## 5 Discussion  
As expected, the final instance result provided a better classification capability when compared with the traditional Model-Based approach (Analytical Model) and Data-Driven approach using Ensemble Triads Optimization. As can be observed in Figure A7, the Analytical Model approach was able to perform a correct anomaly classification at 96.1% of the attempts, and the Ensemble Triads Optimization approach was able to classify the anomaly correctly at 91.8% of the attempts. The final instance result, performing the hybridization using ANFIS, achieved a correct classification score of 97.6%, improving model-based or data-driven approaches isolated (see Figure A8). It is essential to highlight the high dependency of the data-driven approach with the features extraction step. In this work, even considering a model-based approach based on a single model and a comprehensive set of features and using ensemble triads and committees, the model-based approach performed better than the data-driven approach. It is important to remember that all comparisons were made exactly under the same conditions of seed, training set, training fold, and testing set. This assures a fair analysis of overall results for a single Generic Anomaly Detection Hybridization Algorithm (GADHA) execution. Thus, any change in one of those conditions produces different combinations of prediction algorithms. All provided machine learning algorithms that need randomness seeds will demand an uncertain number of executions and time to find an appropriate seed that yields better results. This is a common behavior in any situation where this kind of algorithm is employed. The computational cost depends on the number of selected basic algorithms and their respective performance and abilities, like parallel processing, for example. In this way, this value is the sum of the costs of each of the algorithms independently. However, if the total number of algorithms provided is less than the total number of available processing cores, the processing cost will be equal to the cost of the function with the worst performance. As the system noise is not modeled, and unpredictable disturbances are usually found in any complex system, data are necessary to differentiate normal behavior signature from abnormal operational conditions or failures response. The results achieved by the algorithm depend on a balanced number of training data and the desired states to be detected. Like in many data-driven approaches, it is challenging to estimate a minimum amount, but it is possible to find out such value during the experimentation. Considering that there are no algorithms inherent problems, if the minimum desired accuracy for each desired state was not achieved, more training data becomes necessary. The practical implications of GADHA for scaled industrialization can reduce development time and complexity for a large set of components and subsystems with further impact on development cost. States A1 and A2 are related to spring degradation, while A4 and A5 are related to operational misuse of the system. Considering the RUL estimation, to better discriminate the states from an abnormal operation of the system to a spring degradation can represent a significant advantage. Figure 7 will give support to our considerations. The figure may be considered a model-based degradation profile of a given landing-gear system. The Y-axis is the performance of the system and provides the degradation status. The X-axis is the total accumulated stress. P1 and P2 are two different points of degradation and F is the point when the system is in failure condition. The first detected abnormal condition was the point when an abnormal system state was classified. Many things can be stated. If the point is P1, the predicted RUL is the prediction based on P1 through F and can be expressed in cycles. The same can be declared about P2 and the expected RUL based on P2. The classification problem can change the perception of the user in terms of the right accumulated stress.