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This study proposes a novel dual-stator linear-rotary ultrasonic motor. The piezoelectric ceramic excites both out-of-plane and in-plane vibration modes within the stator. These distinct vibration modes independently drive the slider (rotor), generating reciprocating linear and rotational motions, respectively. Finite element analysis and laser vibrometer-based vibration testing validated the motor's operational principle. The close agreement between simulated and measured resonant frequencies for both vibration modes, with mere discrepancies of 3 % and 4 %, respectively, underscores the accuracy of the stator’s vibrational characteristics. Subsequently, two stators are fabricated and assembled to the ultrasonic motor prototype. Experimental results demonstrate the motor’s impressive performance, achieving a maximum linear velocity of 265 mm/s and a peak rotational speed of 1600 rpm. Furthermore, the motor delivers a maximum thrust force of 0.18 N and a stalling torque of 1.8 mN·m.

In response to the problem of difficulty in extracting fault features of rolling bearings in wind turbine transmission systems under complex working conditions, which limits the accuracy of fault diagnosis. This article proposes an Adaptive Deep Learning based Rolling Bearing Fault Diagnosis Method (ADLM). Introducing dynamic convolution into Convolutional Neural Networks (CNNs) can adaptively capture data features; At the same time, the fishing optimization algorithm (CFOA) was used to optimize the hyperparameters of the bidirectional long short-term memory network (BiLSTM), and the CFOA-BiLSTM network was constructed to fully leverage its advantages in time series analysis. The specific implementation steps are as follows: first, preprocess the collected vibration signals and divide the processed dataset into a training set and a testing set; Then, parallel adaptive convolutional neural networks (ACNN) are used to process the training set and extract spatial domain local features from the vibration signal; Then, the features extracted from the two branches are weighted and fused through a dynamic weight adjustment mechanism, and the fused features are input into the CFOA-BiLSTM network to further capture the time-dependent features of the signal; Finally, the extracted features are input into the classifier to complete model training, and the model performance is evaluated using a test set. Experimental verification shows that on the dataset of Southeast University, the diagnostic accuracy of the ADLM model reached 98.52 %, demonstrating good reliability, robustness, and superiority in the diagnosis of rolling bearing faults.

In order to ensure the reliable operation of TBM excavation process, it is particularly important to analyze the vibration characteristics in complex surrounding rock environments. The coupling dynamics model of the TBM main drive system proposed in this article considers the structural characteristics of distributed support and multi-source inputs, as well as nonlinear internal excitations such as bearing dynamic stiffness, gear meshing error, and tooth side clearance, which can more accurately calculate the dynamic characteristics of the main drive system. Based on the TBM scale test-bed, the modeling method and the vibration response of the main components were compared and verified. Based on the coupled dynamic model of the main driving system, the vibration characteristics of the driving system were analyzed under different excavation penetrations and different proportions of soft and hard surrounding rocks. The analysis results show that during the process of penetration from 5 mm to 6 mm, the average vibration increase speed is the highest, reaching 0.1493 g/mm. As the proportion of soft surrounding rock increases, the lateral unbalanced load and torque of the cutterhead significantly increase. Meanwhile, as the proportion of soft surrounding rock increases, the corresponding rate of load increase significantly increases. Within the range where the proportion of soft surrounding rock increases from 21 % to 35 %, its lateral overturning vibration RMS value increases by 13.08 %. Within the range where the proportion of soft surrounding rock increases from 35 % to 50 %, its lateral overturning vibration RMS value increases by 32.18 %. This can easily cause safety accidents such as the fracture of key load-bearing components of the system during the excavation process.

To enhance sound absorption of Helmholtz resonance metamaterials in low frequency region with simple structure and engineering practicability, according to the well-established acoustic absorption theory of micro-perforated panel, a novel designed Helmholtz resonance metamaterial with extended microperforated neck is proposed, and a theoretical modelling method is developed by using the transfer matrix method which is validated by finite element simulation. Both theoretical calculation and finite element simulation results show that sound absorption performance of proposed Helmholtz resonance metamaterial is improved significantly compared to that of Helmholtz resonator with normal neck, and the resonant absorption coefficient is close to 1. The influence of geometric parameters of microperforated neck is also investigated in detail, and some meaningful conclusions are drawn. This work provides a perfect solution for low-frequency noise control with Helmholtz resonance metamaterials.

