological advances, Condition-Based Maintenance (CBM) developed, which is considered a preventive strategy. It relies on signals collected from machines, such as vibrations, acoustic emissions, current signals, and others, to monitor the health condition of machinery at any point in time and usually arrest it when a given signal reaches a pre-defined failure threshold [4], over which the monitored component is not able to perform its function. Predictive maintenance (PM) can be considered a natural evolution of CBM. PM not only detects the health condition of a system but also predicts its health status in the future and estimates its Remaining Useful Life (RUL). Thus, while CBM uses only the current condition of the component, predictive maintenance also uses Condition Monitoring (CM) data trends to make predictions regarding future states and to ensure enough time to schedule maintenance interventions when most appropriate. The difference between the two maintenance strategies is shown in Figure 1.  
Predictive maintenance is often referred to as Prognostic and Health Management (PHM).