## 2 Literature Review  
In a literature review about Fault Detection and Isolation, it was observed that most of the current solutions were very specific for a closed system or even a particular situation. It means that it is hard to reuse the solution. Even for a similar system, with a few different parameters, it would be necessary to implement adaptations. However, a minimal generalization becomes a difficult task. The aeronautical industry was not completely unaware of this and had some initiatives to provide frameworks and recommendations that any customer, manufacturer, or stakeholder could adopt if desired. Even so, different protocols and standards were adopted depending mostly on the region (America or Europe) and segment (commercial or military). Nevertheless, two relevant frameworks provide significant levels of protocols in different layers to standardize implementations, products, communications, interface, processes, projects, and other subjects of interest. The first and more comprehensive one is the Integrated Vehicle Health Management (IVHM). The National Aeronautics and Space Administration (NASA) was the first one to adopt and implement this concept. Nowadays, it has strong support from the Society of Automotive Engineers (SAE), and it was taken in different stages in the aviation industry [5; 6; 7]. The second initiative comes from the Machinery Information Management Open Systems Alliance (MIMOSA). Its proposed standards, namely Open System Architecture for Condition-Based Maintenance (OSA-CBM) and Open System Architecture for Enterprise Application Integration (OSA-EAI), have established protocols to support asset management [8]. The OSA-CBM framework is based on six layers depicted in Figure 1. From layers 1 to 3, the data flow is considering each component as an independent system, and its primary concern is to best estimate the actual state for the given parts. From layers 4 to 6, a gradual increment on data fusion takes place to raise awareness of the whole monitored system situation. In the end, it is up to the responsible manager to evaluate and decide the actions or inaction that will be applied to the respective system, restarting the cycle again. It is essential to highlight that diagnosis takes place mostly at layer 4, starting in layer 3, while the system is being operated in the OSA-CBM framework. In terms of asset management, it would be necessary to establish the history of the system operation and also previous knowledge in the system's reliability. Such capability is acquired from the use of the OSA-EAI framework and IVHM state-of-the-art implementations. All those initiatives become crucial in an age of interoperable systems, and still more challenging because an Air Force, or an air transportation company, manages many different aircraft fleets, from varied manufacturers, for several environments and purposes. On the other hand, systems that apply the same protocol, standard architecture, and user interfaces in the same situation are simpler to support, since they can share a set of standard solutions and even components in some cases. The term anomaly detection refers to the capability to identify anomalies based on the acquired data through pattern recognition that does not conform to the expected system behavior [9]. From all circumstances exposed so far in the anomaly detection and diagnosis fields, it is pronounced the number of anomaly detection algorithms and methodologies aimed at ad-hoc solutions. The best way to analyze this argument is by dividing those studies into three main approaches:  
* Data-Driven techniques: It is independent of a physical model, and it uses the historical records and online data to solve anomaly detection problems. The system or component is irrelevant from this perspective, and generalization can only be considered if data-sets are the same in all aspects;
* Model-Based techniques: It has sets of equations that explain mostly the behavior of a physical system. The data are analyzed through these equations.