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A real-time multi-head mixed attention mechanism-based prediction method for tunnel boring machine disc cutter wear

Science China Technological Sciences 2025年第1期 290-311页

作者： Haodi WANG chengjin qin Honggan YU Zhinan ZHANG Guoqiang HUANG Zhengyang LIU Chengliang LIU State Key Laboratory of Mechanical System and Vibration School of Mechanical EngineeringShanghai Jiao Tong University

In the process of hard rock tunnel excavation, workers often need to enter the tunnel boring machine(TBM)cutterhead at regular intervals to measure cutter wear. However, this method is time-consuming and labor-intensive. Existing cutter prediction models primarily rely on geological parameters to predict overall cutter wear before construction, making it challenging to monitor real-time wear at different locations of the cutter and obtain accurate geological parameters. To address these challenges, this paper proposes a multi-head mixed attention mechanism-based method for real-time wear prediction of TBM disc cutter. First, a method of cutter wear normalization to eliminate measurement noise is explored. Then, considering the complex correlation of TBM operating parameters in feature and time dimensions, a new multi-head mixed attention mechanism model is designed to establish the dependency between different features and different moments, to better establish the mapping model between operating parameters and cutter wear. Finally, the current cutter wear state can be calculated by accumulating the wear amount of all previous small excavation sections. The effectiveness of the method is verified by using field data from the Mumbai tunnel. The results demonstrate that the method is capable of real-time prediction of front cutter and edge cutter wear on the test set, achieving an average accuracy rate of 95.75%. Moreover, the method can update the cutter wear status after every meter of excavation, which has good real-time performance. In addition, the average accuracy of cutter wear prediction of the proposed method is 11.75%, 10.375%, 3.875%, 3.625%, 1.375%, 3.125%, 2.25%, and 0.75%higher than that of LSTM, CNN, LSTM-CNN, CACNN, SACNN, MMADNN, MTACNN, and MFACNN. In summary, this approach offers an accurate prediction of cutter wear state while reducing inspection time and costs and has high application value.

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A pre-generated matrix-based method for real-time robotic drilling chatter monitoring

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Chinese Journal of Aeronautics 2019年第12期32卷 2755-2764页

作者： Jianfeng TAO chengjin qin Dengyu XIAO Haotian SHI Chengliang LIU State Key Laboratory of Mechanical System and Vibration School of Mechanical Engineering Shanghai Jiao Tong University

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.

关键词： Aviation manufacturing Fast wavelet packet decomposition Matrix notch filter Real-time chatter monitoring Robotic drilling operations

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A multi-scale convolutional neural network for bearing compound fault diagnosis under various noise conditions

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Science China(Technological Sciences) 2022年第11期65卷 2551-2563页

作者： JIN YanRui qin chengjin ZHANG ZhiNan TAO JianFeng LIU ChengLiang School of Mechanical Engineering Shanghai Jiao TongUniversityShanghai 200240China

Recently,with the urgent demand for data-driven approaches in practical industrial scenarios,the deep learning diagnosis model in noise environments has attracted increasing ***,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 *** paper presents a novel anti-noise multi-scale convolutional neural network(AM-CNN)for solving the issue of compound fault diagnosis under different intensity ***,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 ***,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 ***,a multi-label classifier is utilized to simultaneously distinguish multiple bearing *** proposed AM-CNN is verified under our collected compound fault *** 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 ***,the experimental results show that AM-CNN can achieve good cross-domain performance with 100%accuracy and 100%***,AM-CNN has the potential to be an accurate and stable fault diagnosis tool.

