## 3 Data Extraction and Documentation
### Machine Learning Methods  
The following methods were identified for the ML category, based on successful applications: (_A_) **Random forest (RF)**, which is an example of parallel ensemble method. It operates by constructing a set of decision trees. Training and outcomes make use of the mean prediction of the individual trees. The objective in parallel methods is to exploit independence between the base learners, as the error can be reduced dramatically by averaging. Otherwise, in sequential ensemble methods, the base learners are generated sequentially, and the objective is to exploit the dependence between them. The overall performance can be boosted by weighing previously mislabelled examples with higher loads; (_B_) **Gradient Boosting Methods (GBM)** differ from RF in the order and way the trees are built [66]. In GBM new trees (constructed one at time, these are not in forest composition) help to correct errors made by previously trained trees; (_C_) In **Support Vector Machine (SVM)**, as a supervised learning machine approach, the input variables, arranged in separate columns, constitute an n-dimensional space [67]. A hyperplane is selected to separate the inputs in classes. SVMs are usually employed in combination with **Kernel Methods (KM)** to further enhance performance in classification using non linearity. (_D_) In the **Decision Tree Method (DTM)**, a tree (top down structure) is constructed as a predictive structure. The branches of this tree illustrate the outcome of the decisions taken; (_E_) in **Rule Based Learning (RBL)** the network of decisions can be converted in rules. They work on memory and matching schemes to evaluate conflict and to decide how to proceed in learning. (_F_) The **k Nearest Neighbour (k-NN)** classifies objects of instances based on the nearest values with respect to features. It requires computation of distances between samples [68]; (_G_) **Linear (LiR)** tries to estimate real values based on continuous variables, while (_H_) **Logistic Regression (LR)** estimates estimate discrete values based on given set of independent variables. In simple words, it predicts the probability of occurrence of an event by fitting data to a logit function [63]. (_I_) **Gaussian Mixture Model (GMM)** is known as a non-parametric method of density estimation [68]. The unknown parameters are estimated by maximizing the log-likelihood, using the expectation-maximization algorithm [69]. Therefore, an instance is assigned to cluster with a probability amount [70]. Under the clustering approach over the main unsupervised class there are (_J_I) **Hierarchical Methods (HM)** (divided in agglomerative and divisive). They work on a bottom-up or a top-down strategy (depending on the type) starting from one object for a cluster or from all objectives in one cluster. It iteratively agglomerates or subdivides clusters until termination conditions are satisfied. HMs are generally contraposed to (_J_2) **Fuzzy Unordered Rule Induction Algorithm (FURIA)** that produces better decision boundaries between different classes over fuzzy logic rules [18]. (_L_) **Hidden Semi-Markov Decision Process model (SMDP)** is utilized to represent the deteriorating process of machines, due to the generally distributed sojourn time and the unobservable underlying stochastic process on deteriorating quality states [71]. Specific ML applications make use of neural networks. **Artificial neural network (ANN)** tries to adapt to the information flow of the biological learning scheme. Different types of applications of ANNs include Back Propagation Neural Network (BPNN), Adaptive Neuro-Fuzzy Inference System (ANFIS), Bayesian Neural Network (BNN), Probabilistic Neural networks (PNNs), and Generalized Regression Neural Networks (GRNNs) as a feed-forward category [30]. The **BPNN** algorithm adjusts the connection weights based on the gradient descent method where the squared error is minimized in the direction of the greatest improvement [72]. BPNN suffers a large number of iterations to converge to the desired solution.