# Fuzzy and Neural Network Approaches to Wind Turbine Fault Diagnosis
## 4 Simulation Results, Experimental Validation, Comparisons and Discussion
### Discussion and Final Remarks
Other explicit disturbance decoupling techniques can take advantage of their robustness capabilities, e.g., the NLGA-AF, but with quite complicated and not straightforward design procedures. The FS and NN-based schemes rely on the learning accumulated from off-line simulations, but the training stage can be computationally heavy. Regarding the considered methods using RFS, or FS, they seem rather simple and straightforward, even if optimisation stages can be required. It is worth noting how the achieved results have highlighted that data-driven approaches, such as fuzzy structures are able to provide good performances. However, they can be easily outperformed by self-learning schemes, representing data-driven solutions that did not require optimisation stages, adaptation procedures or disturbance compensation methods. Further investigations will be oriented to apply the developed methodologies to real WT installations, in the presence of more realistic disturbance and uncertainty effects. In fact, the methodologies designed in this paper were proposed with the perspective to implement effective FDI solutions that could be included in real installations to improve their reliability and availability, thus increasing the effectiveness of the health monitoring, while improving the energy production, and reducing O&M costs. Note also that the solutions proposed in this paper rely on data-driven schemes. In fact, model-based methods are designed to detect any discrepancy between real system and model behaviours, and it is assumed that a discrepancy signal is related to (has a response from) a fault. However, the same difference signal can respond to model mismatch or noise in real measurements, which can be (erroneously) detected as a fault, giving rise to a 'false alarm' in detection. These considerations have led to investigate 'robust' methods, in which particular attention is paid to the discrimination between actual faults and errors due to model mismatch. Moreover, this work focused on the determination of 'good' solutions suitable for robust diagnosis. Moreover, the paper also highlighted how 'accurate models' can be obtained from real data. The paper considered the case in which noise and disturbance affects the acquired data. The FDI strategies can be obtained by means of both model-based and data-driven approaches. Comparisons between these strategies were thus addressed. Therefore, the purpose of the study is to provide guidelines for the data-driven design of robust and reliable FDI schemes. Moreover, attention was paid to the practical application of the methods to realistic WT installations, as reported in this last section. On the other hand, this study can aim also to encourage technology transfer in different engineering fields. In fact, the development of FDI procedures has impacts on all areas of the control discipline. For example, the proposed solutions can be applied to different processes, see e.g., the case of image and speech recognition [34; 35]. Finally, the key features of this study include viable approaches relying on data-driven methodologies for robust and reliable fault diagnosis, as well as extended application studies using different WT measurement data.