针对锂电池健康状态(State of Health,SOH)估计和剩余使用寿命(Remaining Useful Life,RUL)预测过程中健康特征提取单一、估计精度低等问题,提出了一种Inception-LSTM模型用于锂电池SOH估计与RUL预测。首先选取合适的恒压恒流充电时间...针对锂电池健康状态(State of Health,SOH)估计和剩余使用寿命(Remaining Useful Life,RUL)预测过程中健康特征提取单一、估计精度低等问题,提出了一种Inception-LSTM模型用于锂电池SOH估计与RUL预测。首先选取合适的恒压恒流充电时间构建特征序列HF,并采用Pearson相关性系数分析HF和容量之间的相关性;另外针对特征变量的特征提取不够全面问题,采用Inception模型进行特征提取,采用LSTM进行时序建模,随后利用注意力机制进一步提取对电池健康度影响较大的特征来估计电池健康状态,利用该深度学习模型来挖掘电池在复杂使用条件下的动态变化特征。实验结果表明文章模型SOH估计最大均方根误差在3.86%以内,RUL预测最大误差在1个循环。实验结果表明该方法在SOH估计和RUL预测方面优于传统模型。展开更多
针对航空电缆电弧故障引起的微小电流变化难以识别的问题,提出了一种基于Inception模块和双向长短期记忆网络(bidirectional long short-term memory, BiLSTM)的交流串联电弧故障诊断方法。首先通过计算自相关系数的离散平方和(discrete...针对航空电缆电弧故障引起的微小电流变化难以识别的问题,提出了一种基于Inception模块和双向长短期记忆网络(bidirectional long short-term memory, BiLSTM)的交流串联电弧故障诊断方法。首先通过计算自相关系数的离散平方和(discrete sum of squares of the atocorrelation coefficient)、信息熵(Shannon entropy)以及小波能量熵(wavelet energy entropy)提取原始电流数据的特征,将特征合并形成新的特征矩阵,对原始数据实现特征增强。之后Inception-BiLSTM网络利用特征矩阵进行学习,最后完成对电弧故障的诊断。为了验证模型在实际环境中的诊断性能,在充分考虑实际情况下,基于航空电缆电弧模拟实验平台进行了振动试验、应力实验以及潮湿电缆实验,并将实验数据整合作为检测样本。实验结果表明,本文方法对于识别电弧故障有着较高的准确度,可以达到99.69%。展开更多
The utilization of Inlet Guide Vane (IGV) plays a key factor in affecting the instability evolution. Existing literature mainly focuses on the effect of IGV on instability inception that occurs in the rotor region. Ho...The utilization of Inlet Guide Vane (IGV) plays a key factor in affecting the instability evolution. Existing literature mainly focuses on the effect of IGV on instability inception that occurs in the rotor region. However, with the emergence of compressor instability starting from the stator region, the mechanism of various instability inceptions that occurs in different blade rows due to the change of IGV angles should be further examined. In this study, experiments were focused on three types of instability inceptions observed previously in a 1.5-stage axial flow compressor. To analyze the conversion of stall evolutions, the compressor rotating speed was set to 17 160 r/min, at which both the blade loading in the stator hub region and rotor tip region were close to the critical value before final compressor stall. Meanwhile, the dynamic test points with high-response were placed to monitor the pressures both at the stator trailing edges and rotor tips. The results indicate that the variation of reaction determines the region where initial instability occurs. Indeed, negative pre-rotation of the inlet guide vane leads to high-reaction, initiating stall disturbance from the rotor region. Positive pre-rotation results in low-reaction, initiating stall disturbance from the stator region. Furthermore, the type of instability evolution is affected by the radial loading distribution under different IGV angles. Specifically, a spike-type inception occurs at the rotor blade tip with a large angle of attack at the rotor inlet (−2°, −4° and −6°). Meanwhile, the critical total pressure ratio at the rotor tip is 1.40 near stall. As the angle of attack decreases, the stator blade loading reaches its critical boundary, with a value of approximately 1.35. At this moment, if the rotor tip maintains high blade loading similar to the stator hub, the partial surge occurs (0° and +2°);otherwise, the hub instability occurs (+4° and +6°).展开更多
