Explore the vast range of titles available.

During tunneling process, it is of critical importance to dynamically adjust operation parameters of shield machine due to changes of geological conditions. Cutterhead torque is one of the key load parameters, and its accurate prediction could adjust operational parameters including cutterhead rotational speed and tunneling speed in advance and avoid potential cutterhead jamming. Based on operation and state data collected by the monitoring system, we propose a residual-convolutional-LSTM neural network (RCLSTMNet) for forecasting cutter head torque in shield machine. On the basis of correlation analysis, parameters closely related to cutter head torque are selected as inputs by employing cosine similarity, which significantly reduces input dimension. Convolutional-LSTM neural network is fused and constructed for extracting deep useful features, while residual network module is utilized to avoid gradient disappearing and improve regression performance. Comparisons with recent data-driven cutterhead torque prediction methods are made on the actual engineering datasets, which demonstrate the presented RCLSTMNet outperforms the other data driven models in most cases. Moreover, the predicted curves of cutterhead torque using the proposed RCLSTMNet coincide with the actual curves much better than predicted curves using the other models. Meanwhile, the highest and average accuracy of RCLSTMNnet reach 98.1% and 95.6%, respectively.

Accurate and timely misfire fault diagnosis is of vital significance for diesel engines. However, existing algorithms are prone to fall into model over-fitting and adopt low energy-concentrated features. This paper presents a novel extreme gradient boosting-based misfire fault diagnosis approach utilizing the high-accuracy time–frequency information of vibration signals. First, diesel engine misfire tests were conducted under different spindle speeds, and the corresponding vibration signals were acquired via a triaxial accelerometer. The time-domain features of signals were extracted by using a time-domain statistics method, while the high-accuracy time–frequency domain features were obtained via the high-resolution multisynchrosqueezing transform. Thereafter, considering the nonlinearity and high dimensionality of the original characteristic data sets, the locally linear embedding method was employed for feature dimensionality reduction. Eventually, to avoid model overfitting, the extreme gradient boosting algorithm was utilized for diesel engine misfire fault diagnosis. Experiments under different spindle speeds and comprehensive comparisons with other evaluation methods were conducted to demonstrate the effectiveness of the proposed extreme gradient boosting-based misfire diagnosis method. The results verify that the highest classification accuracy of the proposed extreme gradient boosting-based algorithm is up to 99.93%. Simultaneously, the classification accuracy of the presented approach is approximately 24.63% higher on average than those of algorithms that use wavelet packet-based features. Moreover, it is shown that it obtains the minimum root mean squared error and can effectively prevent the model from falling into overfitting.

The onset of chatter vibration in milling operations will result in poor surface finish and low machining productivity. Hence, it is of crucial importance to predict and eliminate this undesirable instability. In this paper, an Adams–Simpson-based method is developed for the stability analysis of milling processes. The regenerative chatter for milling operations can be described by delay differential equations with time-periodic coefficients. After dividing the forced vibration time interval equally into small time intervals, the Adams–Moulton method and the Simpson method are adopted to construct the Floquet transition matrix over one tooth passing period. On this basis, the milling stability can be obtained by using the Floquet theory. The accuracy and efficiency of the proposed method are verified through two benchmark examples, in which comparisons with the first-order semi-discretization method and the Adams–Moulton-based method are conducted. The results demonstrate that the proposed method has both high computational efficiency and accuracy, thus it is of high industrial application value.

Chatter vibration in milling has been one crucial factor hindering the realization of high-performance machining. The corresponding stability analysis is of great significance for obtaining chatter-free machining parameters. Based on the predictor-corrector scheme, this paper develops an accurate and efficient holistic-discretization method for the stability analysis of milling processes. According to the system state equation, the period of the time-periodic coefficient matrix is divided into two time intervals. The forced vibration time period is then equidistantly discretized. Working as a holistic unit, the time-periodic parameter matrix, the state term, and the delay term are approximated over two different subintervals by the second-order Lagrange interpolations. Finally, the Floquet transition matrix can be constructed by taking advantage of the predictor-corrector scheme, and the milling stability can be semi-analytically determined by utilizing the Floquet theory. The computational accuracy of the proposed method is analyzed theoretically and illustrated by making comparisons with the first-order semi-discretization method (1st SDM), the second-order, and the third-order updated full-discretization methods (2nd UFDM and 3rd UFDM). The stability lobes for two benchmark milling models and the computational efficiency of these methods are presented to further verify the effectiveness of the proposed method. Theoretical analysis and numerical results validate that the proposed predictor-corrector-based holistic-discretization method achieves both high computational accuracy and efficiency for milling stability analysis. In conclusion, the proposed semi-analytical algorithm has a high potential for industrial applications.

