Attention-based long short-term memory fully convolutional network for chemical process fault diagnosis

A correct and timely fault diagnosis is important for improving the safety and reliability of chemical processes. With the advancement of big data technology, data-driven fault diagnosis methods are being extensively used and still have considerable potential. In recent years, methods based on deep neural networks have made significant breakthroughs, and fault diagnosis methods for industrial processes based on deep learning have attracted considerable research attention. Therefore, we propose a fusion deeplearning algorithm based on a fully convolutional neural network(FCN) to extract features and build models to correctly diagnose all types of faults. We use long short-term memory(LSTM) units to expand our proposed FCN so that our proposed deep learning model can better extract the time-domain features of chemical process data. We also introduce the attention mechanism into the model, aimed at highlighting the importance of features, which is significant for the fault diagnosis of chemical processes with many features. When applied to the benchmark Tennessee Eastman process, our proposed model exhibits impressive performance, demonstrating the effectiveness of the attention-based LSTM FCN in chemical process fault diagnosis.

Real-time UAV path planning based on LSTM network

To address the shortcomings of single-step decision making in the existing deep reinforcement learning based unmanned aerial vehicle(UAV)real-time path planning problem,a real-time UAV path planning algorithm based on long shortterm memory(RPP-LSTM)network is proposed,which combines the memory characteristics of recurrent neural network(RNN)and the deep reinforcement learning algorithm.LSTM networks are used in this algorithm as Q-value networks for the deep Q network(DQN)algorithm,which makes the decision of the Q-value network has some memory.Thanks to LSTM network,the Q-value network can use the previous environmental information and action information which effectively avoids the problem of single-step decision considering only the current environment.Besides,the algorithm proposes a hierarchical reward and punishment function for the specific problem of UAV real-time path planning,so that the UAV can more reasonably perform path planning.Simulation verification shows that compared with the traditional feed-forward neural network(FNN)based UAV autonomous path planning algorithm,the RPP-LSTM proposed in this paper can adapt to more complex environments and has significantly improved robustness and accuracy when performing UAV real-time path planning.

Time-varying parameters estimation with adaptive neural network EKF for missile-dual control system

In this paper, a filtering method is presented to estimate time-varying parameters of a missile dual control system with tail fins and reaction jets as control variables. In this method, the long-short-term memory(LSTM) neural network is nested into the extended Kalman filter(EKF) to modify the Kalman gain such that the filtering performance is improved in the presence of large model uncertainties. To avoid the unstable network output caused by the abrupt changes of system states,an adaptive correction factor is introduced to correct the network output online. In the process of training the network, a multi-gradient descent learning mode is proposed to better fit the internal state of the system, and a rolling training is used to implement an online prediction logic. Based on the Lyapunov second method, we discuss the stability of the system, the result shows that when the training error of neural network is sufficiently small, the system is asymptotically stable. With its application to the estimation of time-varying parameters of a missile dual control system, the LSTM-EKF shows better filtering performance than the EKF and adaptive EKF(AEKF) when there exist large uncertainties in the system model.

Landslide displacement prediction based on optimized empirical mode decomposition and deep bidirectional long short-term memory network

There are two technical challenges in predicting slope deformation.The first one is the random displacement,which could not be decomposed and predicted by numerically resolving the observed accumulated displacement and time series of a landslide.The second one is the dynamic evolution of a landslide,which could not be feasibly simulated simply by traditional prediction models.In this paper,a dynamic model of displacement prediction is introduced for composite landslides based on a combination of empirical mode decomposition with soft screening stop criteria(SSSC-EMD)and deep bidirectional long short-term memory(DBi-LSTM)neural network.In the proposed model,the time series analysis and SSSC-EMD are used to decompose the observed accumulated displacements of a slope into three components,viz.trend displacement,periodic displacement,and random displacement.Then,by analyzing the evolution pattern of a landslide and its key factors triggering landslides,appropriate influencing factors are selected for each displacement component,and DBi-LSTM neural network to carry out multi-datadriven dynamic prediction for each displacement component.An accumulated displacement prediction has been obtained by a summation of each component.For accuracy verification and engineering practicability of the model,field observations from two known landslides in China,the Xintan landslide and the Bazimen landslide were collected for comparison and evaluation.The case study verified that the model proposed in this paper can better characterize the"stepwise"deformation characteristics of a slope.As compared with long short-term memory(LSTM)neural network,support vector machine(SVM),and autoregressive integrated moving average(ARIMA)model,DBi-LSTM neural network has higher accuracy in predicting the periodic displacement of slope deformation,with the mean absolute percentage error reduced by 3.063%,14.913%,and 13.960%respectively,and the root mean square error reduced by 1.951 mm,8.954 mm and 7.790 mm respectively.Conclusively,this model not only has high prediction accuracy but also is more stable,which can provide new insight for practical landslide prevention and control engineering.

