# Fuzzy and Neural Network Approaches to Wind Turbine Fault Diagnosis
## 1 Introduction
In particular, the design of simple and viable solutions to FDI are considered here, with respect for example, to more complex methodologies relying on convolutional neural networks or support vector machines. In fact, as already remarked above, the FDI modules provide the direct reconstruction of the fault signals affecting the process, which represents the most robust and reliable fault indicator, as remarked e.g., in [5]. Moreover, the FDI design addressed in the paper is also enhanced by the procedure for deriving the fault reconstructors that are estimated via data-driven approaches. In more detail, the first data-driven strategy proposed in this work exploits Takagi-Sugeno (TS) fuzzy prototypes [17], which are estimated via a fuzzy clustering algorithm and from the data-driven identification developed in [18]. As alternative tool, a second approach is also proposed, which uses Neural Networks (NNs) to derive the nonlinear dynamic relations between the input and output measurements of WT process and the faults affecting the plant. The selected structures belong to the feed-forward Multi-Layer Perceptron (MLP) NN class that includes also Auto-Regressive with eXogenous (ARX) inputs in order to model nonlinear dynamic links among the data. In this way, the training of these Nonlinear ARX (NARX) prototypes for fault estimation can exploit standard back-propagation training algorithms, as recalled e.g., in [19]. The designed FDI schemes are tested via a high-fidelity simulator of a WT process, which describes its behaviour in healthy and faulty conditions. This simulator, which represents a well-established benchmark [20], includes the presence of uncertainty, disturbance and model-reality mismatch effects, thus allowing to verify the reliability and robustness characteristics of the proposed FDI methodologies. Moreover, this work has validated the efficacy of the designed FDI techniques by exploiting a more realistic scenario, which consists of a hardware-in-the-loop (HIL) tool. The same authors have already investigated similar topics in previous papers. For example, this study analyses the solutions addressed e.g., in [21], but taking into account a more realistic real-time system illustrated in Section 4. The fuzzy methodology was also proposed by the same authors in [22], which considered the development of recursive algorithms for the implementation of adaptive laws relying on Linear Parameter Varying (LPV) systems. However, the approach considered in this paper estimates the FDI models by means of off-line procedures. Moreover, this study improves the solutions achieved e.g., in [23], but applied to a wind farm. The paper [24] proposed the design of a FTC using the input-output data from a single WT, by exploiting the results obtained in [25]. On the other hand, this work considers the verification and the validation of the developed FDI methodologies by exploiting a HIL tool, proposed considered in a preliminary paper by the same authors [26]. It is worth highlighting the main aims of the article. * The paper proposes two data-driven techniques for the FDI of a WT system;
* the developed FDI strategies are based on the direct reconstruction of the fault functions affecting the monitored process. In this way, the diagnostic residuals are represented by the estimated fault signals;
* the residual generators are organised into a bank structure in order to accomplish the fault isolation task. Their structures rely on Fuzzy Systems (FSs) in the form of TS models and dynamic NNs;
* a fault sensitivity analysis enhances the design of the residual generators, by a proper selection of their inputs;
* the developed FDI schemes are applied to a WT benchmark first; moreover, the verification of their robustness and reliability features is performed using a HIL tool representing the realistic behaviour of a WT process. The paper has the following structure. Section 2 briefly summarises the WT simulator, as it represents a well-established benchmark [3;20].