## 2 Predictive Model
### Classification-Based Methods
In the latter, a fuzzy clustering-based classification model is transformed into a fuzzy logic inference model, allowing its direct interpretation and inspection; also, improvements in the obtaining of the model are presented to allow the treatment of more complicated scenarios. Table 1 shows a summary for the above reviewed works, where the used techniques and conventional applications, along with the physical variables that have been analyzed by them, are presented. As can be observed, NNs, decision trees, and FLSs are the most commonly used methods for fault detection. Although NNs can be more suitable for fault detection from a generalization viewpoint, decision trees have been preferred in many cases because of the clarity in their interpretation (human friendly) and their low computation burden, which are desirable features in online condition monitoring systems. Also, if the amount of data is limited, a simple decision tree can be used; yet, other aspects of such small dataset have to be taken into account, for instance: redundancy of data, data imbalance, information contained, data type (continuous or discrete), range, time dependency, etc. Regarding the physical variable measured from the in-test equipment, the current signals show to be a powerful and representative source of information for fault detection; although promising results are obtained, the combination of multiple physical variables, e.g., current and vibrations signals, should be explored in order to improve the reliability of new classification schemes and expand the number of fault conditions that can be determined by a single classification algorithm, exploiting the information that each signal can provide, e.g., current signals can provide information to diagnose electrical faults and vibration signals can provide information to diagnose mechanical faults. \begin{table}
\begin{tabular}{c l l l l} \hline \hline \multicolumn{2}{c}{**Classification Methods**} & \multicolumn{2}{c}{**Equipment Under test**} & \multicolumn{2}{c}{
\begin{tabular}{c} **Physical Variable Used as** \\ **Information Source** \\ \end{tabular} } \\ \hline \hline \(\blacksquare\) & NNs and rule sets [26] & & & \\ \(\blacksquare\) & Recursive deterministic perceptron NN [27] & \(\blacksquare\) & Refrigeration systems & \\ \(\blacksquare\) & Feedforward NN [28] & \(\blacksquare\) & Electric equipment (fan, coil, \(\blacksquare\) & Temperature \\ \(\blacksquare\) & Hebbian NN [29] & & pump, and chiller) & \(\blacksquare\) & Energy consumption \\ \(\blacksquare\) & B-spline membership fuzzy NN [30] & \(\blacksquare\) & Stator winding of an & \(\blacksquare\) & Voltage \\ \(\blacksquare\) & ANFIS [52, 51] & induction motor & \(\blacksquare\) & Data from DGA \\ \(\blacksquare\) & Feed forward NN [32, 33, 34] & \(\blacksquare\) & Bearings in induction motors & \(\blacksquare\) & Current \\ \(\blacksquare\) & Multi-layer NN [35] & \(\blacksquare\) & Broken rotor bars & \(\blacksquare\) & Vibrations \\ \(\blacksquare\) & Modular NNs [36] & \(\blacksquare\) & Electric transmission lines & \(\blacksquare\) & Speed \\ \(\blacksquare\) & Adaptive linear NN and feed forward NN [37] & \(\blacksquare\) & Transformer & \\ \(\blacksquare\) & Neural-fuzzy network and statistical analysis [38] & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Classification methods and their applications.