## 2 Predictive Model
### Classification-Based Methods
[PERSON] and [PERSON] [45] also use the fault tree analysis and Bayesian networks for fault detection of a system for oil pressure warning instructions in an aircraft engine, where a diagnostic decision tree to guide maintenance personnel to make more efficient decisions when attempting to repair the system is obtained. An advanced Bayesian non-linear state estimation technique called Unscented Kalman Filtering to detect faults in HVAC (heating, ventilation, and air conditioning) components is presented by [PERSON] et al. [46]. This algorithm can detect common faults in a chiller plant and functional failures caused by problems in the compressor and occlusions in the valves with a computational performance of 0.25s using Intel Xeon (R) 2.67 GHz-19 Cores and 0.52s using Intel Core i7 2.8 GHz-1 Core. Another tree-based method is the tree-structured fault dependence kernel developed by [PERSON] et al. [47]. It implements a structured labeling to include dependency information and describe severity levels in a high-margin learning framework for fault detection of building cooling systems. It is important to highlight that the testing accuracy increases or decreases accordingly with the change of training samples. For instance, in [47], the testing accuracy of the proposed strategy boosted from 69.64% (six training samples) to 99.12% (180 training samples). That is, accumulating more training data is beneficial for the fault detection and diagnosis. Other classification method that has been widely used is the FLS. In general, it uses knowledge-based reasoning to construct logical rules and, thus, diagnose faults. In this type of algorithms, the designer knowledge about the in-test equipment, e.g., operating conditions, nominal parameters, overall performance, etc., plays a fundamental role. In [48], an FLS is designed to diagnose stator winding faults in induction motors. Similar results are obtained under noisy and noiseless conditions. Therefore, FLS is a good option because there is no general and accurate analytical model that describes completely the induction motor under fault conditions, leaving the open doors to uncertainties or noisy conditions. [PERSON] et al. [49] present two FLSs to detect broken rotor bars (BRB) in both regimes of operating conditions, i.e., transient and stationary. The combination of fractal dimension analysis and FL system demonstrated to be highly effective on identifying half-BRB, one BRB, and two BRB, as well as healthy condition, since an effectiveness of 95% and 100% for start-up transient and steady state is obtained. For transformers, [PERSON] et al. [50] present the diagnosis of several transformer faults using dissolved gas in oil analysis (DGA) and an FLS for its interpretation. An overview of different FLSs for DGA is presented in [51], where it is indicated that there is not a single technique that can enable the detection of the full range of faults, therefore the combination of different methods has to be explored as a promising solution. Although promising results have been obtained using FLSs, a relatively high superiority of an adaptive neuro fuzzy inference system for DGA is presented in [52], obtaining an accuracy of 98% for all the 100 fault cases under study, while FL obtained 95%. Regarding other electric systems an equipment, the fault diagnosis of the power system using fuzzy logic is presented in [53]. An online monitoring system of voltage variations in electric systems is presented in [54], where an FLS is used to diagnose and classify instantaneously, i.e., sample to sample, the severity of the electric variation. Their proposal is a suitable tool for analyzing stored data; furthermore, it provides phase information unlike the conventional root mean square technique; moreover, it gives results sample to sample, which is better for nonstationary signals. [PERSON] et al. [55] diagnose a fan coil electric and [PERSON] et al. [56] classify the faults of a steam generator of a pressurized water reactor.