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 F 1 score. Moreover, the proposed model significantly outperforms the comparison models.

Advancements in deep learning and computer vision have led to the discovery of numerous effective solutions to challenging problems in the field of agricultural automation. With the aim to improve the detection precision in the autonomous harvesting process of green asparagus, in this article, we proposed the DA-Mask RCNN model, which utilizes the depth information in the region proposal network. Firstly, the deep residual network and feature pyramid network were combined to form the backbone network. Secondly, the DA-Mask RCNN model added a depth filter to aid the softmax function in anchor classification. Afterwards, the region proposals were further processed by the detection head unit. The training and test images were mainly acquired from different regions in the basin of the Yangtze River. During the capturing process, various weather and illumination conditions were taken into account, including sunny weather, sunny but overshadowed conditions, cloudy weather, and daytime greenhouse conditions as well as nighttime greenhouse conditions. Performance experiments, comparison experiments, and ablation experiments were carried out using the five constructed datasets to verify the effectiveness of the proposed model. Precision, recall, and F1-score values were applied to evaluate the performances of different approaches. The overall experimental results demonstrate that the balance of the precision and speed of the proposed DA-Mask RCNN model outperform those of existing algorithms.

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 generalisation performances. The effectiveness of MSCNN‐LSTMNet is validated by experimental 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.

Induced by flexibility of the industrial robot, cutting tool or the workpiece, chatter in robotic machining process has detrimental effects on the surface quality, tool life and machining productivity. Consequently, accurate detection and timely suppression for such undesirable vibration is desperately needed to achieve high performance robotic machining. This paper presents a novel approach combining the notch filter and local maximum synchrosqueezing transform for the timely chatter identification in robotic drilling. The proposed approach is accomplished through the following steps. In the first step, the optimal matrix notch filter is designed to eliminate the interference of the spindle frequency and corresponding harmonic components to the measured acceleration signal. Subsequently, the high-resolution time–frequency information of the non-stationary filtered acceleration signal is acquired by employing local maximum synchrosqueezing transform (LMSST). On this basis, the filtered acceleration signal is divided into a finite number of equal-width frequency bands, and the corresponding sub-signal for each frequency band is obtained by summing the corresponding coefficient of the LMSST. Finally, to accurately depict the non-uniformity of energy distribution during the chatter incubation process, the statistical energy entropy is calculated and utilized as the indicator to detect chatter online. The effectiveness of the proposed approach is validated by a large number of robot drilling experiments with different cutting tools, workpiece materials and machining parameters. The results show that the presented local maximum synchrosqueezing-based approach can effectively recognize the chatter at an early stage during its incubation and development process.

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 drive. Currently, 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 accuracy. To address these issues, we propose a novel residual denoising and multiscale attention-based weighted domain adaptation network (RDMA-WDAN) for TBM main bearing fault diagnosis. Our approach skillfully designs a deep feature extractor incorporating residual denoising and multiscale attention modules, achieving better domain adaptation despite significant domain interference. The residual denoising component utilizes a convolutional block to extract noise features, removing them via residual connections. Meanwhile, 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 features. During training, a weighting mechanism is introduced to prioritize domain samples with clear fault features. This 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 method. Comparative 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 adaptation tasks, the RDMA–WDAN improves the accuracy by 45.672%, 23.188%, 43.266%, 16.077%, 5.716%, and 9.678% compared with baseline models.

Recording the trajectory of table tennis balls in real-time enables the analysis of the opponent’s attacking characteristics and weaknesses. The current analysis of the ball paths mainly relied on human viewing, which lacked certain theoretical data support. In order to solve the problem of the lack of objective data analysis in the research of table tennis competition, a target detection algorithm-based table tennis trajectory extraction network was proposed to record the trajectory of the table tennis movement in video. The network improved the feature reuse rate in order to achieve a lightweight network and enhance the detection accuracy. The core of the network was the “feature store & return” module, which could store the output of the current network layer and pass the features to the input of the network layer at the next moment to achieve efficient reuse of the features. In this module, the Transformer model was used to secondarily process the features, build the global association information, and enhance the feature richness of the feature map. According to the designed experiments, the detection accuracy of the network was 96.8% for table tennis and 89.1% for target localization. Moreover, the parameter size of the model was only 7.68 MB, and the detection frame rate could reach 634.19 FPS using the hardware for the tests. In summary, the network designed in this paper has the characteristics of both lightweight and high precision in table tennis detection, and the performance of the proposed model significantly outperforms that of the existing models.