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The safe and reliable operation of the pitch system is essential for the stable and efficient operation of a wind turbine (WT). The pitch fault data collected by supervisory control and data acquisition systems (SCADA) often contain a wide variety of variables, leading to redundant features that interfere with the accuracy of final diagnosis results, making it difficult to meet requirements. Also, the problem of extracting only local features while ignoring global information is present in the feature extraction process using the deep Convolutional Neural Network (CNN) model. To address these issues, the global average correlation coefficient is proposed in this article to measure the correlation between multiple variables in SCADA data. By considering the correlation among multiple variables comprehensively, redundant features are effectively eliminated, enhancing the accuracy of fault diagnosis. Furthermore, a new local amplification fusion architecture network (LAFA-Net) based on multi-head attention (MHA) is introduced. An efficient local feature extraction module, designed to enhance the model's perception of detailed features while maintaining global context information, is first introduced. LAFA-Net integrates the advantages of CNN and MHA, efficiently extracting and fusing valuable features from filtered data for both local and global aspects. Experiments on real pitch fault data demonstrate that the global average correlation coefficient effectively screens out redundant features in the dataset that negatively impact fault diagnosis results, thereby improving diagnosis efficiency and accuracy. The LAFA-Net model, capable of accurately diagnosing multiple types of pitch faults, shows a superior classification effect and accuracy compared to several advanced models, along with a faster convergence speed. [ABSTRACT FROM AUTHOR]

Deep-learning-based fault diagnosis of wind turbine has played a significant role in advancing the renewable energy industry. However, the imbalanced data sampled by the supervisory control and data acquisition systems has led to low diagnosis accuracy. Additionally, deep neural networks can encounter issues like gradient vanishing and insufficient feature learning during backpropagation when the model is too deep. This article introduces a novel approach that is based on dynamic weight loss functions to modulate unbalanced data and improve diagnostic accuracy by focusing on misclassification of a small sample number. The proposed approach employs a 1D-CNN model and an ensemble-learning-based convolution neural network (EL-CNN) to enhance diversity of models and complementarity of feature learning. The EL-CNN model addresses the problem of local features being overlooked and provides more accurate results. The effectiveness of this proposed approach is well demonstrated through experimental cases on real wind turbine pitch system fault data. Two different networks using three different loss functions and three state-of-the-art fault diagnosis models are tested, demonstrating the EL-CNN model's superiority. [ABSTRACT FROM AUTHOR]

Current large animal models that could closely resemble the typical features of cirrhotic portal hypertension in human have not been well established. Thus, we aimed to develop and describe a reliable and reproducible canine cirrhosis model of portal hypertension. A total of 30 mongrel dogs were randomly divided into four groups: 1 (control; n = 5), 2 (portal vein stenosis [PVS]; n = 5], 3 (thioacetamide [TAA]; n = 5), and 4 (PVS plus TAA; n = 15). After 4-months modeling period, liver and spleen CT perfusion, abdominal CT scans, portal hemodynamics, gastroscopy, hepatic function, blood routine, the bone marrow, liver, and spleen histology were studied. The animals in group 2 (PVS) developed extrahepatic portosystemic collateral circulation, particularly esophageal varices, without hepatic cirrhosis and portal hypertension. Animals from group 3 (TAA) presented mild cirrhosis and portal hypertension without significant symptoms of esophageal varices and hypersplenism. In contrast, animals from group 4 (PVS + TAA) showed well-developed micronodular and macronodular cirrhosis, associated with significant portal hypertension and hypersplenism. The combination of PVS and TAA represents a novel, reliable, and reproducible canine cirrhosis model of portal hypertension, which is associated with the typical characteristics of portal hypertension, including hypersplenism. [ABSTRACT FROM AUTHOR]