This paper presents a sensitivity analysis method for analyzing the key factors affecting the stability problem of the transonic compressors.The adjoint method is integrated into the meridional stability model,a linea...This paper presents a sensitivity analysis method for analyzing the key factors affecting the stability problem of the transonic compressors.The adjoint method is integrated into the meridional stability model,a linear stability prediction model utilized to analyze the flow stability problem of the compressor,and the sensitivity analysis method is further developed for the flow stability problem of the compressor.The study selects the NASA Rot or 37,a transonic compressor,as the research object to verify the proposed method and explore the sensitive factors leading to the stall inception.The results of sensitivity analysis to both the flow parameters and the external source terms reveal that the stall inception is sensitive to the base flow field at the rotor tip and the stability margin of the compressor can be enhanced by improving the flow field at this region.Physical explanations are presented and discussed to correlate the three-dimensional flow field to the results obtained via the employed analysis method,which shows that flow structures and characteristics near the end-wall region,especially the tip leakage flow or the tip leakage vortex and its interaction with the shock wave,contribute to the stall inception.展开更多
由于皮肤黑色素癌图像存在类内差异大、样本数据集小等特点,采用深度残差网络可以有效解决训练过程中过拟合问题,提高识别准确率.但是深度残差网络模型的训练参数多,时间复杂度高.为了提高训练效率,提高识别准确率,首先从理论上分析了...由于皮肤黑色素癌图像存在类内差异大、样本数据集小等特点,采用深度残差网络可以有效解决训练过程中过拟合问题,提高识别准确率.但是深度残差网络模型的训练参数多,时间复杂度高.为了提高训练效率,提高识别准确率,首先从理论上分析了深度残差网络模型的结构,通过修改网络结构,利用Inception结构代替残差网络中的卷积层、池化层,减少模型的训练参数数量,降低时间复杂度.在此基础上,提出了基于Inception深度残差网络皮肤黑色素癌分类识别算法(Inception Deep Residual Network,IDRN),用Inception结构代替残差网络中的卷积池化层,用SeLU激活函数代替传统的ReLU函数.之后,在公开的黑色素癌皮肤镜图像ISIC2017数据集上进行实验验证.理论和实验表明,与传统的卷积神经网络ResNet50相比,本文提出的新的分类算法降低了时间复杂度,提高了识别准确率.展开更多
文摘针对锂电池健康状态(State of Health,SOH)估计和剩余使用寿命(Remaining Useful Life,RUL)预测过程中健康特征提取单一、估计精度低等问题,提出了一种Inception-LSTM模型用于锂电池SOH估计与RUL预测。首先选取合适的恒压恒流充电时间构建特征序列HF,并采用Pearson相关性系数分析HF和容量之间的相关性;另外针对特征变量的特征提取不够全面问题,采用Inception模型进行特征提取,采用LSTM进行时序建模,随后利用注意力机制进一步提取对电池健康度影响较大的特征来估计电池健康状态,利用该深度学习模型来挖掘电池在复杂使用条件下的动态变化特征。实验结果表明文章模型SOH估计最大均方根误差在3.86%以内,RUL预测最大误差在1个循环。实验结果表明该方法在SOH估计和RUL预测方面优于传统模型。
文摘针对航空电缆电弧故障引起的微小电流变化难以识别的问题,提出了一种基于Inception模块和双向长短期记忆网络(bidirectional long short-term memory, BiLSTM)的交流串联电弧故障诊断方法。首先通过计算自相关系数的离散平方和(discrete sum of squares of the atocorrelation coefficient)、信息熵(Shannon entropy)以及小波能量熵(wavelet energy entropy)提取原始电流数据的特征,将特征合并形成新的特征矩阵,对原始数据实现特征增强。之后Inception-BiLSTM网络利用特征矩阵进行学习,最后完成对电弧故障的诊断。为了验证模型在实际环境中的诊断性能,在充分考虑实际情况下,基于航空电缆电弧模拟实验平台进行了振动试验、应力实验以及潮湿电缆实验,并将实验数据整合作为检测样本。实验结果表明,本文方法对于识别电弧故障有着较高的准确度,可以达到99.69%。
基金support of the National Natural Science Foundation of China(No.52322603)the Science Center for Gas Turbine Project of China(Nos.P2022-B-II-004-001 and P2023-B-II-001-001)+1 种基金the Fundamental Research Funds for the Central Universities,Chinathe Beijing Nova Program of China(Nos.20220484074 and 20230484479).
