## 2 Literature Review
Such models need to be as representative as possible to the system, but this rarely happens due to noise, lack of complete knowledge of phenomenons involved, disturbances, and even impossibility to perform the necessary measurements;
* Hybrid techniques: It is the combination of the other two techniques with their benefits and limitations. However, there are different ways to perform hybridization. So by abstracting the  
Figure 1: Open System Architecture for Condition-Based Maintenance (OSA-CBM) framework. data-set and model, it is possible to extract the interaction architecture between both algorithms and check the feasibility and compatibility with the proposed system. Hybrid approaches are taking place more often once data-driven techniques can explain part of that uncontrolled behavior presented in model-based approaches. The assumption that such methods could never reach a perfect representation in the real world is very plausible, and it leads to the assumption that more optimization is always possible. Although, it can be unnecessary if requirements do not demand it. The concept of the wisdom of the crowd from statistics is interpreted in machine learning as Ensemble Learning. Considering a choice for one of the available algorithms in the literature, instead of training and applying only one instance of it, a considerable number of inducers are created and trained to obtain various countable results from different approaches. The work [10], in their survey about ensemble learning, points out the premise that the combination of multiple models compensates the error of a single inducer. From this perspective, the present work was developed looking to generate a framework to support fault classification with a hybrid approach. The model-based capability to establish the basic behavior of the system will be combined with ensemble techniques that can mix a set of different data-driven methods, depending on the system characteristics. Varied methods to organize them can be found in the literature, and this is one of the most relevant aspects observed for this work. It addresses the ability of ensemble learning in generalizing many different fashion distinct algorithms or even different versions of one of them. Besides the fact of their straightforward application, they provided the necessary hint to tackle the research problem in this study. Ensemble learning is a broad term in machine learning used for the combination of multiples inducers to make a decision. Thereby, the primary purpose of this technique is to try to use several inducers to compensate for the errors of one or a few of them [10]. There are three major approaches for ensemble methods: boosting, bagging, and stacking. All the others can be considered derivations, adaptations, or a mix of them. Table 1 resumes these meta-algorithms used in [11; 12; 13]. Ensemble methods have been used to handle FDI problems in several publications. The work [13] did research comparing different machine learning classifiers, and the best results were achieved using ensemble techniques to detect submersible electrical pump failures. The work [12] developed an ensemble of dynamic neural networks for FDI from gas turbine engines. His approach claims to be the first one to use ensemble methods through system identification to generate residuals instead of evaluating them like previous works. In the time of paper writing, ensembles can be considered as the state-of-the-art technique for a broad set of machine learning and real-life challenges [10]. Adaptative Neuro-Fuzzy Inference System (ANFIS) aggregates the Artificial Neural Networks (ANN) concept of robustness, adaptation, non-linear mapping, and at the same time as it is based on the Takagi-Sugeno-Kang inference model [14]. The Fuzzy Inference System (FIS), or fuzzy logic, can handle uncertainties and imprecise pieces of information. While FIS requires excellent knowledge to have an appropriate set of rules to achieve better accuracy, ANFIS does not demand an expert since