航空发动机剩余寿命(remaining useful life,RUL)预测是设备故障预测与健康管理(prognostics and health management,PHM)的核心问题。针对发动机数据维度高、滞后性强和复杂度高等挑战,提出了一种基于自训练权重的多尺度注意力双向长...航空发动机剩余寿命(remaining useful life,RUL)预测是设备故障预测与健康管理(prognostics and health management,PHM)的核心问题。针对发动机数据维度高、滞后性强和复杂度高等挑战,提出了一种基于自训练权重的多尺度注意力双向长短期记忆神经网络模型。通过不同尺度的双向长短期记忆神经网络(bidirectional long short-term memory neural network,BiLSTM)提取多尺度特征;提出一种基于自训练权重的融合算法,通过引入注意力机制进行不同尺度的特征筛选,以提高预测精度。将各模型在NASA的C-MAPSS数据集上进行实验对比,结果证明,所提出预测模型在准确率和均方根误差指标上均有所提升。展开更多
Based on the CNN-LSTM fusion deep neural network,this paper proposes a seismic velocity model building method that can simultaneously estimate the root mean square(RMS)velocity and interval velocity from the common-mi...Based on the CNN-LSTM fusion deep neural network,this paper proposes a seismic velocity model building method that can simultaneously estimate the root mean square(RMS)velocity and interval velocity from the common-midpoint(CMP)gather.In the proposed method,a convolutional neural network(CNN)Encoder and two long short-term memory networks(LSTMs)are used to extract spatial and temporal features from seismic signals,respectively,and a CNN Decoder is used to recover RMS velocity and interval velocity of underground media from various feature vectors.To address the problems of unstable gradients and easily fall into a local minimum in the deep neural network training process,we propose to use Kaiming normal initialization with zero negative slopes of rectifi ed units and to adjust the network learning process by optimizing the mean square error(MSE)loss function with the introduction of a freezing factor.The experiments on testing dataset show that CNN-LSTM fusion deep neural network can predict RMS velocity as well as interval velocity more accurately,and its inversion accuracy is superior to that of single neural network models.The predictions on the complex structures and Marmousi model are consistent with the true velocity variation trends,and the predictions on fi eld data can eff ectively correct the phase axis,improve the lateral continuity of phase axis and quality of stack section,indicating the eff ectiveness and decent generalization capability of the proposed method.展开更多
文摘航空发动机剩余寿命(remaining useful life,RUL)预测是设备故障预测与健康管理(prognostics and health management,PHM)的核心问题。针对发动机数据维度高、滞后性强和复杂度高等挑战,提出了一种基于自训练权重的多尺度注意力双向长短期记忆神经网络模型。通过不同尺度的双向长短期记忆神经网络(bidirectional long short-term memory neural network,BiLSTM)提取多尺度特征;提出一种基于自训练权重的融合算法,通过引入注意力机制进行不同尺度的特征筛选,以提高预测精度。将各模型在NASA的C-MAPSS数据集上进行实验对比,结果证明,所提出预测模型在准确率和均方根误差指标上均有所提升。
基金financially supported by the Key Project of National Natural Science Foundation of China (No. 41930431)the Project of National Natural Science Foundation of China (Nos. 41904121, 41804133, and 41974116)Joint Guidance Project of Natural Science Foundation of Heilongjiang Province (No. LH2020D006)
文摘Based on the CNN-LSTM fusion deep neural network,this paper proposes a seismic velocity model building method that can simultaneously estimate the root mean square(RMS)velocity and interval velocity from the common-midpoint(CMP)gather.In the proposed method,a convolutional neural network(CNN)Encoder and two long short-term memory networks(LSTMs)are used to extract spatial and temporal features from seismic signals,respectively,and a CNN Decoder is used to recover RMS velocity and interval velocity of underground media from various feature vectors.To address the problems of unstable gradients and easily fall into a local minimum in the deep neural network training process,we propose to use Kaiming normal initialization with zero negative slopes of rectifi ed units and to adjust the network learning process by optimizing the mean square error(MSE)loss function with the introduction of a freezing factor.The experiments on testing dataset show that CNN-LSTM fusion deep neural network can predict RMS velocity as well as interval velocity more accurately,and its inversion accuracy is superior to that of single neural network models.The predictions on the complex structures and Marmousi model are consistent with the true velocity variation trends,and the predictions on fi eld data can eff ectively correct the phase axis,improve the lateral continuity of phase axis and quality of stack section,indicating the eff ectiveness and decent generalization capability of the proposed method.