A forecasting model for wave heights based on a long short-term memory neural network

To explore new operational forecasting methods of waves,a forecasting model for wave heights at three stations in the Bohai Sea has been developed.This model is based on long short-term memory(LSTM)neural network with sea surface wind and wave heights as training samples.The prediction performance of the model is evaluated,and the error analysis shows that when using the same set of numerically predicted sea surface wind as input,the prediction error produced by the proposed LSTM model at Sta.N01 is 20%,18%and 23%lower than the conventional numerical wave models in terms of the total root mean square error(RMSE),scatter index(SI)and mean absolute error(MAE),respectively.Particularly,for significant wave height in the range of 3–5 m,the prediction accuracy of the LSTM model is improved the most remarkably,with RMSE,SI and MAE all decreasing by 24%.It is also evident that the numbers of hidden neurons,the numbers of buoys used and the time length of training samples all have impact on the prediction accuracy.However,the prediction does not necessary improve with the increase of number of hidden neurons or number of buoys used.The experiment trained by data with the longest time length is found to perform the best overall compared to other experiments with a shorter time length for training.Overall,long short-term memory neural network was proved to be a very promising method for future development and applications in wave forecasting.

基于多源信息融合和WOA-CNN-LSTM的外脚手架隐患分类预警研究

面对施工现场外脚手架隐患信息的多样性,传统的基于传感器监测的单一信号预警研究存在容错力不佳、含有信息有限等问题。针对施工现场外脚手架“图像+监测”数据,提出一种基于数据层和特征层信息融合的脚手架隐患分类预警方法。首先,利用Revit三维建模软件建立外脚手架实体模型,对不同初始隐患下的外脚手架进行有限元分析,划分隐患预警等级;其次,利用无迹卡尔曼滤波算法(Unscented Kalman Filter,UKF)及卷积长短时记忆网络(Convolutional Neural Network-Long Short Term Memory Network,CNN-LSTM)实现脚手架同类信息数据层融合及异类信息特征层融合;最后,通过实时收集西安市某在建项目落地式双排扣件式钢管脚手架隐患信息,对其进行分类预警,并使用鲸鱼优化算法(Whale Optimization Algorithm,WOA)对CNN-LSTM网络进行参数优化,发现隐藏节点个数为30、学习率为0.0072、正则化系数为1×10^(-4)时分类效果最佳,优化后预警精度达到了91.4526%。通过可视化WOA-CNN-LSTM、CNN-LSTM、CNN-SVM(Support Vector Machine,支持向量机)及CNN-GRU(Gate Recurrent Unit,门控循环单元)分类预警结果,证实了优化后的CNN-LSTM网络在脚手架分类预警方面的优越性。

基于注意力机制的CNN-BiLSTM的IGBT剩余使用寿命预测

针对绝缘栅双极型晶体管(IGBT)可靠性问题,提出了一种融合卷积神经网络(CNN)、双向长短期记忆(BiLSTM)网络和注意力机制的剩余使用寿命(RUL)预测模型,可用于IGBT的寿命预测。模型中使用CNN提取特征参数,BiLSTM提取时序信息,注意力机制加权处理特征参数。使用IGBT加速老化数据集对提出的模型进行验证。结果表明,对比自回归差分移动平均(ARIMA)、长短期记忆(LSTM)、多层LSTM(Multi-LSTM)、 BiLSTM预测模型,在均方根误差和决定系数等评价指标方面该模型的性能最优。验证了提出的寿命预测模型对IGBT失效预测是有效的。