关键词： anti-noise residual pre-processing block bearing compound fault multi-label classifier multi-scale convolution feature extraction

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A novel LSTM-autoencoder and enhanced transformer-based detection method for shield machine cutterhead clogging

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Science China(Technological Sciences) 2023年第2期66卷 512-527页

作者： qin chengjin WU RuiHong HUANG GuoQiang TAO JianFeng LIU ChengLiang State Key Laboratory of Mechanical System and Vibration School of Mechanical EngineeringShanghai Jiao Tong UniversityShanghai 200240China

Shield tunneling machines are paramount underground engineering equipment and play a key role in tunnel *** 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 *** this study,we propose an enhanced transformer-based detection model for detecting the cutterhead clogging status of shield tunneling ***,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 ***,variational mode decomposition and wavelet transform are employed to denoise the *** 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 *** 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 ***,an enhanced transformer-based neural network is constructed to extract essential implicit features and detect cutterhead clogging *** collected from actual tunnel construction projects are used to verify the proposed *** results show that the proposed model achieves accurate detection of shield machine cutterhead clogging status,with 98.85%accuracy and a 0.9786 F1 ***,the proposed model significantly outperforms the comparison models.

关键词： shield tunneling machine cutterhead clogging fault diagnosis autoencoder multihead self-attention mechanism imbalanced data

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A residual denoising and multiscale attention-based weighted domain adaptation network for tunnel boring machine main bearing fault diagnosis

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Science China(Technological Sciences) 2024年第8期67卷 2594-2618页

作者： ZHONG Tao qin chengjin SHI Gang ZHANG ZhiNan TAO JianFeng LIU ChengLiang State Key Laboratory of Mechanical System and Vibration School of Mechanical EngineeringShanghai Jiao Tong UniversityShanghai 200240China

As a critical component of a tunnel boring machine(TBM),the precise condition monitoring and fault analysis of the main bearing is essential to guarantee the safety and efficiency of the TBM cutter ***,under conditions of strong noise and complex working environments,traditional signal decomposition and machine learning methods struggle to extract weak fault features and achieve high fault classification *** address these issues,we propose a novel residual denoising and multiscale attention-based weighted domain adaptation network(RDMA-WDAN)for TBM main bearing fault *** approach skillfully designs a deep feature extractor incorporating residual denoising and multiscale attention modules,achieving better domain adaptation despite significant domain *** residual denoising component utilizes a convolutional block to extract noise features,removing them via residual ***,the multiscale attention module uses a 4-branch convolution and 3 pooling strategy-based channel–spatial attention mechanism to extract multiscale features,concentrating on deep fault *** training,a weighting mechanism is introduced to prioritize domain samples with clear fault *** optimizes the deep feature extractor to obtain common features,enhancing domain adaptation.A low-speed and heavy-loaded bearing testbed was built,and fault data sets were established to validate the proposed *** experiments show that in noise domain adaptation tasks,proposed the RDMA–WDAN significantly improves target domain classification accuracy by 42.544%,23.088%,43.133%,16.344%,5.022%,and 9.233%over dense connection network(DenseNet),squeeze–excitation residual network(SE-ResNet),antinoise multiscale convolutional neural network(ANMSCNN),multiscale attention module-based convolutional neural network(MSAMCNN),domain adaptation network,and hybrid weighted domain adaptation(HWDA).In combined noise and working condition domain a

关键词： tunnel boring machine(TBM) main bearing fault diagnosis domain adaptation antinoise cross working RDMA-WDAN

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Geological information prediction for shield machine using an enhanced multi-head self-attention convolution neural network with two-stage feature extraction

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Geoscience Frontiers 2023年第2期14卷 86-104页

作者： chengjin qin Guoqiang Huang Honggan Yu Ruihong Wu Jianfeng Tao Chengliang Liu State Key Laboratory of Mechanical System and Vibration School of Mechanical EngineeringShanghai Jiao Tong UniversityShanghai 200240China

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 *** 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 ***,we select 30 important parameters according to statistical analysis method and the working principle of the shield ***,we delete the non-working sample data,and combine 10 consecutive data as the input of the ***,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 *** 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,*** 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 ***,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.

