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您的检索式：作者名="Honggan Yu"
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| 1 | Anti‐noise diesel engine misfire diagnosis using a multi‐scale CNN‐LSTM neural network with denoising module显示文摘Currently,accuracy of existing diesel engine fault diagnosis methods under strong noise and generalisation performance between different noise levels are still limited.A novel multi‐scale CNN‐LSTM neural network(MSCNN‐LSTMNet)is proposed with a residual‐CNN denoising module for anti‐noise diesel engine misfire diagnosis.First,a residual‐CNN module is designed for denoising the original vibration signal measured from the diesel engine cylinder and residual loss for constructing a new loss function is utilised.Considering the essential characteristics of measured vibration signals at different scales,a multi‐scale convolutional NN(CNN)block is designed to realize multi‐scale feature extraction.Specifically,multiple convolution layers with different branches and different convolution kernel sizes are utilised to extract different time scales features,enhancing the robustness of the model.On this basis,the LSTM is utilised to further extract sequential features for improving anti‐noise and generalisa-tion performances.The effectiveness of MSCNN‐LSTMNet is validated by experi-mental results of both one‐and hybrid‐cylinder diesel engine misfires diagnosis under various noise levels and working conditions.The results demonstrate that MSCNN‐LSTMNet achieved much better anti‐noise and generalisation performances than the existing methods.Under strong noise conditions(−10 dB signal‐to‐noise ratio)for four datasets,MSCNN‐LSTMNet obtained 97.561%average accuracy,while average accuracy for random forest,deep neural network,CNN and MSCNNNet were 73.828%,79.544%,82.247%,and 89.741%,respectively.Moreover,for 11 noise generalisation tasks between different noise levels,MSCNN‐LSTMNet obtained at least 96.679%,97.849%,98.892%,and 94.010%accuracy on the four datasets,which are much higher than those of the existing methods. | Chengjin Qin Yanrui Jin Zhinan Zhang Honggan Yu Jianfeng Tao Hao Sun Chengliang Liu | 2023 | CAAI Transactions on Intelligence Technology2023，8，3： | 1 |
| 2 | Geological information prediction for shield machine using an enhanced multi-head self-attention convolution neural network with two-stage feature extraction显示文摘Due to the closed working environment of shield machines,the construction personnel cannot observe the construction geological environment,which seriously restricts the safety and efficiency of the tunneling process.In this study,we present an enhanced multi-head self-attention convolution neural network(EMSACNN)with two-stage feature extraction for geological condition prediction of shield machine.Firstly,we select 30 important parameters according to statistical analysis method and the working principle of the shield machine.Then,we delete the non-working sample data,and combine 10 consecutive data as the input of the model.Thereafter,to deeply mine and extract essential and relevant features,we build a novel model combined with the particularity of the geological type recognition task,in which an enhanced multi-head self-attention block is utilized as the first feature extractor to fully extract the correlation of geological information of adjacent working face of tunnel,and two-dimensional CNN(2dCNN)is utilized as the second feature extractor.The performance and superiority of proposed EMSACNN are verified by the actual data collected by the shield machine used in the construction of a double-track tunnel in Guangzhou,China.The results show that EMSACNN achieves at least 96%accuracy on the test sets of the two tunnels,and all the evaluation indicators of EMSACNN are much better than those of classical AI model and the model that use only the second-stage feature extractor.Therefore,the proposed EMSACNN achieves high accuracy and strong generalization for geological information prediction of shield machine,which is of great guiding significance to engineering practice. | Chengjin Qin Guoqiang Huang Honggan Yu Ruihong Wu Jianfeng Tao Chengliang Liu | 2023 | Geoscience Frontiers2023，14，2： | 1 |
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