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
为了提高脑电情绪识别分类精度,最大限度利用脑电信号的空间和时间信息,提出一种Inception残差注意力卷积神经网络与双向长短期记忆(bi-directional long short-term memory, BiLSTM)网络相结合的新型架构时空Inception残差注意力网络...为了提高脑电情绪识别分类精度,最大限度利用脑电信号的空间和时间信息,提出一种Inception残差注意力卷积神经网络与双向长短期记忆(bi-directional long short-term memory, BiLSTM)网络相结合的新型架构时空Inception残差注意力网络。将脑电信号采集电极位置映射到二维矩阵中,采集信号作为通道,构成三维数据;将得到的三维数据输入到时空Inception残差注意力卷积网络之中,提取时空信息;将得到的特征输入到全连接层进行分类;将Inception结构引入脑电情绪识别领域,实现多尺度特征提取,并将电极映射到矩阵之中,保留电极位置信息,使用时空Inception残差注意力网络从时空两个维度获取脑电相关信息。实验表明,使用该模型对DEAP数据集进行情绪四分类可得到93.71%的准确度,相较于对比模型,识别精度提高了10%~20%。提出的模型在脑电信号情绪识别领域具有优良性能。展开更多
传统的雷达高分辨距离像(High Resolution Range Profile,HRRP)序列识别方法依赖于人工提取特征,并且在使用现有的经典深度学习方法识别小数据集时存在梯度消失和过拟合问题,导致收敛速度慢,识别率低。针对上述问题,提出了一种基于注意...传统的雷达高分辨距离像(High Resolution Range Profile,HRRP)序列识别方法依赖于人工提取特征,并且在使用现有的经典深度学习方法识别小数据集时存在梯度消失和过拟合问题,导致收敛速度慢,识别率低。针对上述问题,提出了一种基于注意力机制的集成Inception网络模型,通过集成Attention-Inception单分支网络,实现了HRRP序列更深层次特征的提取;通过对模型的损失函数加入L2正则化,缓解小数据集在集成网络中的过拟合问题;利用Inception Ⅰ和Inception Ⅱ结构提取HRRP序列多尺度特征,并引入注意力机制计算特征序列的分配权重;加入残差结构,减缓了集成网络梯度消失问题。在预处理后的HRRP序列上进行实验结果表明,所提方法的目标识别率达到93.3%,并且与未去除噪声的HRRP序列相比目标识别率提高了14.67%。展开更多
针对于框架结构的使用环境恶劣,同时常常伴随着大量的噪声,在使用普通的一维卷积神经网络对框架结构进行故障诊断时,存在无法做出有效故障诊断的问题。本研究在一种抗噪声能力较强的卷积神经网络中加入Inception模块,提出了一种识别率...针对于框架结构的使用环境恶劣,同时常常伴随着大量的噪声,在使用普通的一维卷积神经网络对框架结构进行故障诊断时,存在无法做出有效故障诊断的问题。本研究在一种抗噪声能力较强的卷积神经网络中加入Inception模块,提出了一种识别率和抗噪声能力更高的卷积神经网络—BICNN(Convolution Neural Network based on Inception),并用BICNN卷积神经网络基于数据驱动的方式,对楼体框架模型进行了集成故障诊断研究。集成诊断结果表明BICNN具有更高的识别率和较强的抗噪声能力,而且在训练步数较少的情况下振荡次数少收敛情况良好。因此采取本研究所提出的方法,对框架结构进行故障诊断时具有高诊断率和稳定性,为维护框架结构的稳定运行具有重大安全意义。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°).
文摘为了提高脑电情绪识别分类精度,最大限度利用脑电信号的空间和时间信息,提出一种Inception残差注意力卷积神经网络与双向长短期记忆(bi-directional long short-term memory, BiLSTM)网络相结合的新型架构时空Inception残差注意力网络。将脑电信号采集电极位置映射到二维矩阵中,采集信号作为通道,构成三维数据;将得到的三维数据输入到时空Inception残差注意力卷积网络之中,提取时空信息;将得到的特征输入到全连接层进行分类;将Inception结构引入脑电情绪识别领域,实现多尺度特征提取,并将电极映射到矩阵之中,保留电极位置信息,使用时空Inception残差注意力网络从时空两个维度获取脑电相关信息。实验表明,使用该模型对DEAP数据集进行情绪四分类可得到93.71%的准确度,相较于对比模型,识别精度提高了10%~20%。提出的模型在脑电信号情绪识别领域具有优良性能。
文摘针对于框架结构的使用环境恶劣,同时常常伴随着大量的噪声,在使用普通的一维卷积神经网络对框架结构进行故障诊断时,存在无法做出有效故障诊断的问题。本研究在一种抗噪声能力较强的卷积神经网络中加入Inception模块,提出了一种识别率和抗噪声能力更高的卷积神经网络—BICNN(Convolution Neural Network based on Inception),并用BICNN卷积神经网络基于数据驱动的方式,对楼体框架模型进行了集成故障诊断研究。集成诊断结果表明BICNN具有更高的识别率和较强的抗噪声能力,而且在训练步数较少的情况下振荡次数少收敛情况良好。因此采取本研究所提出的方法,对框架结构进行故障诊断时具有高诊断率和稳定性,为维护框架结构的稳定运行具有重大安全意义。
