Yue Si

To establish a high-fidelity dynamic model for a dual U-tube Coriolis mass flowmeter (CMF), this paper employs Timoshenko beam theory and the finite element method (FEM) to discretize the complete sensor assembly, including exciters, pick-offs, sensor added masses, and brace bars. MATLAB numerical simulations were conducted to obtain the first ten natural frequencies and the frequency response function (FRF) under simulated impact, which were rigorously compared with experimental data. Given the typical CMF operating range of 80–180 Hz, this study focuses on low-order modes below 200 Hz. The results indicate that the simulation accurately reproduces both the experimental operating mode (97.25 Hz) and the higher-order torsional mode (196.8 Hz) with an error within 3%. This research provides a solid theoretical and experimental foundation for structural optimization, fault diagnosis, and high-precision signal processing of CMFs.

Tool wear state identification under complex cutting conditions remains a challenging task regarding accuracy and robustness. Existing deep learning approaches often fail to fully exploit the complementary information between time-frequency images and one-dimensional statistical indicators. Furthermore, simple feature concatenation strategies are insufficient for achieving deep cross-modal fusion. In response to these challenges, this study presents, this paper proposes a novel multimodal monitoring framework named the Statistical Aware Transformer (SAT). Crucially, a statistical adaptive normalization mechanism is introduced. This module utilizes hand-crafted physical statistical features as conditional vectors to dynamically generate affine transformation parameters (scale and shift), thereby modulating the high-level feature extraction process of time-frequency images. By integrating statistical priors, this mechanism enables the model to adaptively attend to frequency bands critical for wear progression. Validated on the PHM2010 milling dataset, the proposed method demonstrates superior identification accuracy compared to traditional Convolutional Neural Networks (CNNs) and standard Vision Transformers (ViT) in cross-condition testing, confirming the efficacy of SAT in facilitating effective multimodal feature fusion.

With the increasing complexity of industrial machinery and the growing demand for intelligent fault diagnosis, accurately identifying rolling bearing faults under conditions of data scarcity and varying operating conditions has become a major challenge. In real-world applications, the scarcity of fault samples and the diversity of working environments often lead to sample imbalance and distribution shifts, which severely compromise the performance of conventional diagnostic models. To address these challenges, a novel multi-scale adaptive transformer (MAT) model is proposed for fault diagnosis of rolling bearings under imbalanced small-sample and cross-condition scenarios. The model integrates a multi-scale feature enhancement backbone with the global modeling capability of a hierarchical encoder, enabling the simultaneous extraction of fine-grained local features and long-range contextual fault representations. Specifically, the feature enhancement backbone incorporates dilated convolutions, spatial pyramid pooling, and a spatial attention mechanism to extract rich contextual information through expanded receptive fields, fuse multi-scale spatial features, and adaptively focus on fault-relevant regions to suppress noise. This design effectively improves the representational capacity and robustness of the model under data-limited conditions. In the hierarchical encoder, a channel attention residual sublayer is introduced to adaptively reweight feature dimensions, thereby increasing the sensitivity of the model to critical local features and improving the resistance of the model to overfitting. Extensive experiments conducted on the Western Reserve University and Paderborn bearing datasets demonstrate that the proposed MAT model significantly outperforms existing mainstream methods in both cross-condition and imbalanced small-sample fault diagnosis tasks. These results fully validate the effectiveness and generalization capability of the proposed approach in practical intelligent manufacturing applications.

Coriolis flow meters are widely used in the petroleum, chemical, and pharmaceutical industries. To ensure safety and efficiency during production, it is necessary to ensure the measurement accuracy of the flow meter. The dynamic imbalance of a flow meter is a key factor that affects the measurement accuracy of the flow meter. Therefore, a method for evaluating the unbalance state status of flow meters is proposed. This study aimed to quickly and accurately identify the dynamic unbalanced state of a flow meter. First, the frequency response function of the original signal is calculated to reduce the error caused by the influence of excitation. Second, the method of combining empirical wavelet and Laplacian sparse decomposition is used to extract the characteristic information of each order modal. Finally, through experiments, it is found that the second-order and third-order modal characteristic details are sensitive to the degree and location of the flow meter dynamic unbalance fault. Therefore, second-order and third-order modal characteristic information was selected to calculate two characteristic indicators that reflect the degree and location of the flow meter dynamic unbalance fault to achieve the effect of fault diagnosis. Experiments and simulations show that this method is more accurate and efficient than the other methods.