基于CNN-LSTM和卷复制方法的高可用系统设计方法

针对单机服务器存在的单点故障问题,以及主备双机中存在的逻辑故障导致数据丢失的问题,设计了一种基于卷积和长短期记忆神经网络(CNN-LSTM)和卷复制方法的HA(High Availability)系统。系统至少包含两个节点,一个主节点以及一个或多个备用节点,主节点和备节点之间支持主备切换。每个服务器节点上包含4个模块,分别是负责接收配置信息与读写请求的代理模块;进行磁盘读写操作和重定向读写的磁盘I/O(输入输出)模块;负责主备节点间备份快照、映射表、数据块复制的卷复制模块以及基于CNN-LSTM进行状态检测的高可用模块。实验表明,该系统不仅可以解决单点故障问题,也可以解决主备双机集群中无法解决的逻辑错误问题;同时基于CNN-LSTM方法,自动针对服务器的运行健康状态进行分析和预测,可以根据预测结果自动通知管理员进行处理或自动进行主备切换。

组合模型对管道腐蚀速率预测的效能研究--基于注意力机制增强的CNN与LSTM模型

为评估卷积神经网络(Convolutional Neural Network,CNN)、长短期记忆(Long Short-Term Memory,LSTM)网络及结合的CNN-LSTM模型在管道腐蚀速率预测中的性能表现,特别引入注意力机制,以期提高模型对关键特征的捕捉能力和预测的准确性。分析影响管道腐蚀速率的环境因素作为模型输入,并通过注意力机制优化特征表示。结果表明,结合注意力机制的CNN-LSTM模型在准确性和可靠性上超越了单独的CNN或LSTM模型。这一结果不仅展示了深度学习模型通过技术增强了处理复杂数据的能力,也为实际工业应用中的时间序列预测提供了新的视角,同时证实了利用深度学习技术对管道腐蚀速率进行精确预测的可行性和有效性。

基于K均值聚类算法和LSTM神经网络的管道腐蚀阶段预测方法

针对声发射检测获得的管道腐蚀信号,提出了一种基于K均值(K-means)聚类算法和长短期记忆(LSTM)神经网络的管道腐蚀阶段预测方法。首先,利用K-means聚类算法将腐蚀信号分类,再构建LSTM神经网络模型,并采取了无监督学习的方式,以声发射波形为出发点,对模型进行参数优化,最后进行管道腐蚀阶段预测,并根据评价指标对模型进行评价。研究表明:对LSTM神经网络模型适当增加隐藏层,可以使得模型更加稳定,鲁棒性更好;与现有故障诊断模型相比,LSTM神经网络模型的精度更高。

基于VMD-SSA-LSTM考虑刀具磨损的数控铣床切削功率预测模型研究

传统的切削过程功率获取需要基于复杂的切削功率模型且很少考虑刀具磨损的影响,针对此设计了一种基于变分模态分解(VMD)、麻雀搜索算法(SSA)、长短时记忆(LSTM)神经网络的考虑刀具磨损的数控铣床切削功率预测模型,该模型无需解构数控铣床运行过程的能耗机理,基于一次性的历史实验数据即可实现数控铣床切削过程功率的高精度预测。首先,采用人工智能机器视觉技术对刀具磨损图片进行分析处理,获取刀具磨损图像的数字化特征,从而得到刀具最大磨损量;然后,建立基于VMD-SSA-LSTM考虑刀具磨损的数控铣床切削功率预测模型,利用VMD对数控铣床运行数据进行分解,采用SSA算法对LSTM神经网络超参数进行寻优,并将分解出的铣床运行数据分量输入到LSTM神经网络中,接着将每个分量的预测值相加,得到切削功率预测值;最后以面铣加工为例,将所提出的预测模型与BP神经网络、LSTM神经网络和传统模型进行对比分析,验证了所提模型的有效性和优越性。

