## 6 Conclusions  
New aircraft generate a large amount of data that allow a series of analyses for both safety and maintenance interests to \"more accurately\" estimate the condition of various subsystems. In this context, the algorithms identified in this work seek to identify and isolate system states (system degradation, faults, or operational deviation), which can be done through FDI techniques. It does not matter whether the data affect maintenance or safety but whether the necessary parameters are reached to guarantee the operation within tolerable limits of these subsystems.  
Several other works addressed solutions for FDI, combining different machine learning techniques or combining one of them with an analytical model, obtaining more accurate results. However, in complex systems cases, the solutions are precise and are generally dedicated to a particular subsystem that responds better to the studied technique. Even in works that require little adaptation,  
Figure 7: RUL prediction example.  
it becomes unfeasible to apply the chosen techniques for that solution, because it is very common for hybrid approaches to be incredibly dependent on its model.  
Many of these works then are not used by others due to high degree of forced coupling between the different used algorithms. Despite the primary need to achieve the desired failure detection and isolation rates, it is necessary, whenever possible, to maintain the least degree of coupling possible between algorithms. The reason for this is because it becomes easy to replace the more specific technique of one subsystem for another one without affecting possible dependencies on other parts of the algorithm.  
Under the same circumstances, the proposed method demonstrated its ability to combine different techniques in order to achieve equal or better accuracy than its isolated application. It is a matter of fact that since the algorithm can decide which combination or even just one of its techniques yields better results. It is not about a new FDI Method, but a methodology to do hybridization of some provided different techniques analyzed over their isolated and combined ensemble sets' performance, looking for improved general accuracy. Furthermore, it can combine model-based techniques gathering their decisions on the second instance layer.  
Conceptualization, D.W.D.I.; Formal analysis, D.W.D.I.; Investigation, D.W.D.I.; Methodology, G.C.R.; Supervision, H.C.M.; writing--original draft, H.C.M.; Writing--review & editing, G.C.R. and A.M. All authors have read and agreed to the published version of the manuscript.  
This study was financed in part by the Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior--Brazil (CAPES)--Finance Code 001.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} ACC & Accuracy \\ ADAM & Adaptive Moment Estimation \\ ADRC & Active Disturbance Rejection Control \\ AM & Analytical Model \\ ANFIS & Adaptive Neuro-Fuzzy Inference System \\ ANN & Artificial Neural Networks \\ CBFI & Consistency-Based Fault Isolation \\ CBM & Condition-Based Maintenance \\ DT & Decision-Tree \\ ESO & Extended State Observer \\ ET & Ensemble Triads \\ F1 & F1-score \\ FDI & Fault-Detection and Isolation \\ FIS & Fuzzy Inference System \\ FN & False-Negative \\ FP & False-Positive \\ GADHA & Generic Anomaly Detection Hybridization Algorithm \\ GD & Gradient Descend \\ IMU & Inertial Measurement Unit \\ IVHM & Integrated Vehicle Health Management \\ JVM & Java Virtual Machine \\ LDA & Linear Discriminant Analysis \\ LNG & Landing Gear \\ LPV & Linear-Parameter-Varying \\ MIMOSA & Machinery Information Management Open System Alliance \\ MF & Membership Function \\ MS & Minimum Square \\ NASA & National Aeronautics and Space Administration \\ \end{tabular}  
Figure A2: Subset triads results.  
Figure A6: Degradation Isolation.