## 2 Predictive Model
### Hybrid Techniques  
It is common to find research where the authors decide to use not only predictive techniques, but also to combine different algorithms that lead them to obtain models or methods that offer better results, including greater precision and efficiency, as well as better handling of data. This section deals with those works whose authors use more than one method, combining classification techniques and regression techniques, as well as other methods that do not belong to the predictive modeling of DM. The use of hybrid techniques, i.e., techniques that combine different methods, is frequently observed in the diagnosis of equipment such as motors, transformers, and electric vehicles mainly, as stated below. In the extensive field of motors, [PERSON] et al. [81] use the hybrid fuzzy min-max (FMM) neural network and classification and regression tree (CART), which is known as FMM-CART, to perform rule extraction and data classification in order to detect and classify faults in different motor conditions. They show the overall accuracy rates of five motor conditions (healthy, broken rotor bars, unbalanced voltages, eccentricity, and stator winding faults). FMM presented the lowest accuracy, 93.62%, while CART and FMM-CART achieved 98.11% and 98.25%, respectively, for multiple motor conditions in a  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{2}{c}{**Regression Methods**} & \multicolumn{1}{c}{**Equipment Under Test**} & \multicolumn{1}{c}{**Type of Fault**} & \multicolumn{1}{c}{ \begin{tabular}{c} **Effectiveness** \\ **Percentage** \\ \end{tabular}  } \\ \hline \multirow{2}{*}{\(\blacksquare\)} & SVM in regression & \(\blacksquare\) & \begin{tabular}{c} Stator winding of \\ an induction motor \\ \end{tabular}  & \(\blacksquare\) & \begin{tabular}{c} Stator winding \\ short circuit faults \\ \end{tabular}  & 95.1, 80.8, and 92.7 \\ \hline \multirow{2}{*}{\(\blacksquare\)} & Autoregressive time & & & \\ series model with & & & & \\ exogenous variables & & & & \\ and SVM [66] & & & & \\ \hline \multirow{2}{*}{\(\blacksquare\)} & Autoregressive time & & & & \\ series model with & & & & & \\ exogenous variables & & & & & \\ and SVM [67] & & & & & \\ \hline \multirow{2}{*}{\(\blacksquare\)} & Autoregressive time & & & & \\ series model with & & & & & \\ exogenous variables & & & & & \\ and SVM [67] & & & & & \\ \hline \multirow{2}{*}{\(\blacksquare\)} & Autoclass SVM [68] & \(\blacksquare\) & \begin{tabular}{c} Steam generator \\ and pressure \\ boundary of a \\ reactor \\ coolant system \\ \end{tabular}  & \(\blacksquare\) & \begin{tabular}{c} Incipient faults \\ \end{tabular}  & 100 \\ \hline \multirow{2}{*}{\(\blacksquare\)} & Logistic regression [71] & \(\blacksquare\) & Busbar & \(\blacksquare\) & \begin{tabular}{c} Internal and \\ external faults \\ \end{tabular}  & 99.69 \\ \hline \multirow{2}{*}{\(\blacksquare\)} & CART [78] & \(\blacksquare\) & \begin{tabular}{c} Synchronous generators \\ \end{tabular}  & \(\blacksquare\) & Inter-turn fault & 95.58–98.15 \\ \hline \multirow{2}{*}{\(\blacksquare\)} & Time-series data & \(\blacksquare\) & \ \begin{tabular}{c} Polyphase \\ induction motors \\ \end{tabular}  & \(\blacksquare\) &
\begin{tabular}{c} Eccentricities and \\ bar/end-ring & 100 \\ \cline{1-1}  & Time & & & \\ mining [79] & & & & \\ \cline{1-1  
time of 0.21s, 0.92s, and 0.96s, respectively. Two years later, in 2014, [PERSON] and [PERSON] [82] implement this hybrid model and conclude that it can produce accurate predictions of motor failures in an online learning environment. In addition, the results of the model are better than those compared with CART, FMM, and multi-layer perceptron. At the noisy test, multi-layer perceptron and FMM presented 78.39% and 94.88% accuracy, whereas FMM-CART and CART achieved stable results with 96.54% and 97.82% accuracy. The multilayer perceptron structure was the most complex with 30 hidden nodes, whereas FMM produced 12 nodes (hyperboxes). FMM-CART and CART created eight and six leaves, respectively.