## 2 Predictive Model
### Hybrid Techniques
In 2018, [PERSON] and [PERSON] [109] present a methodology to classify cross-country faults in series-compensated double circuit transmission lines. This method is based on EMD and three different classifiers: SVM, NB, and PNN. The effectiveness is 95% for SVM, 91.66% for Naive-Bayes, and 96.7% for PNN, where their response times are 0.03s, 0.012s, and 0.016s respectively. [PERSON] et al. [110] apply qualitative trend analysis and NB for the diagnosis of multiple failures in transmission lines. This hybrid diagnosis system can be generalized to deal with other types of faults along the transmission line. Regarding other machines, [PERSON] and [PERSON] [111] use a scheme of classification and detection of faults in an ion implanter, proposing a hybrid classification tree, i.e., they combine a grouping algorithm with CART. They indicate that their methodology is general and can be applied to other machines by simply modifying the warning generation criteria. For the fault detection in components of nuclear power plants components, statistical methods have been used. [PERSON] et al. [112] used a set of auto-associative kernel regression models, a hybrid approach based on correlation analysis, a genetic algorithm, and a sequential probability ratio test to detect faults by taking as a case study a coolant pump of a typical pressurized water reactor. [PERSON] et al. [113] propose an artificial NN combined with optimal zoom search to recognize various degrees of failure in a high-pressure feedwater heater system. The classification of the healthy and defective conditions of a face milling tool is done through the acquisition of sound signals using the discrete WT (DWT) and the J48 algorithm, which is a decision tree technique [114]. On the other hand, to detect and diagnose faults in HVAC systems, [PERSON] et al. [115] combine NNs and clustering analysis. Table 3 lists the works that have presented hybrid techniques for the detection of faults and diagnosis of the abovementioned electric equipment and systems. According to the information shown in Table 3, two different are combined on average to perform the diagnosis, where not only DM techniques are implemented, but other signal processing algorithms are used to extract or highlight features contained into the analyzed signals in order to simplify the fault classification task. As main techniques, the WT and the EMD are found. While the works that use WT exploit its capability for time frequency decomposition in a symmetric way, the works that use EMD exploit its capability to decompose a signal in an adaptive way. In this regard, EMD has been preferred in many works since a-priori information for the analyzed signal is not needed. From this point of view, other recent schemes based on EMD such as down-sampling EMD [116], which is a method that provides specific advantages over EMD, should be explored in the field of fault detection in electric equipment. WT has been also widely used to remove the unwanted noise in an electrical signal. This noise is generated by acquisition systems, sensors, or any electronic device. Regarding DM techniques, FLSs have presented suitable results under noisy conditions in the input signals, since this noise is somehow compared with the uncertainties of the input data, which is an inherent ability of FLSs.