## 3 Data Extraction and Documentation
### Machine Learning Methods
**ANFIS** is an integration of ANNs and fuzzy logic principles, which makes it possible to utilize the benefits of both in a single framework [73]. Probabilistic neural network involves one-pass learning implemented directly in the learning structure. In the regression schema, dependence between layer decisions is arranged. **BNNs** comprise a probabilistic model and neural network. They try to identify direct dependencies between statistical variables. These are represented in the form of a directed acyclic graph (DAG) and a set of node probability tables (NPTs) [74]. GRNNs are memorybased networks of the probabilistic class in a feed-forward approach. They estimate continuous variables as in standard regression techniques in a conditional expectation [75]. Feed Forward Back propagation Neural Network (**FFBPNN**) over the main category of a probabilistic network is an iterative process operation, in which the input patterns are fed into the input layer of the system. Each hidden layer makes the computation, and it forwards the next active layer in the network until the output layer. The output layer computes the errors based on the observed outcomes and desired level. These errors will then be back-propagated by using the back-propagation formula, from the output layer through the hidden layers and finally reach the input layer. The learning process terminates whenever the total error is within an acceptable level or whenever the limit of the number of iterations has been reached [76]. The Restricted Boltzmann Machine (**RBM**), as elements of the probabilistic class, is an energy-based parallel constraint network that consists of a layer of binary hidden units and a layer of binary visible units without interconnections between both visible/visible and hidden/hidden units [43]. An innovative trend of the last number of years focused on **Deep Neural Networks (DNNs)** (Figure 4, inspired from [77]). Deep learning is the evolution of machine learning. It technically continues to analyze data based on NNS while structuring algorithm in layers. They are applied in systems that require human or artificial \"thought\". Essentially, **DNNs** involve feeding a computer system with data. They can be used to make decisions about other data. Data are fed across neural networks as per ML in SM. These networks (logical constructions) ask multiple binary true/false questions, or extract a numerical value, for every bit of data that pass through them. They classify data according to response. **DNN** can identify failures, if populated by historical data, and the amount is in the complexity of the system. The most commonly used deep architectures are Deep Belief Networks (DBN) and Convolutional Neural Networks (CNN). **DBN** is a feasible method for fault diagnosis and prognosis whenever targeting condition is beyond the historical data. In addition, it also manifests the ability to discover the discriminative information about failures when there is large input dimensionality. DBN architectures of structured **Restricted Boltzmann Machines (RBMs)** are probabilistic generative models. Those learn the joint probability distribution of training data without using data labels. Once the structure of a DBN is determined, the goal for training is to learn the weights and biases between layers. **CNN** consists of multiple layers of hierarchy with some layers of feature representations (feature maps) and classification of type of conventional neural networks. Due to the ability to learn complex and robust representation via a convolutional layer, local patterns in raw data are extracted and subsequently build complex patterns for machine health monitoring through stacking convolutional layers [9]. **Echo State Networks (ESNs)** are a kind of recurrent neural network that arises from the so-called \"reservoir computing approaches\" [78].