Rubber sandwich structures (RSSs) are used extensively in mechanical engineering. The aging state detection of such structures is urgently required to avoid disastrous accidents. However, this is still a challenging task owing to the weakness of the rubber layer aging feature information contained in the vibration signal of the RSS and the lack of effective aging feature information extraction techniques. Thus, an aging state detection method for the RSS using synchrosqueezing transform (SST)-assisted feature extraction and a customized detection indicator was proposed in this study. First, the SST was used to decompose the vibration signal of the RSS, and a time-frequency (TF) spectrum with an enhanced aging state feature was obtained. Second, a TF bandpass filter was constructed and used to filter the information unrelated to the aging state feature from the TF spectrum. Subsequently, a hard threshold denoising method was applied to reduce noise in the filtered TF spectrum. Then, the aging state signal was reconstructed using the inverse SST. Finally, a customized detection indicator was constructed and its value was calculated to detect the aging state of the RSS. A thermo-oxidative aging dataset of the RSS from Xi’an Jiaotong University was used to validate the proposed method. The experimental results showed that the proposed method was more effective than other methods.

At present, the detection accuracy of bolt-loosening diagnoses is still not high. In order to improve the detection accuracy, this paper proposes a fault diagnosis model based on the TSCNN model, which can simultaneously extract fault features from vibration signals and time-frequency images and can precisely detect the bolt-loosening states. In this paper, the LeNet-5 network is improved by adjusting the size and number of the convolution kernels, introducing the dropout operation, and building a two-dimensional convolutional neural network (2DCNN) model. Combining the advantages of a one-dimensional convolutional neural network (1DCNN) with wide first-layer kernels to suppress high-frequency noise, a two-stream convolutional neural network (TSCNN) is proposed based on 1D and 2D input data. The proposed model uses raw vibration signals and time-frequency images as input and automatically extracts sensitive features and representative information. Finally, the effectiveness and superiority of the proposed approach are verified by practical experiments that are carried out on a machine tool guideway. The experimental results show that the proposed approach can effectively achieve end-to-end bolt-loosening fault diagnoses, with an average recognition accuracy of 99.58%. In addition, the method can easily achieve over 93% accuracy when the SNR is over 0 dB without any denoising preprocessing. The results show that the proposed approach not only achieves high classification accuracy but also has good noise immunity.

In this paper, a quasi-static angular contact ball bearing model, considering assembly accuracy is constructed, while a numerical solution method for bearing stiffness under bad assembly state is established. A 7014C angular contact ball bearing is used as the research object and five groups of different spacer inclinations are designed to imitate the installation error of the spindle bearing. The bearing stiffness performance was comparatively analyzed, according to the five spacers. The effect of preload and rotation speed on bearing stiffness are systematically investigated, considering different parallelism errors, as induced by the spacers. The influence mechanism of the badly assembled bearing on the respective stiffness anisotropy is studied based on the proposed model. The results show that the variations of the inclination between the inner and outer rings of the bearing exhibit a very weak effect on the axial stiffness, while the influence on the radial and angular stiffness is more significant.

It is significant to perform looseness condition detection of viscoelastic sandwich structures to avoid serious accidents. Due to the multilayer characteristic of the viscoelastic sandwich structure, the vibration response signal of such structures is nonlinear and nonstationary. Furthermore, the looseness condition feature signal contained in the vibration response signal is very puny. Condition feature extraction has become a challenging task in the looseness condition detection of viscoelastic sandwich structures. Therefore, a novel method called dual-tree complex wavelet packet-based deep autoencoder network is proposed for this task. First, the vibration response signal of the viscoelastic sandwich structure is decomposed by dual-tree complex wavelet packet transform and the sub-band signals which contain rich energy are extracted. Then, the energies of the extracted sub-band signals are calculated to form a feature set. Finally, a deep autoencoder network is established to fuse the feature set, and the fused feature is viewed as the detection index to detect the looseness condition of the viscoelastic sandwich structure. The proposed method is applied to the connecting bolt looseness condition detection of the viscoelastic sandwich structure to validate its effectiveness. Compared with the detection method based on dual-tree complex wavelet packet transform and energy and the detection method based on dual-tree complex wavelet packet transform and permutation entropy, the results indicate that the effectiveness of the proposed method in this article is more superior to that of the other two methods.