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.

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.