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| 1 | A pre-generated matrix-based method for real-time robotic drilling chatter monitoring显示文摘Currently, due to the detrimental effects on surface finish and machining system, chatter has been one crucial factor restricting robotic drilling operations, which improve both quality and efficiency of aviation manufacturing. Based on the matrix notch filter and fast wavelet packet decomposition, this paper presents a novel pre-generated matrix-based real-time chatter monitoring method for robotic drilling. Taking vibration characteristics of robotic drilling into account, the matrix notch filter is designed to eliminate the interference of spindle-related components on the measured vibration signal. Then, the fast wavelet packet decomposition is presented to decompose the filtered signal into several equidistant frequency bands, and the energy of each sub-band is obtained. Finally, the energy entropy which characterizes inhomogeneity of energy distribution is utilized as the feature to recognize chatter on-line, and the effectiveness of the presented algorithm is validated by extensive experimental data. The results show that the proposed algorithm can effectively detect chatter before it is fully developed. Moreover, since both filtering and decomposition of signal are implemented by the pre-generated matrices, calculation for an energy entropy of vibration signal with 512 samples takes only about 0.690 ms. Consequently, the proposed method achieves real-time chatter monitoring for robotic drilling, which is essential for subsequent chatter suppression. | Jianfeng TAO Chengjin QIN Dengyu XIAO Haotian SHI Chengliang LIU | 2019 | Chinese Journal of Aeronautics2019，32，12： | 9 |
| 2 | Multi-class 12-lead ECG automatic diagnosis based on a novel subdomain adaptive deep network显示文摘Arrhythmia is a common type of cardiovascular disease,which has become the leading cause of global deaths.Recently,the automatic 12-lead ECG diagnosis system based on numerous labelled data has attracted increasing attention.However,labelling 12-lead ECG recordings is a complex and time-consuming task for clinicians.And then,the existence of data distribution differences limits the direct cross domain use of the trained model.Enlighted by subdomain adaptation methods,this paper designs a novel subdomain adaptative deep network(SADN)for excavating diagnosis knowledge from source domain datasets.Firstly,the convolutional layer,residual blocks and SE-Residual blocks are utilized for extracting meaningful deep features automatically.Additionally,the feature classifier uses these deep features for obtaining the final diagnosis predictions.Further,designing a novel loss function with local maximum mean discrepancy is utilized for restricting data distribution discrepancy from different datasets.Finally,the Clinical Outcomes in Digital ECG and 1st China Physiological Signal Challenge datasets are utilized for evaluating the superiority of SADN,which presents that SADN enhances algorithm performance on the unlabelled target domain dataset.Further,compared with the existing methods,the proposed network structure acquires better performance with a F1-macro of 89.43%and a F1-macro1 of 87.09%.Besides,among the 4 kinds of ECG abnormalities,the diagnostic effect of the SADN is better than that of cardiology residents.Thus,SADN has promising potential as an auxiliary diagnostic tool for the clinical environment. | JIN YanRui LI ZhiYuan LIU YunQing LIU JinLei QIN Chengjin ZHAO LiQun LIU ChengLiang | 2022 | Science China(Technological Sciences)2022，65，11： | 2 |
| 3 | A novel LSTM-autoencoder and enhanced transformer-based detection method for shield machine cutterhead clogging显示文摘Shield tunneling machines are paramount underground engineering equipment and play a key role in tunnel construction.During the shield construction process,the“mud cake”formed by the difficult-to-remove clay attached to the cutterhead severely affects the shield construction efficiency and is harmful to the healthy operation of a shield tunneling machine.In this study,we propose an enhanced transformer-based detection model for detecting the cutterhead clogging status of shield tunneling machines.First,the working state data of shield machines are selected from historical excavation data,and a long short-term memory-autoencoder neural network module is constructed to remove outliers.Next,variational mode decomposition and wavelet transform are employed to denoise the data.After the preprocessing,nonoverlapping rectangular windows are used to intercept the working state data to obtain the time slices used for analysis,and several time-domain features of these periods are extracted.Owing