Industrial robots are increasingly used in machining due to their cost-effectiveness and larger work envelopes. However, their relatively low structural stiffness makes them vulnerable to machining chatter, which negatively impacts both process stability and surface quality. Accurate prediction of the multi-mode frequency response function (FRF) of robotic milling systems is crucial to ensure process stability. Traditional FRF prediction approaches, however, often require extensive experimental procedures, are complex, and are time-consuming. To address these challenges, this study proposes an innovative feature-transfer-based method for multi-mode FRF prediction in milling robots, requiring only a minimal set of impact tests. The method organizes measured FRFs into second-order complex tensors, facilitating the transfer of features between different postures. Multi-mode parameters of the tool-tip FRF under the source posture are extracted using the least-squares complex exponential (LSCE) method and assembled into a label vector. A complex-kernel extreme learning machine with augmented inputs (CKELM-AI) is then trained to predict the tool-tip FRF under the target posture. Additionally, a virtual sample generation strategy based on CKELM-AI and feature augmentation, including statistical, frequency, and time-frequency features, is applied to enhance prediction accuracy. Experimental validation on a milling robot demonstrates that the proposed method significantly improves both prediction efficiency and accuracy, establishing a new, more efficient approach for predicting multi-mode FRFs without the need for extensive testing.

Aiming at the problem that it is difficult to obtain a large amount of data for bearings with complex working conditions, which leads to the inability to accurately predict their life, a rolling bearing life prediction method based on few samples, LSGAN-Transformer, is proposed. A dropout layer is added to the LSGAN generator to avoid the overfitting phenomenon that often occurs during few-sample training. The normalization of each layer in the traditional Transformer model is moved forward to the input of the decoder and encoder submodules before the residual network, forming a direct gradient path from input to output, avoiding the problem of excessive expected gradient near the output layer that often occurs in the traditional Transformer network. Verification on the PHM2012 dataset and the XJTY-SY dataset shows that the MAE and RMSE of the proposed method are greatly improved; compared with other common prediction models, the MAE and RMSE of the proposed method are improved by 30.61 % and 35.93 % respectively.

The paper presents an integral procedure for conducting experimental measurements on excavation machines. Excavators have a complex structure with pronounced dynamic behavior. The identification of exploitation behavior is observed through experimental measurement of stress and acceleration, drive load, and vibrations. Electro-resistive measuring tapes were used to observe the steel structure, devices for measuring current, i.e. engaged power on the drives, as well as devices for measuring vibrations at characteristic points of the drive. The results obtained realistically reflect the condition and behavior of the structure and drive equipment. The goal is to introduce systematic research to monitor the condition and behavior of the equipment on the excavator. This approach forms the backbone of predictive observation, influencing the proper management of the excavator. Experimental measurements are performed to prove the correctness of the numerical model and to diagnose the condition and behavior of the structure and power units. By monitoring the condition and behavior of the equipment, we can optimally influence the process of maintenance of the equipment as well as the lifespan of the mining machine. This work includes the most important experimental measurements to carry out reconstructions, revitalizations, and modernizations on mining machines.

The shock response spectrum (SRS), calculated from a shock acceleration signal, is a critical indicator of shock environments. However, under intense loads, acceleration sensors are prone to trend term errors that can cause significant drift in the low-frequency spectral lines of large dual-wave shock tester devices. To address this issue, the complementary ensemble empirical mode decomposition (CEEMD) method was employed to decompose acceleration signals and restore the actual shock environment. Intrinsic mode functions (IMFs) were cross-correlated and compared to a predefined threshold to identify the effective IMF components required to reconstruct the signal. K-means clustering was employed to further validate the effectiveness of the IMFs for enhanced selection accuracy. Finally, the reconstructed acceleration signal was used to calculate a corrected SRS. The proposed approach demonstrated significant improvements over the traditional CEEMD algorithm. The corrected SRS exhibits a 5.6316 dB/oct slope in the low-frequency band, reflecting an equal displacement trend. The maximum error at the corresponding frequency was less than 6 % in comparison to the relative displacement response measured by low-frequency spring oscillators. This improved CEEMD correction method can effectively restore the actual shock environment of a dual-wave shock tester device, offering a valuable reference for evaluating shock resistance in onboard equipment.