## 2 Predictive Model
### Hybrid Techniques
The computational time of FMM was only 0.13s. Multilayer perceptron consumed the longest time (2.08s), whereas FMM-CART and CART used almost 1s. The CART method combined with adaptive neuro-fuzzy inference system is presented by [PERSON] et al. [83]. They use current and vibration signals from the induction motor for fault diagnosis; additionally, the hybrid of back-propagation and least-squares algorithm is used to adjust the parameters of membership functions. The total classification accuracy was 91.11% and 76.67% for vibration and current signals, respectively. Other works such as the one presented by [PERSON] et al. [84] involve the identification of stator failures in induction motors using the multinomial logistic regression analysis and the Wavelet Transform (WT). [PERSON] et al. [85] use a multiple linear regression modeling technique along with the analysis of variance and the genetic algorithm optimization to obtain classification models to diagnose three-phase induction motors under normal and short-circuit conditions. The method presents percentages of hits greater than 95% in the diagnosis of the normal and incipient short-circuit fault condition, even at different motor load levels. In addition to the low cost and simplicity, this method does not require physical access to the machine because the current and voltage can be measured from the motor control board. Thus, the probability of the occurrence of human accident is reduced significantly. Unlike several other reported methods for fault diagnosis, the proposed approach requires few data and only uses simulation data to construct the expert system. [PERSON] et al. [86] propose an algorithm based on two parts: In the first one, the optimal size of the structure of the Probabilistic Neural Network (PNN) is determined, using an orthogonal least-squares regression algorithm. In the second part, the fusion of a Bayesian classifier is recommended as an effective solution to diagnose incipient interturn fault in the machine. To track the health status of a degraded system and predict the remaining service life of a turbofan engine, [PERSON] et al. [87] propose a method that combines the echo state kernel recursive least-squares algorithm and a Bayesian technique, which demonstrates an excellent performance with respect to long-term prediction. To obtain an effective diagnosis of faults in automotive systems, intelligent monitoring schemes of the vehicle's condition are needed. In this regard, [PERSON] et al. [88] develop three new approaches for fusion of classifiers in order to reduce the error rate. These approaches are: Joint optimization of the fusion center and individual classifiers, class-specific Bayesian fusion, and dynamic fusion, demonstrating that the proposed techniques surpass the individual classifiers such as PNN, k-Nearest Neighbor (kNN), or principal components analysis. A fault detection scheme for applications in the automotive industry is presented by [PERSON] and [PERSON] [89]. They achieve the detection of faults by using kernel regression techniques and a NN. The resulting network uses significantly less basis functions than a radial basis function network with the same accuracy. [PERSON] et al. [90] present a model-based approach to predict the remaining driving range by combining a particle filtering and Markov chains by implementing detailed models of the battery, electric motor, and vehicle dynamics. [PERSON] and [PERSON] [91] and [PERSON] and [PERSON] [92] study different methodologies for the prediction of electric vehicle energy consumption. In particular, [PERSON] and [PERSON] compare three approaches that include (1) approaches based on driver/vehicle/environment dependent factors using speed profile matching and driving habit matching, (2) approach of comparison with the average using personalized adjustment, and (3) a collaborative filtering approach that uses matrix factorization; whereas [PERSON] and [PERSON] use the least-mean square algorithm for the prediction of the mean energy consumption.