# Intelligent Fault Diagnosis Techniques Applied to an Offshore Wind Turbine System
## 4 Performance and Robustness Analysis
### Hardware-in-the-Loop Experiments  
The HIL test rig was implemented in order to validate the proposed fault diagnosis schemes in real-time conditions. This tool was formerly considered in [23], but for fault-tolerant control design purposes.  
The experimental setup in Figure 7 consists of three interconnected components:  
* Simulator: The offshore wind turbine system summarized in Section 2 was implemented in the LabVIEW(r) environment. This software tool runs on an industrial CPU, which allows real-time monitoring of the simulated system parameters.
* Onboard electronics: The fault diagnosis schemes were implemented in the AWC 500 system, which features standard wind turbine specifications. This element acquires the signals from the wind turbine simulator and processes the fault diagnosis solutions proposed in this study.
* Interface circuits: These facilitate communication between the simulator and the onboard electronics.  
The achieved performances were evaluated on the basis of the following computed indices, which were formerly proposed in [24]:  
* False Alarm Rate (FAR): the ratio between the number of wrongly-detected faults and the number of simulated faults;
* Missed Fault Rate (MFR): the ratio between the total number of missed faults and the number of simulated faults;
* True FDI Rate (TFR): the ratio between the number of correctly-detected faults and the number of simulated faults;
* Mean FDI Delay (MFD): the average time delay between fault occurrence and fault detection.  
Figure 7: The block diagram of the HIL test rig. FDI, Fault Detection and Isolation.  
A total of 1000 experiments were performed in order to compute these indices, as the efficacy of the developed fault diagnosis techniques depends on the model-reality mismatch and the actual measurements errors.  
Table 9 summarizes the results obtained by implementing fuzzy estimators using the real-time HIL setup.  
On the other hand, Table 10 reports the values achieved with the NN fault estimators implemented using the same real-time HIL setup.  
Some further remarks can be made here. When an accurate mathematical description of the system under diagnosis can be included in the design phase, model-based fault diagnosis techniques may yield the best performances. However, when modeling errors and uncertainty are present, the optimization and learning exploited by the proposed data-driven solutions lead to very accurate results. In fact, the TS fuzzy models led to interesting fault diagnosis capabilities, as they used the adaptation accumulated from offline simulations. On the other hand, the NN structures use the training stage, which can be computationally heavy. It can thus be concluded that the proposed data-driven approaches seem to represent powerful techniques that are able to cope with uncertainty and disturbances, as well as variable working conditions.  
Finally, the results reported here confirm the effectiveness of the developed fault diagnosis schemes when applied to a real-time test rig. Moreover, the robustness features of the proposed solutions support the viability of applying the proposed fault diagnosis techniques to real offshore wind turbine systems.