关键词： Geological information prediction Shield machine Enhanced multi-head self-attention CNN

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Multi-class 12-lead ECG automatic diagnosis based on a novel subdomain adaptive deep network

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Science China(Technological Sciences) 2022年第11期65卷 2617-2630页

作者： JIN YanRui LI ZhiYuan LIU Yunqing LIU JinLei qin chengjin ZHAO LiQun LIU ChengLiang State Key Laboratory of Mechanical System and Vibration School of Mechanical EngineeringShanghai Jiao Tong UniversityShanghai 200240China Department of Cardiology Shanghai First People's Hospital Afiliated to Shanghai Jiao Tong University Shanghai 200080China

Arrhythmia is a common type of cardiovascular disease,which has become the leading cause of global ***,the automatic 12-lead ECG diagnosis system based on numerous labelled data has attracted increasing ***,labelling 12-lead ECG recordings is a complex and time-consuming task for *** then,the existence of data distribution differences limits the direct cross domain use of the trained *** by subdomain adaptation methods,this paper designs a novel subdomain adaptative deep network(SADN)for excavating diagnosis knowledge from source domain ***,the convolutional layer,residual blocks and SE-Residual blocks are utilized for extracting meaningful deep features ***,the feature classifier uses these deep features for obtaining the final diagnosis ***,designing a novel loss function with local maximum mean discrepancy is utilized for restricting data distribution discrepancy from different ***,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 ***,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 ***,SADN has promising potential as an auxiliary diagnostic tool for the clinical environment.

关键词： arrhythmia detection subdomainadaptation deep network 12-lead ECG

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A novel lightweight computerized ECG interpretation approach based on clinical 12-lead data

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Science China(Technological Sciences) 2024年第2期67卷 449-463页

作者： LIU Yunqing qin chengjin LIU JinLei JIN YanRui LI ZhiYuan ZHAO LiQun LIU ChengLiang School of Mechanical Engineering Shanghai Jiao Tong UniversityShanghai 200240China Key Labof Artificial Intelligence(Ministryof Education) AI InstituteShanghai Jiao Tong UniversityShanghai 200240China Department of Cardiology Shanghai First Peoples Hospital Affiliated to Shanghai Jiao Tong UniversityShanghai 200080China

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 ***,we proposed an innovative lightweight computerized ECG interpretation approach based on 12-lead *** 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 *** 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 ***,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 *** 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 ***,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.

关键词： computerized ECG interpretation large-scale 12-lead clinical ECG database lightweight neural network

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A novel multi-channel CNN-LSTM and transformer-based network for diesel engine misfire diagnosis under different noise conditions

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Science China(Technological Sciences) 2024年第9期67卷 2965-2967页

作者： LI Ou LEI JunBo qin chengjin ZHANG ZhiNan TAO JianFeng LIU ChengLiang State Key Laboratory of Mechanical System and Vibration School of Mechanical EngineeringShanghai Jiao Tong UniversityShanghai 200240China

Misfire fault, as a major fault of diesel engines, usually occurs because of low fuel quality, insufficient compression, ignition system failure, *** engines may experience issues such as reduced power, increased fuel consumption, and increased noise when misfire happens. Therefore, timely detection and diagnosis of diesel engine misfire faults are of great significance in the industry [1].

关键词： Diesel diagnosis network

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Anti‐noise diesel engine misfire diagnosis using a multi‐scale CNN‐LSTM neural network with denoising module

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CAAI Transactions on Intelligence Technology 2023年第3期8卷 963-986页

作者： chengjin qin Yanrui Jin Zhinan Zhang Honggan Yu Jianfeng Tao Hao Sun Chengliang Liu State Key Laboratory of Mechanical System and Vibration School of Mechanical EngineeringShanghai Jiao Tong UniversityShanghaiChina

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 ***,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 *** the essential characteristics of measured vibration signals at different scales,a multi‐scale convolutional NN(CNN)block is designed to realize multi‐scale feature ***,multiple convolution layers with different branches and different convolution kernel sizes are utilised to extract different time scales features,enhancing the robustness of the *** this basis,the LSTM is utilised to further extract sequential features for improving anti‐noise and generalisa-tion *** 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 *** results demonstrate that MSCNN‐LSTMNet achieved much better anti‐noise and generalisation performances than the existing *** 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%,***,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.

关键词： fault diagnosis machine learning neural network

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