# Technological Advances on Fault Diagnosis in Wind Turbines: A Patent Analysis
## 3 Results and Discussion
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**Title** & **Technology** & **Main Findings and/or Conclusions** & **Reference** \\ \hline Multiview enhanced fault diagnosis for wind turbine gearboxing with fusion of vibration and current signals & Vibration Analysis + Canonical correlation analysis (CCA) & The paper undertook a novel evaluation of a multiview fault diagnosis framework enhanced to comprehend the correlated and complementary features between current and vibration signals, which were considered as two different but related views. They used an unsupervised multiview learning method based on canonical correlation analysis (CCA) to evaluate this correlation. The results have shown balanced fault characteristics and achieved higher performance in fault diagnosis, especially in composite faults, compared to methods based on unimodal signals. & The paper analyzed information on two theory quantifiers used to monitor and detect changes in the vibration signals of two operational wind turbines of 750 kW and 2 MW. The authors evaluated the signals by power spectrum (frequency domain method), wavelet transform (time-frequency domain method), and Bandt-Pomre (time-domain method). The results demonstrated that the proposed method could distinguish (cluster) well between the states of fault. & The paper analyzed information on two theory quantifiers used to monitor and detect changes in the vibration signals of two operational wind turbines of 750 kW and 2 MW. The authors evaluated the signals by power spectrum (frequency domain method), wavelet transform (time-frequency domain method), and Bandt-Pomre (time-domain method). The results demonstrated that the proposed method could distinguish (cluster) well between the states of fault. & In this article, researchers proposed a new depth network called deep residual network (DRN) to further analyze the raw data generated by WTs. In the method, the raw data gathered by the SCADA system are applied directly as inputs to the DRN network. Then, a convolutional residual building block (CRBB) was established by using convolutional layers and succeeding units. The results indicate that the proposed DRN achieved better performance and outperforms some published fault-detection methods. & The authors proposed a system for the automatic classification of thermographic images using a convolutional neural network developed via open-source libraries. The results showed a 99\% accuracy for a dataset of 1000 images using a multi-layer perceptron architecture and 100\% accuracy for a convolutional neural network. & [64] \\ Attention-guided joint learning CNN with noise robustness for bearing fault diagnosis and vibration signal denoising & Deep Learning + Vibration & The paper reports a novel attention-driven joint learning convolutional neural network (JL-CNN) for monitoring conditions. The fault diagnosis task (FD-Task) and the signal denoising task (SD-Task) are integrated into an end-to-end CNN architecture, reaching good noise robustness through dual-task joint learning. This method allowed FD-Task and SD-Task to achieve deep cooperation and mutual learning, and the results showed outstanding fault diagnosis capacity and signal denoising ability. & [65] \\ \hline \end{tabular}
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Table 2: Articles that focus on the use of condition monitoring (CD) and nondestructive testing (NDT) for fault diagnosis on Wind Turbines. in the North American wind industry, with 42,000 MW installed and more than 38,000 MW under service in the U.S. and Canada [66]. Compared to the top ten competitors in this set, Vestas Wind Systems A/S has approximately 13% of those records. In second place is the State Grid Corporation of China, with 18 documents. The State Grid Corporation of China (Beijing, China), founded on 29 December 2002, is a large corporation and is considered crucial to China's energy and economic security.