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Remaining useful life prediction for turbofan engines by genetic algorithm-based selective ensembling and temporal convolutional network
ZHU Lin, NING Qian, LEI Yinjie, CHEN Bingcai
Journal of Computer Applications 2020, 40 (
12
): 3534-3540. DOI:
10.11772/j.issn.1001-9081.2020050661
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As the turbofan engine is one of the core equipment in the field of aerospace, its health condition determines whether the aircraft could work stably and reliably. And the prediction of the Remaining Useful Life (RUL) of turbofan engine is an important part of equipment monitoring and maintenance. In view of the characteristics such as complicated operating conditions, diverse monitoring data, and long time span existing in the turbofan engine monitoring process, a remaining useful life prediction model for turbofan engines integrating Genetic Algorithm-based Selective ENsembling (GASEN) and Temporal Convolutional Network (TCN) (GASEN-TCN) was proposed. Firstly, TCN was used to capture the inner relationship between data under long span, so as to predict the RUL. Then, GASEN was applied to ensemble multiple independent TCNs for enhancing the generalization performance of the model. Finally, the proposed model was compared with the popular machine learning methods and other deep neural networks on the general Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) dataset. Experimental results show that, the proposed model has higher prediction accuracy and lower prediction error than the state-of-the-art Bidirectional Long-Short Term Memory (Bi-LSTM) network under many different operating modes and fault conditions. Taking FD001 dataset as an example:on this dataset, the Root Mean Square Error (RMSE) of the proposed model is 17.08% lower than that of Bi-LSTM, and the relative accuracy (Accuracy) of the proposed model is 12.16% higher than that of Bi-LSTM. It can be seen that the proposed model has considerable application prospect in intelligent overhaul and maintenance of equipment.
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Optimized convolutional neural network method for classification of pneumonia images
DENG Qi, LEI Yinjie, TIAN Feng
Journal of Computer Applications 2020, 40 (
1
): 71-76. DOI:
10.11772/j.issn.1001-9081.2019061039
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Currently, Convolutional Neural Network (CNN) is applied in the field of pneumonia classification. Aiming at the hardness to improve the accuracy of pneumonia recognition of convolution network with shallow layers and simple structure, deep learning method was adopted; and concerning the problem that the deep learning method often consumes a lot of system resources, which makes the convolution network difficult to be deployed at user end, an classification method based on optimized convolution neural network was proposed. Firstly, according to the features of pneumonia images, AlexNet and Inception V3 models with good image classification performance were selected. Then, the characteristics of medical images were used to re-train the Inception V3 model with deeper layers and more complex structure. Finally, through knowledge distillation method, the trained "knowledge" (effective information) was extracted into AlexNet model, so as to reduce the occupancy of system resources and improve the accuracy. The experimental data show that after knowledge distillation, AlexNet model has the accuracy, specificity and sensitivity improved by 4.1, 7.45 and 1.97 percentage points respectively, and has the Graphics Processing Unit (GPU) occupation reduced by 51 percentage points compared with InceptionV3 model.
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