SBAS-InSAR技术融合CNN-LSTM模型的矿区开采沉陷监测与预测

针对传统矿区开采沉陷监测方法耗费人力财力和预测预警模型较少的问题,研究提出一种基于短基线集合成孔径雷达干涉测量(Small Baseline Subset-Interferometry Synthetic Aperture Radar,SBAS-InSAR)技术和卷积神经网络(Convolutional Neural Networks,CNN)与长短期记忆网络(Long Short-Term Memory,LSTM)相结合的矿区开采沉陷监测预测方法。首先,利用SBAS-InSAR技术对建新煤矿进行矿区开采沉陷监测,获取了该矿区的年平均沉降速率和累计沉降值。用GNSS监测数据与SBAS-InSAR结果进行对比验证,其拟合效果较好。其次,在此基础上利用CNN-LSTM模型预测后6期沉降数据,其结果与CNN和LSTM预测结果进行对比。研究显示,CNN-LSTM模型的平均绝对误差(S_(MAE))和均方根误差(S_(RMSE))比单一的CNN和LSTM分别至少降低了44.8%和40.6%,其决定系数均高于98%。最后,进一步预测前6期和中6期沉降数据,验证了CNN-LSTM预测模型在时间上的一致性。因此,SBAS-InSAR融合CNN-LSTM模型在类似矿山开采沉陷监测和预测中有较好的应用前景。

基于Transformer-LSTM的闽南语唇语识别

针对端到端句子级闽南语唇语识别的问题,提出一种基于Transformer和长短时记忆网络(LSTM)的编解码模型.编码器采用时空卷积神经网络及Transformer编码器用于提取唇读序列时空特征,解码器采用长短时记忆网络并结合交叉注意力机制用于文本序列预测.最后,在自建闽南语唇语数据集上进行实验.实验结果表明:模型能有效地提高唇语识别的准确率.

基于CNN-BiLSTM的油田注水流量预测

针对深度学习中的RNN(Recurrent Neural Networks)常用于时间序列预测,但其存在难以对历史序列进行特征提取、以及无法突出关键信息的影响且时间序列过长时早期信息易丢失等问题,提出一种基于双重注意力机制CNN(Convolutional Neural Networks)-BiLSTM(Bi-directional Long Short-Term Memory)的油田注水流量预测方法。该方法以油田历史注水数据为输入,利用CNN层提取历史注水数据特征,并引入特征注意力机制层,通过计算权重值的方式为特征赋予相应权重,使关键特征更容易得到较大权重,进而对预测结果产生影响;BiLSTM层对数据进行时序建模,并引入时间步注意力机制,通过选取关键时间步并突出该时间步的隐藏状态表达,使早期隐藏状态不会随时间消失,能提升模型对长时间序列的预测效果,最后完成流量预测。以公开数据集和中国南部某地区油田注水数据为算例,并与MLP(Multilayer Perceptron)、GRU(Gate Recurrent Unit)、LSTM(Long Short-Term Memory)、BiLSTM,CNN进行对比,证明该方法在油田注水流量预测中精度更高,可帮助油田制定生产计划、减少资源浪费以及提高注采率,具有一定的实际工程应用价值。

基于SDP和MCNN-LSTM的齿轮箱故障诊断方法

齿轮箱在长期使用过程中,不可避免地会产生齿轮故障和轴承故障,严重影响传动精度和设备运行安全。基于此,针对齿轮箱常见故障类型,研究多通道对称点图案(symmetrized dot pattern, SDP)数据处理方法,并利用最小能量误差法实现SDP关键参数的选取。结合多尺度卷积神经网络(multi-scale convolutional neural network, MCNN)的空间处理优势、长短时记忆网络(long short term memory, LSTM)的时间处理优势及其良好的抗噪性和鲁棒性,提出了一种基于SDP和MCNN-LSTM的齿轮箱故障诊断模型。同时利用东南大学齿轮箱数据集,验证了基于SDP和MCNN-LSTM的齿轮箱故障诊断方法对齿轮和轴承常见故障类型特征提取的有效性,并与现有其他故障诊断方法进行对比,结果表明了所提方法具有更高的精度。

基于改进鲸鱼算法优化LSTM的粮油温度预测

针对粮油温度预测问题,提出一种基于改进鲸鱼优化算法(IWOA)优化长短时记忆神经网络(LSTM)的粮油温度预测模型.针对传统WOA算法收敛速度慢和容易陷入局部最优等问题,提出了应用Logistic混沌映射、Levy飞行策略等方法来提升WOA算法的种群丰富度和搜索能力的方法.采用真实粮油温度值与多种模型预测值对比,IWOA-LSTM的MAE、RMSE比其他模型分别降低了13.64%~68.33%、6.06%~60.39%,R^(2)提高了1.65%~14.27%.结果表明,本文所提模型可以准确预测未来粮油温度变化趋势.

