## 2 Predictive Model
### Classification-Based Methods  
In a condition monitoring context, a classification model can be constructed from a given system and used to provide warnings and predict certain failures in early stages. In this regard, researchers around the world have proposed and used different classification methods in machine learning, pattern recognition, and statistics to perform faults diagnosis. Figure 2: **(a)** Schematic diagram for tasks and functionalities of data mining (DM) and (b) prediction model. Recent research on DM has been focused on developing classification techniques capable of handling datasets with different features, e.g., imbalance of proportionally, and large amounts of data. In the latter, this capacity is strongly required because, on the one hand, the availability of data is growing and, on the other hand, their performance can be compromised if limited datasets are analyzed. In fact, the amount of available data during the training of a neural network (NN) plays an important issue in its performance. For instance, [PERSON] et al. [26] contrasted three different techniques: Neural networks trained by using a hybrid of evolutionary search and backpropagation, neural networks trained by straightforward backpropagation, and simple predictive rulesets trained by evolutionary algorithms. Results indicate that evolved NNs outperform backpropagation trained NNs. However, the results are slightly unsatisfactory from a business viewpoint, obtaining a maximum accuracy of 77.9%, which can be somehow expected due to the small amount of training data, highlighting the need of additional data to establish a better reference during the pattern recognition task. Fortunately, there are many works in which the authors also use NNs as the basis of their investigations and promising results are obtained. In an energy consumption context, [PERSON] et al. [27] diagnose different electrical equipment of an office building, including fans, pumps, cooling equipment, and chillers. They use a recursive deterministic perceptron NN to distinguish between normal and defective datasets, where an effectiveness percentage higher than 97% is obtained. Similarly, the use of NNs for fault detection on induction motors are presented [28; 29; 30; 31; 32; 33; 34; 35]. [PERSON] et al. [28] presented an on-line diagnostic scheme to alert the engine protection system of an incipient failure. This scheme consists of a feed-forward NN with a self-organized feature map to display the operating conditions of the in-test machine. An interesting feature offered by the results is that the method is not sensitive to unbalanced supply voltages or asymmetries in the machine. [PERSON] et al. [29] use the alpha-beta stator currents of a three-phase induction motor as input variables to diagnose stator faults. In their proposal, an unsupervised Hebbian-based NN is used to extract the main components of the stator current data. Other proposals combine NNs with fuzzy logic systems (FLSs) to detect inter-turn faults [30; 31]. In particular, [PERSON] et al. [31] developed an ANFIS (Adaptive Neural Fuzzy Inference System) for the detection of stator inter-turn insulation and bearing wear faults, where five input parameters, i.e., current, bearing temperature, winding temperature, speed, and the noise of the machine are used to construct the model. For the inter-turn insulation fault, they obtain an effectiveness of 94.03% using two inputs and 96.67% using five inputs. For the bearing wear fault, the accuracy rate with two inputs is 90.5%, and 98.7% with five inputs. These results demonstrate the importance of an information-rich dataset. In [32; 33; 34], several NNs are implemented in field programmable gate arrays (FPGAs) to diagnose different faults in induction motors. The diagnosis of broken rotor bars is presented by [PERSON] et al. [35], where the multi-layer perceptron NN used is able to detect the faults in the rotor with a classification effectiveness of 98.80%.