针对齿轮箱单一传感器故障识别精度波动大、数据利用率低、可靠性低及故障诊断模型在多工况下泛化能力不足等问题,提出了一种加权融合多通道数据与深度迁移模型的齿轮箱故障诊断方法。首先,为了充分挖掘齿轮箱多通道数据的信息,提出了...针对齿轮箱单一传感器故障识别精度波动大、数据利用率低、可靠性低及故障诊断模型在多工况下泛化能力不足等问题,提出了一种加权融合多通道数据与深度迁移模型的齿轮箱故障诊断方法。首先,为了充分挖掘齿轮箱多通道数据的信息,提出了基于信息熵加权的多通道融合方法,采用信息熵法计算各通道数据的融合权重,并对各通道的采样数据进行加权融合。其次,利用源域的融合数据对深度迁移模型进行预训练,将预训练得到的模型参数作为目标域模型的初始化参数,同时冻结目标域模型特征提取器的参数,并利用目标域的融合数据对目标域模型分类器的参数进行微调,实现深度迁移模型从源域到目标域的迁移以适应新的目标样本识别任务。最后,齿轮箱多工况迁移诊断试验结果表明,所提方法可有效用于齿轮箱的故障诊断,相比传统迁移学习方法平衡分布自适应算法(balanced distribution adaptation,BDA)、迁移成分分析(transfer component analysis,TCA)、联合分布自适应算法(joint distribution adaptation,JDA)、统计分布和几何空间联合调整算法(joint geometric and statistical alignment,JGSA)、测地线流式核算法(geodesic flow kernel,GFK)及深度迁移学习方法自适应批归一化(adaptive batch normalization,AdaBN)、多核最大均值差异(multi-kernel maximum mean discrepancy,MK-MMD)、深度卷积迁移学习网络(deep convolutional transfer learning network,DCTLN)这8种当前常用方法,具有更高的平均迁移诊断精度和变工况下良好的泛化性能。展开更多
Nowadays aviation accidents have become one of the major causes of severe injuries and fatalities around the world. This attracts the research community to look into aviation safety by applying data analysis technique...Nowadays aviation accidents have become one of the major causes of severe injuries and fatalities around the world. This attracts the research community to look into aviation safety by applying data analysis techniques based on an advanced machine learning algorithm. An ensemble classification model based on Aviation Safety Reporting System(ASRS) has been proposed to analyze aviation safety targeting the people injured in the system.The ensemble classification model shall contain two modules: the data-driven module consisting of data cleaning, feature selection,and imbalanced data division and reorganization, and the modeldriven module stacked by Random Forest(RF), XGBoost(XGB),and Light Gradient Boosting Machine(LGBM) separately. The results indicate that the ensemble model could solve the data imbalance while vastly improving accuracy. LGBM illustrates higher accuracy and faster run in the analysis of a single model of the ASRS-based imbalanced data, while the ensemble model has the best performance in classification at the same time. The ensemble model proposed for imbalanced data classification can provide a certain reference for similar data processing while improving the safety of civil aviation.展开更多
Objective: to apply the out-of-hospital continuous nursing in the group of schizophrenic patients, and analyze the effect of intervention on the quality of life of patients with mental classification. Methods: the tim...Objective: to apply the out-of-hospital continuous nursing in the group of schizophrenic patients, and analyze the effect of intervention on the quality of life of patients with mental classification. Methods: the time of sample data extraction was concentrated in the interval from April 2020 to December 2020. The number of cases was 62. All the samples were equally divided into two groups. The odd-even grouping method, with different names (control group, study group) to distinguish, the use of conventional nursing control group as the basic reference, analysis of the application of out-of-hospital extended nursing group of the quality of life scores, negative emotions, mental state and other recovery effects. Results: before treatment, no significant difference was ensured in the relevant control data (P > 0.05). After intervention, the control index data of the study group was better, which was recorded into the statistical software (P < 0.05). Conclusion: out-of-hospital extended nursing intervention measures in patients with schizophrenia has achieved a relatively ideal experimental outcome, which should be further promoted in clinical practice.展开更多
文摘针对齿轮箱单一传感器故障识别精度波动大、数据利用率低、可靠性低及故障诊断模型在多工况下泛化能力不足等问题,提出了一种加权融合多通道数据与深度迁移模型的齿轮箱故障诊断方法。首先,为了充分挖掘齿轮箱多通道数据的信息,提出了基于信息熵加权的多通道融合方法,采用信息熵法计算各通道数据的融合权重,并对各通道的采样数据进行加权融合。其次,利用源域的融合数据对深度迁移模型进行预训练,将预训练得到的模型参数作为目标域模型的初始化参数,同时冻结目标域模型特征提取器的参数,并利用目标域的融合数据对目标域模型分类器的参数进行微调,实现深度迁移模型从源域到目标域的迁移以适应新的目标样本识别任务。最后,齿轮箱多工况迁移诊断试验结果表明,所提方法可有效用于齿轮箱的故障诊断,相比传统迁移学习方法平衡分布自适应算法(balanced distribution adaptation,BDA)、迁移成分分析(transfer component analysis,TCA)、联合分布自适应算法(joint distribution adaptation,JDA)、统计分布和几何空间联合调整算法(joint geometric and statistical alignment,JGSA)、测地线流式核算法(geodesic flow kernel,GFK)及深度迁移学习方法自适应批归一化(adaptive batch normalization,AdaBN)、多核最大均值差异(multi-kernel maximum mean discrepancy,MK-MMD)、深度卷积迁移学习网络(deep convolutional transfer learning network,DCTLN)这8种当前常用方法,具有更高的平均迁移诊断精度和变工况下良好的泛化性能。
基金Supported by the Joint Fund of National Natural Science Foundation of China and Civil Aviation Administration of China (U1833110)。
文摘Nowadays aviation accidents have become one of the major causes of severe injuries and fatalities around the world. This attracts the research community to look into aviation safety by applying data analysis techniques based on an advanced machine learning algorithm. An ensemble classification model based on Aviation Safety Reporting System(ASRS) has been proposed to analyze aviation safety targeting the people injured in the system.The ensemble classification model shall contain two modules: the data-driven module consisting of data cleaning, feature selection,and imbalanced data division and reorganization, and the modeldriven module stacked by Random Forest(RF), XGBoost(XGB),and Light Gradient Boosting Machine(LGBM) separately. The results indicate that the ensemble model could solve the data imbalance while vastly improving accuracy. LGBM illustrates higher accuracy and faster run in the analysis of a single model of the ASRS-based imbalanced data, while the ensemble model has the best performance in classification at the same time. The ensemble model proposed for imbalanced data classification can provide a certain reference for similar data processing while improving the safety of civil aviation.
文摘Objective: to apply the out-of-hospital continuous nursing in the group of schizophrenic patients, and analyze the effect of intervention on the quality of life of patients with mental classification. Methods: the time of sample data extraction was concentrated in the interval from April 2020 to December 2020. The number of cases was 62. All the samples were equally divided into two groups. The odd-even grouping method, with different names (control group, study group) to distinguish, the use of conventional nursing control group as the basic reference, analysis of the application of out-of-hospital extended nursing group of the quality of life scores, negative emotions, mental state and other recovery effects. Results: before treatment, no significant difference was ensured in the relevant control data (P > 0.05). After intervention, the control index data of the study group was better, which was recorded into the statistical software (P < 0.05). Conclusion: out-of-hospital extended nursing intervention measures in patients with schizophrenia has achieved a relatively ideal experimental outcome, which should be further promoted in clinical practice.
