# Intelligent Fault Diagnosis Techniques Applied to an Offshore Wind Turbine System
## 5 Conclusions  
This paper presents the development and analysis of practical tools for performing fault diagnosis of a wind turbine system. The design of this indicator relies on the direct estimate of the fault itself and uses two data-driven schemes. These are proposed by the authors to be viable tools for coping  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Estimated Fault \(\hat{f}_{i}(k)\)** & **FAR** & **MFR** & **TFR** & **MFD** \\ \hline
1 & 0.007 & 0.006 & 0.899 & 0.014 \\
2 & 0.234 & 0.005 & 0.867 & 0.516 \\
3 & 0.004 & 0.004 & 0.914 & 0.080 \\
4 & 0.005 & 0.005 & 0.922 & 0.070 \\
5 & 0.006 & 0.007 & 0.905 & 0.097 \\
6 & 0.005 & 0.006 & 0.989 & 0.871 \\
7 & 0.701 & 0.007 & 0.981 & 6.987 \\
8 & 0.498 & 0.008 & 0.987 & 0.289 \\
9 & 0.197 & 0.176 & 0.798 & 0.399 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Performance indices with NN fault estimators.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Estimated Fault \(\hat{f}_{i}(k)\)** & **FAR** & **MFR** & **TFR** & **MFD** \\ \hline
1 & 0.005 & 0.005 & 0.995 & 0.077 \\
2 & 0.004 & 0.004 & 0.996 & 0.490 \\
3 & 0.004 & 0.004 & 0.996 & 0.080 \\
4 & 0.005 & 0.005 & 0.995 & 0.070 \\
5 & 0.003 & 0.004 & 0.997 & 0.060 \\
6 & 0.004 & 0.005 & 0.996 & 0.760 \\
7 & 0.005 & 0.004 & 0.995 & 0.640 \\
8 & 0.005 & 0.004 & 0.995 & 0.060 \\
9 & 0.004 & 0.005 & 0.996 & 0.180 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Performance indices with fuzzy fault estimators. MFR, Missed Fault Rate; TFR, True FDI Rate; MFD, Mean FDI Delay.  
with poor knowledge of the process dynamics in the presence of noise and disturbance effects. These data-driven schemes are based on fuzzy and neural network structures used to derive the nonlinear dynamic link between the input-output measurements and the considered fault signals. The selected prototypes belong to nonlinear autoregressive with exogenous input architectures, as they can describe any nonlinear dynamic relationship with an arbitrary degree of accuracy. The fault diagnosis strategies were tested via a high-fidelity simulator describing the normal and faulty behaviors of an offshore wind turbine plant. The achieved performances, in terms of reliability and robustness, were thus verified by considering the presence of uncertainty and disturbance effects simulated by the wind turbine benchmark. In order to assess the considered fault diagnosis solutions in a more realistic framework, hardware-in-the-loop experiments were also analyzed by means of an industrial computer interacting with onboard electronics. The achieved results highlight that data-driven approaches, such as fuzzy systems and neural networks, are able to lead to robust and reliable solutions, even if optimization and adaptation procedures are required. Further works will consider the application of these fault diagnosis schemes to real plants.  
Sample availability: The software simulation codes for the proposed fault diagnosis strategies and the proposed results are available from the authors in the Matlab and Simulink environments.  
[PERSON] conceived of and designed the simulations; moreover, he analyzed the methodologies and the achieved results; together with [PERSON], he also wrote the paper.  
This research received no external funding.  
The research work has been supported by the FAR2018 local fund from the University of Ferrara. The costs to publish in open access have been covered by the FIR2018 local fund from the University of Ferrara.  
The authors declare no conflicts of interest.