基于注意力机制和CNN-LSTM融合模型的锂电池SOC预测

为提高锂电池荷电状态SOC(state-of-charge)预测精度,提出1种基于注意力机制和卷积神经网络-长短时记忆CNN-LSTM(convolution neural network-long short-term memory)融合模型的锂电池荷电状态预测方法。该模型采用一维CNN和LSTM神经网络学习得到SOC与锂电池放电数据的非线性关系,以及SOC序列存在的长期依赖性。同时,该模型采用“多对一”的结构,将当前时刻的锂电池SOC与多个历史时刻的放电数据建立映射关系,并通过注意力机制关注到对当前时刻SOC影响较大的历史放电数据,进一步提升SOC的预测准确度。动态工况下的锂电池SOC预测实验表明,该方法在不同温度条件下的平均预测误差为0.89%,与SVM、GRU和XGBoost相比,分别降低了81.2%、66.7%和56.5%,且优于未融合注意力机制的LSTM和CNN-LSTM,具有较高的预测精度和应用价值。

基于CNN-LSTM的永磁同步风力发电机转子偏心早期故障诊断

对永磁同步风力发电机转子早期动偏心和早期静偏心故障的特点和诊断方法进行研究,通过Ansys建立永磁同步风力发电机的早期动偏心和早期静偏心模型,提出一种基于CNN-LSTM的故障诊断和分类方法。通过对永磁同步风力发电机定子三相电流及其Welch功率谱数据的分析,判断是否为正常的动偏心趋势和静偏心趋势;然后通过空载电动势对不同故障程度进行分类。最后,在神经网络模型中完成故障诊断和分类任务。所提方法大大降低了设备维修成本,可准确快速地识别转子早期偏心故障。

基于CNN-LSTM的工业出水水质预测模型

工业废水含有多种污染物,提前预测工业废水水质从而快速对其进行相应处理具有重要意义。为此,研究提出了一种新的卷积神经网络(convolutional neural network,CNN)和长短期记忆网络(long short-term memory,LSTM)融合的工业废水水质污染物指标预测模型(CNN-LSTM)。为了更好地捕捉工业废水数据的时序性和动态性,模型设置了多个滑动窗口。使用CNN算法将时间序列数据进行高维特征提取,利用LSTM模型学习时间序列数据的时序特征,建立CNN-LSTM工业废水预测模型,并对废水水质中的化学需氧量(COD_(Cr))、氨氮、总磷(TP)3项指标进行预测分析。结果表明,与CNN和LSTM两个基准模型相比,CNN-LSTM预测模型的平均绝对值误差率(MAE)和均方误差率(MSE)均较小,预测效果较优。该模型能较好地实现对工业废水出水水质的准确预测,可为工业废水水质的在线监测和精准控制提供有效的、可行的技术支持和决策依据。

基于LSTM车速预测和深度确定性策略梯度的增程式电动汽车能量管理

为提高增程式电动汽车的能量管理性能,首先利用长短时记忆(LSTM)神经网络进行车速预测,然后计算出预测时域内的需求功率,并将其与当前时刻的需求功率共同输入深度确定性策略梯度(DDPG)智能体,由智能体输出控制量,最后通过硬件在环仿真验证了控制策略的实时性。结果表明,采用所提出的LSTM-DDPG能量管理策略相对于DDPG能量管理策略、深度Q网络(DQN)能量管理策略、功率跟随控制策略在世界重型商用车辆瞬态循环(WTVC)工况下的等效燃油消耗量分别减少0.613 kg、0.350 kg、0.607 kg,与采用动态规划控制策略时的等效燃油消耗量仅相差0.128 kg。