## 3 Data Extraction and Documentation
### Machine Learning Approaches and Tasks  
Machine learning models are data-driven learning methods used to train software to make generalized predictions from historical data. These models have the ability to automatically learn how to solve problems of different natures and with different dimensionality values, from hundreds of input features to just a few [12]. ML-based PdM can be divided into the following main classes: (_i_) _Supervised_ is the task of inferring a function from labelled training data. Knowledge on failures is included in the modelling dataset; (_ii_) _Unsupervised_ is where logistic and/or process information are available, but maintenance related data do not exist, the observations have no \"labels\"; hence, an algorithm is used to identify hidden patterns in the input variables [4]; (_iii_) _Semi-supervised learning_ uses unlabelled examples to help learn the probability distribution over the input space and jointly optimizes the prediction over the labelled and unlabelled examples [13]. This is becoming popular in the recent research. (_iv_) _Reinforcement learning_ can combine the learning and acting phases simultaneously into on-line learning and gives a self-optimizing feature. It uses data to learn about an optimal relationship (for a given objective), either between the input and the decision or the input and the performance index. Sub categories of several data driven models in ML are identified as follows: (_a_) _Ensemble learning_ are meta-algorithms that attempt to decreasing variance. It aims to do this by biasing data while improving predictions using bagging, boosting, and stacking methods [14]; (_b_) _Inductive learning_ involves an algorithm for decision map and rule-based learning; (_c_) _Instance-based learning_ is a class of inductive learning. It generates classification predictions using specific instances on local approximation. Instance-based learning algorithms do not maintain a set of abstractions derived from specific instances [15]. However, many researchers engaged in machine learning have promoted recent developments like _Deep Learning (DL)_, as revolutionary inventions that will have a transformative effect. DL has emerged as a promising computational technique for dynamic system prediction due to its enhanced capability to characterize the system complexity [56], overcoming the shortcomings of those traditional methods [57]. It is a ML technique that learns multiple levels of representations in deep architectures. The traditional neural networks are less effective in handling the unlabelled data that are often encountered in real life. Deep architectures incorporate both unsupervised pre-training and supervised fine-tuning strategies to construct the learning models [58]. DL over the class of artificial intelligence systems allows automatic processing of data. It works towards highly non-linear and complex feature abstraction via a cascade of multiple layers, instead of handcrafting the optimum feature representation of data with domain knowledge. In this perspective, the contraposition that was used between deep learning category vs. the others is suitable. Moreover, the PdM instances can be formulated as follows: (_i_) **Classification** problem, which aims to determine the probability of a part failing before n numbers of parts are processed; (_ii_) **Regression** problem in which it is predicting the time remaining until the next failure is expected to occur, called RUL [6]. Both the categories are under supervised learning class; (_iii_) **Clustering** is generally listed under the unsupervised approach as the results are a collection of data items split between similarity and dissimilarity categories [59]. The following high-level schematics, Figures 3 and 4, were defined, matching different classification schemes presented in the literature [60; 61; 62; 63; 64]. Different and multiple names for the literature were found and grouped as per **RQ5** into data-driven models (blue blocks), learning approaches (grey blocks), and learning modes (light green blocks).