Aviation accidents are currently one of the leading causes of significant injuries and deaths worldwide. This entices researchers to investigate aircraft safety using data analysis approaches based on an advanced mach...Aviation accidents are currently one of the leading causes of significant injuries and deaths worldwide. This entices researchers to investigate aircraft safety using data analysis approaches based on an advanced machine learning algorithm.To assess aviation safety and identify the causes of incidents, a classification model with light gradient boosting machine (LGBM)based on the aviation safety reporting system (ASRS) has been developed. It is improved by k-fold cross-validation with hybrid sampling model (HSCV), which may boost classification performance and maintain data balance. The results show that employing the LGBM-HSCV model can significantly improve accuracy while alleviating data imbalance. Vertical comparison with other cross-validation (CV) methods and lateral comparison with different fold times comprise the comparative approach. Aside from the comparison, two further CV approaches based on the improved method in this study are discussed:one with a different sampling and folding order, and the other with more CV. According to the assessment indices with different methods, the LGBMHSCV model proposed here is effective at detecting incident causes. The improved model for imbalanced data categorization proposed may serve as a point of reference for similar data processing, and the model’s accurate identification of civil aviation incident causes can assist to improve civil aviation safety.展开更多
为识别光伏组件故障类型,提高光伏系统发电效率,提出了一种基于改进CNN-SVM模型的光伏组件红外图像故障诊断方法。首先以光伏组件红外图像为输入样本构建改进CNN模型,采用全局平均池化层代替传统CNN模型的全连接层,在进行图像特征提取...为识别光伏组件故障类型,提高光伏系统发电效率,提出了一种基于改进CNN-SVM模型的光伏组件红外图像故障诊断方法。首先以光伏组件红外图像为输入样本构建改进CNN模型,采用全局平均池化层代替传统CNN模型的全连接层,在进行图像特征提取的同时降低模型参数量;利用数据增强和批归一化技术提高模型泛化能力,降低模型过拟合。其次采用非线性支持向量机SVM代替传统CNN模型中的Softmax分类器,以提高光伏组件红外图像故障识别准确率。最后采用Infrared Solar Modules数据集对所提模型进行了实例验证。结果表明:与传统CNN模型相比,改进CNN-SVM模型故障诊断准确率高,对各故障类型的识别能力强。展开更多
基金supported by the National Natural Science Foundation of China Civil Aviation Joint Fund (U1833110)Research on the Dual Prevention Mechanism and Intelligent Management Technology f or Civil Aviation Safety Risks (YK23-03-05)。
文摘Aviation accidents are currently one of the leading causes of significant injuries and deaths worldwide. This entices researchers to investigate aircraft safety using data analysis approaches based on an advanced machine learning algorithm.To assess aviation safety and identify the causes of incidents, a classification model with light gradient boosting machine (LGBM)based on the aviation safety reporting system (ASRS) has been developed. It is improved by k-fold cross-validation with hybrid sampling model (HSCV), which may boost classification performance and maintain data balance. The results show that employing the LGBM-HSCV model can significantly improve accuracy while alleviating data imbalance. Vertical comparison with other cross-validation (CV) methods and lateral comparison with different fold times comprise the comparative approach. Aside from the comparison, two further CV approaches based on the improved method in this study are discussed:one with a different sampling and folding order, and the other with more CV. According to the assessment indices with different methods, the LGBMHSCV model proposed here is effective at detecting incident causes. The improved model for imbalanced data categorization proposed may serve as a point of reference for similar data processing, and the model’s accurate identification of civil aviation incident causes can assist to improve civil aviation safety.
文摘为识别光伏组件故障类型,提高光伏系统发电效率,提出了一种基于改进CNN-SVM模型的光伏组件红外图像故障诊断方法。首先以光伏组件红外图像为输入样本构建改进CNN模型,采用全局平均池化层代替传统CNN模型的全连接层,在进行图像特征提取的同时降低模型参数量;利用数据增强和批归一化技术提高模型泛化能力,降低模型过拟合。其次采用非线性支持向量机SVM代替传统CNN模型中的Softmax分类器,以提高光伏组件红外图像故障识别准确率。最后采用Infrared Solar Modules数据集对所提模型进行了实例验证。结果表明:与传统CNN模型相比,改进CNN-SVM模型故障诊断准确率高,对各故障类型的识别能力强。