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

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.