## 2 Literature Review
### Prognostics and Health Management (PHM)  
Within the scientific literature, most researchers refer to prognostics as \"an engineering discipline focused on predicting the time at which a system or a component will no longer perform its intended function\" [3; 7]. As far as prognostic models are concerned, [PERSON] et al. [6] proposed a complete review of all the known methodologies of prognostics. They identified four different categories of prognostic approach: physical models, knowledge-based models, data-driven models, and hybrid models. The quality of the prognostic predictions, including steady-state, transient performance, unanticipated conditions, loads, and operational regimes, was the main advantage of a model-based approach [6], while its limitations were high implementation costs, the difficulty of building a good physical model, and the impossibility to adopt a specific model for other systems. The knowledge-based approach relies upon stochastic probabilistic reliability models of equipment/components, or upon experts' judgments. In their review, [PERSON] et al. [6] suggested two main methodologies to this aim; namely, expert system and fuzzy logic. Data-driven approaches are appropriate when the understanding of the system's functioning principles is not comprehensive or when the system is complex and the development of an accurate physical model is too expensive [31; 32]. [PERSON] et al. in [33] explained that during the planning and testing phases, the data-driven model faces the challenges posed by the complexity of real-world systems. In general, these include dealing with incomplete knowledge of future load conditions, inaccurate estimation of the current state of health, poor evolution models, sensor noise, and varying operating conditions [33]. The data-driven methodology is based on statistical and learning techniques, in which the collected data under nominal and degraded conditions are combined and, thanks to a generic machine-learning algorithm, it is possible to generate appropriate prognostic models. In this sense, two data-driven methodologies are identifiable: statistical and artificial approaches. In the literature, there are many application examples of the latter approach. The most used and experimented is the artificial neural network (ANN) and its variants (e.g., feed-forward neural network (FFNN), polynomial neural network (PNN), dynamic wavelet neural network (DWNN), self-organizing feature map (SOM), and multilayer perception neural network (MPNN)). An ANN is a generic algorithm that tries to mimic the human brain structure. It consists of simple processing elements (comparable to biological neurons, that comprises a node and weight) connected to a complex layer structure, which enables the model to approximate a complex nonlinear function with multi-inputs and a single or multi-output [34]. The use of intelligent approaches, notably ANN-based algorithms, are attractive in the areas of prognostics for their potential to reduce the complexity of a problem and to support the ability of time series prediction. The performance of existing data-driven methods leveraging deep learning models, such as the recurrent neural network (RNN), heavily depends on the quality and size of the available training data used to optimise their model parameters. However, gathering large amounts of run-to-failure training data can be very costly and time-consuming [32]. In the literature, generic ANNs are often used for prognostic purposes. [PERSON] et al. in [35], for example, developed a model based on recurrent neural networks to identify possible asset malfunctions. In particular, the proposed framework analyses a dataset of historical sensor data in order to perform predictive maintenance on a set of engines. [PERSON] and [PERSON] [36] theorised an intelligent diagnostic in which a self-organised neural network was deployed for performing a multivariable trending of faults, applied to the RUL estimation of bearing systems.