The vibration signals extracted from gearbox are usually masked in heavy background noise. Analyzing the feature components of these signals is challenging and crucial to incipient fault diagnosis. The Kurtogram has been verified as a very powerful and practical tool in the mechanical fault diagnosis due to its advantages in detecting and characterizing transients in signals. However, the accuracy of the kurtogram is limited because it is based on short-time Fourier transform (STFT) or FIR filter in extracting transient characteristics from a noisy signal. Therefore, it is necessary to develop a more accurate filter to overcome its shortcomings and further improve its fault detection accuracy. Wavelet transform has been widely applied in the past as a powerful tool to analyze the non-stationary signals, and an alpha-stable distribution model is often used to describe the statistical features of non-Gaussian signals. In this paper, a Morlet wavelet filter is optimized based on the kurtogram and the alpha parameter of the alpha-stable distribution model. Through the analysis of simulation signals at different fault degrees and operating conditions, it can be concluded that alpha-stable distribution has better performance than kurtosis in measuring the non-Gaussian characteristics of an impulsive gear fault signal. The results obtained from the simulation and practical experiments confirm the superiority of the proposed method for incipient gear fault diagnosis. INDEX TERMS Incipient fault diagnosis, kurtogram, Morlet wavelet, adaptive filter, alpha-stable distribution.

With the increase in the installed capacity of wind power systems, the fault diagnosis and condition monitoring of wind turbines (WT) has attracted increasing attention. In recent years, machine learning (ML) has played a crucial role as an emerging technology for fault diagnosis in wind power systems has played a crucial role. Even though ML methods have shown great potential in dealing with the issues related to the fault diagnosis of WT, there are still some challenges encountered in many aspects. In this paper, typical fault diagnosis methods based on ML methods for wind power systems are thoroughly reviewed in terms of both theoretical fundamentals and industrial applications, including traditional machine learning (TML), artificial neural networks (ANN), deep learning (DL) and transfer learning (TL), in the development line of ML technologies. The advantages and disadvantages of various methods are analyzed and discussed. Meanwhile, a distribution diagram is provided for the discussions of ML methods applied for WT fault diagnosis, and the existing challenges on the applications for fault diagnosis based on ML for wind power generation systems are presented. Moreover, some prospects for future research directions are provided. INDEX TERMSwind turbines, machine learning, fault diagnosis, review Nomenclature AE autoencoder AI artificial intelligence ANN artificial neural networks ART adaptive resonance theory BPNN back propagation neural network CA clustering algorithm CNN convolutional neural network DAE denoising autoencoder DBN deep belief network FCM fuzzy C-means clustering HMM hidden Markov model LSSVM least squares support vector machine LSTM long short-term memory network ML machine learning RBFNN radial basis function neural network RF random forest RLM extreme learning machine RNN recurrent neural network RVM relevance vector machine SAE stacked autoencoder SCADA supervisory control and data acquisition SDAE stacked denoising autoencoders 23 SOM self-organizing map 24 SVM support vector machine 25 t-SNE t-distributed logistic neighbor embedding 26 TL transfer learning 27 TML traditional machine learning 28 VMD variational mode decomposition 29 WT wind turbines 30 I. INTRODUCTION 31 With the increasing consumption of fossil fuels and the 32 gradual deterioration of environmental problems, there is an 33 urgent need to find a clean and renewable energy source. 34 Wind energy is irreplaceable in energy structures owing 35 to its rapid growth. Wind power accounts for 20% of the 36 world's total electricity, and WT are receiving increasing 37 attention as the core components of wind power generators [1], 38 [2]. Usually, wind power generators are installed in remote 39 areas or offshore areas where traffic is inconvenient, and 40 the gearbox is generally installed in a sky above tens or 41even hundreds of meters from the ground. In addition, the 42 blades are often subjected to complex alternating impact loads

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Adaptive wavelet filtering is a very important fault feature extraction method in the domain of condition monitoring; however, owing to the time-consuming computation and difficulty of choosing criteria used to represent incipient faults, the engineering applications are limited to some extent. To detect incipient gear faults at a fast speed, a new criterion is proposed to optimize the parameters of the modified impulsive wavelet for constructing an optimal wavelet filter to detect impulsive gear faults. First, a new criterion based on spectral negentropy is proposed. Then, a novel search strategy is applied to optimize the parameters of the impulsive wavelet based on the new criterion. Finally, envelope spectral analysis is applied to determine the incipient fault characteristic frequency. Both the simulation and experimental validation demonstrated the superiority of the proposed approach.

Intelligent fault diagnosis for a single wind turbine is hindered by the lack of sufficient useful data, while multi-turbines have various faults, resulting in complex distributions. Collaborative intelligence can better solve these problems. Therefore, a peer-to-peer network is constructed with one node corresponding to one wind turbine in a cluster. Each node is equivalent and functional replicable with a new federated transfer learning method, including model transfer based on multi-task learning and model fusion based on dynamic adaptive weight adjustment. Models with convolutional neural networks are trained locally and transmitted among the nodes. A solution for the processes of data management, information transmission, model transfer and fusion is provided. Experiments are conducted on a fault signal testing bed and bearing dataset of Case Western Reserve University. The results show the excellent performance of the method for fault diagnosis of a gearbox in a wind turbine cluster.