to the data imbalance in the original dataset,the k-means-synthetic minority oversampling technique algorithm is adopted to oversample the extracted time-domain features of the clogging data in the training set to balance the dataset and improve the model performance.Finally,an enhanced transformer-based neural network is constructed to extract essential implicit features and detect cutterhead clogging status.Data collected from actual tunnel construction projects are used to verify the proposed model.The results show that the proposed model achieves accurate detection of shield machine cutterhead clogging status,with 98.85%accuracy and a 0.9786 F1 score.Moreover,the proposed model significantly outperforms the comparison models. | QIN ChengJin WU RuiHong HUANG GuoQiang TAO JianFeng LIU ChengLiang | 2023 | Science China(Technological Sciences)2023，66，2： | 2 |
| 4 | 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 |
| 5 | A multi-scale convolutional neural network for bearing compound fault diagnosis under various noise conditions显示文摘Recently,with the urgent demand for data-driven approaches in practical industrial scenarios,the deep learning diagnosis model in noise environments has attracted increasing attention.However,the existing research has two limitations:(1)the complex and changeable environmental noise,which cannot ensure the high-performance diagnosis of the model in different noise domains and(2)the possibility of multiple faults occurring simultaneously,which brings challenges to the model diagnosis.This paper presents a novel anti-noise multi-scale convolutional neural network(AM-CNN)for solving the issue of compound fault diagnosis under different intensity noises.First,we propose a residual pre-processing block according to the principle of noise superposition to process the input information and present the residual loss to construct a new loss function.Additionally,considering the strong coupling of input information,we design a multi-scale convolution block to realize multi-scale feature extraction for enhancing the proposed model’s robustness and effectiveness.Finally,a multi-label classifier is utilized to simultaneously distinguish multiple bearing faults.The proposed AM-CNN is verified under our collected compound fault dataset.On average,AM-CNN improves 39.93%accuracy and 25.84%F1-macro under the no-noise working condition and 45.67%accuracy and 27.72%F1-macro under different intensity noise working conditions compared with the existing methods.Furthermore,the experimental results show that AM-CNN can achieve good cross-domain performance with 100%accuracy and 100%F1-macro.Thus,AM-CNN has the potential to be an accurate and stable fault diagnosis tool. | JIN YanRui QIN ChengJin ZHANG ZhiNan TAO JianFeng LIU ChengLiang | 2022 | Science China(Technological Sciences)2022，65，11： | 1 |
| 6 | 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 |
| 7 | A novel lightweight computerized ECG interpretation approach based on clinical 12-lead data显示文摘Although 12-lead electrocardiograms(ECGs)provide a wide range of spatiotemporal characteristics,interpreting them for arrhythmia detection is difficult due to a lack of reliable large-scale clinical datasets.Herein,we proposed an innovative lightweight computerized ECG interpretation approach based on 12-lead data.Our model was trained,validated,and tested on 53845 standard 12-lead ECG records collected at Shanghai First People’s Hospital in affiliation with Shanghai Jiao Tong University.The experiments revealed that our approach had a classification accuracy of 94.41%in the classification task of seven types of rhythms,which was markedly superior to related single-lead and 12-lead ECG classification methods.Moreover,the average receiver operating characteristic area under the curve reached a value of 0.940,and the precision values for sinus tachycardia and sinus bradycardia were 0.945 and 0.91,respectively,with specificity values of 0.996 and 0.994.By employing our boosting method,we were able to improve the accuracy to 94.85%.To investigate the performance degradation of the proposed neural network in some classes,an ECG cardiologist was enlisted to review questionable ECGs;this process provides a promising direction for network performance improvement.Therefore,the proposed computerized ECG interpretation approach has practical significance because it could help professional physicians analyze patients’heart conditions based on real-time 12-lead ECG or grade their disease severity in advance. | LIU YunQing QIN ChengJin LIU JinLei JIN YanRui LI ZhiYuan ZHAO LiQun LIU ChengLiang | 2024 | Science China(Technological Sciences)2024，67，2： | 0 |
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