文摘The utilization of Inlet Guide Vane (IGV) plays a key factor in affecting the instability evolution. Existing literature mainly focuses on the effect of IGV on instability inception that occurs in the rotor region. However, with the emergence of compressor instability starting from the stator region, the mechanism of various instability inceptions that occurs in different blade rows due to the change of IGV angles should be further examined. In this study, experiments were focused on three types of instability inceptions observed previously in a 1.5-stage axial flow compressor. To analyze the conversion of stall evolutions, the compressor rotating speed was set to 17 160 r/min, at which both the blade loading in the stator hub region and rotor tip region were close to the critical value before final compressor stall. Meanwhile, the dynamic test points with high-response were placed to monitor the pressures both at the stator trailing edges and rotor tips. The results indicate that the variation of reaction determines the region where initial instability occurs. Indeed, negative pre-rotation of the inlet guide vane leads to high-reaction, initiating stall disturbance from the rotor region. Positive pre-rotation results in low-reaction, initiating stall disturbance from the stator region. Furthermore, the type of instability evolution is affected by the radial loading distribution under different IGV angles. Specifically, a spike-type inception occurs at the rotor blade tip with a large angle of attack at the rotor inlet (−2°, −4° and −6°). Meanwhile, the critical total pressure ratio at the rotor tip is 1.40 near stall. As the angle of attack decreases, the stator blade loading reaches its critical boundary, with a value of approximately 1.35. At this moment, if the rotor tip maintains high blade loading similar to the stator hub, the partial surge occurs (0° and +2°);otherwise, the hub instability occurs (+4° and +6°).
基金supported by the National Natural Science Foundation of China(Nos.52306036,52325602 and U2441279)the National Science and Technology Major Project,China(Nos.Y2022-Ⅱ-0003-0006 and Y2022-Ⅱ-0002-0005)supported by the Project of National Key Laboratory of Science and Technology on Aero-Engine Aero-thermodynamics,China(Nos.6142702200101 and 2024-CXPT-GF-JJ-88-0103)。
文摘This paper presents a sensitivity analysis method for analyzing the key factors affecting the stability problem of the transonic compressors.The adjoint method is integrated into the meridional stability model,a linear stability prediction model utilized to analyze the flow stability problem of the compressor,and the sensitivity analysis method is further developed for the flow stability problem of the compressor.The study selects the NASA Rot or 37,a transonic compressor,as the research object to verify the proposed method and explore the sensitive factors leading to the stall inception.The results of sensitivity analysis to both the flow parameters and the external source terms reveal that the stall inception is sensitive to the base flow field at the rotor tip and the stability margin of the compressor can be enhanced by improving the flow field at this region.Physical explanations are presented and discussed to correlate the three-dimensional flow field to the results obtained via the employed analysis method,which shows that flow structures and characteristics near the end-wall region,especially the tip leakage flow or the tip leakage vortex and its interaction with the shock wave,contribute to the stall inception.
文摘由于皮肤黑色素癌图像存在类内差异大、样本数据集小等特点,采用深度残差网络可以有效解决训练过程中过拟合问题,提高识别准确率.但是深度残差网络模型的训练参数多,时间复杂度高.为了提高训练效率,提高识别准确率,首先从理论上分析了深度残差网络模型的结构,通过修改网络结构,利用Inception结构代替残差网络中的卷积层、池化层,减少模型的训练参数数量,降低时间复杂度.在此基础上,提出了基于Inception深度残差网络皮肤黑色素癌分类识别算法(Inception Deep Residual Network,IDRN),用Inception结构代替残差网络中的卷积池化层,用SeLU激活函数代替传统的ReLU函数.之后,在公开的黑色素癌皮肤镜图像ISIC2017数据集上进行实验验证.理论和实验表明,与传统的卷积神经网络ResNet50相比,本文提出的新的分类算法降低了时间复杂度,提高了识别准确率.
