# Fuzzy and Neural Network Approaches to Wind Turbine Fault Diagnosis
## 5 Conclusions  
This paper investigated two fault diagnosis solutions that can be considered as viable and effective strategies for condition monitoring of a wind turbine process. To this end, the work proposed the design of fault estimators by means of data-driven methodologies relying on fuzzy models and neural networks. These solutions represented effective methods that allow the management of partially unknown information of the system dynamics, while coping with measurement errors, the model-reality mismatch and other disturbance effects. Therefore, these data-driven methodologies were exploited to estimate the nonlinear dynamic relations between the input and output process measurements and the faults. To this aim, the fuzzy and neural network prototypes integrated auto-regressive with exogenous input descriptions, thus making them able to approximate unknown nonlinear dynamic functions with arbitrary degree of accuracy. Once these models are derived for fault diagnosis purpose, their capabilities were verified and validated by using a high-fidelity benchmark that simulates the healthy and the faulty behaviour of a wind turbine system. The benchmark was also useful to analyse the robustness and the reliability characteristics of the developed tools in the presence of model-reality mismatch and measurement errors featured by the wind turbine simulator. Moreover, a hardware-in-the-loop tool was finally implemented for testing the performance of the developed fault diagnosis strategies in a more realistic environment. A comparative analysis with different fault diagnosis methods was also performed to demonstrate the better performance of the proposed dynamic neural network strategy applied to the considered simulator. The proposed designs enhanced the derivation of these solutions by using data-driven training and learning algorithms that led to accurate fault identification and greater robustness. This approach saved the computing cost with reduced iterations, easier design and implementation. The achieved results highlighted that data-driven approaches, such as fuzzy structures were able to provide good performances. However, they were easily outperformed by self-learning schemes, representing data-driven solutions that did not require optimisation stages, adaptation procedures or disturbance compensation methods. Further works will consider the application of the considered methodologies to real installations, in the presence of more realistic disturbance and uncertainty effects. In fact, the methodologies developed in this paper were designed with the perspective to implement effective fault diagnosis solutions that could be included in real installations to improve their reliability and availability, thus increasing the effectiveness of the health monitoring, while improving energy production, operation and maintenance costs.  
Conceptualization, S.F. and S.S.; methodology, S.F.; software, S.F.; validation, S.F. and S.S.; formal analysis, S.S.; investigation, S.F.; resources, S.S.; data curation, S.F.; writing--original draft preparation, S.S.; writing--review and editing, S.S., S.F. and P.C.; visualization, S.F.; supervision, S.S.; project administration, S.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability StatementData are available from the authors upon request.  
The authors declare no conflict of interest.