To accurately predict the remaining useful life (RUL) of rolling bearings under limited data and fluctuating load conditions, we propose a new method for constructing health indicators (HI) and a transfer learning prediction framework, which integrates Convolutional Neural Networks (CNN), Gated Recurrent Units (GRU), and Multi-head attention (MHA). Firstly, we combined Convolutional Neural Networks (CNN) with Gated Recurrent Units (GRU) to fully extract temporal and spatial features from vibration signals. Then, the Multi-head attention mechanism (MHA) was added for weighted processing to improve the expression ability of the model. Finally, a new method for constructing Health indicators (HIs) was proposed in which the noise reduction and normalized vibration signals were taken as a HI, the L1 regularization method was added to avoid overfitting, and the model-based transfer learning method was used to realize the RUL prediction of bearings under small samples and variable load conditions. Experiments were conducted using the PHM2012 dataset from the FEMTO-ST research institute and XJTU-SY dataset. Three sets of 12 migration experiments were conducted under three different operating conditions on the PHM2012 dataset. The results show that the average RMSE of the proposed method was 0.0443, indicating high prediction accuracy under variable loads and small sample conditions. Three different operating conditions and two sets of four migration experiments were conducted on the XJTU-SY dataset, and the results show that the average RMSE of the proposed method was 0.0693, verifying the good generalization of the model under variable load conditions. In summary, the proposed HI construction method and prediction framework can effectively reduce the differences between features, with high stability and good generalizability.

Fault diagnosis methods for rolling bearings are frequently constrained to the automatic extraction of single-scale features from raw vibration signals, overlooking crucial information embedded in data of other scales, which often results in unsatisfactory diagnostic outcomes. To address this, a lightweight neural network model is proposed, which incorporates an improved Inception module for multi-scale convolutional feature fusion. Initially, this model generates time–frequency maps via continuous wavelet transform. Subsequently, it integrates the Fused-conv and Mbconv modules from the EfficientNet V2 architecture with the Inception module to conduct multi-scale convolution on input features, thereby comprehensively capturing fault information of the bearing. Additionally, it substitutes traditional convolution with depthwise separable convolution to minimize training parameters and introduces an attention mechanism to emphasize significant features while diminishing less relevant ones, thereby enhancing the accuracy of bearing fault diagnosis. Experimental findings indicate that the proposed fault diagnosis model achieves an accuracy of 100% under single-load conditions and 96.2% under variable-load conditions, demonstrating its applicability across diverse data sets and robust generalization capabilities.

Elastic composite cylindrical roller bearing is a novel type of roller bearing whose rolling element is designed by innovating the rolling element structure of cylindrical roller bearings. For the purpose of investigating the dynamic properties of the rolling elements with different structural parameters and solving the modes of elastic composite cylindrical rolling element with different filling degrees, first, this study compares elastic composite cylindrical rolling element to solid cylindrical rolling element, in terms of natural frequency and vibration mode. Next, the integration time step is determined, based on the natural frequency of the vibration in the Y direction, the response of various rolling element, under impact loads, is solved. Furthermore, the laws of the energy changes and nodal displacement variations of rolling element are explored, at different filling degrees. Finally, adopting the decay method, the damping ratio of elastic composite cylindrical rolling element, under different structural parameters and external loads, is calculated. According to the results, with the increase of filling degree, the natural frequencies of various orders of elastic composite cylindrical rolling element gradually declined. The damping ratio of rolling elements decreased, as the filling degree increased, while it could be changed by adjusting the structural dimensions of rolling elements. The analysis results of this study provide some theoretical reference for studies on the parameter optimization of rolling element structures, the vibration and noise reduction of elastic composite cylindrical roller bearings.

The selection of grinding process parameters is of paramount importance for enhancing the productivity of high-speed grinding. To augment the material removal rate in the high-speed grinding of non-circular components, an optimization strategy for grinding process parameters, aimed at achieving stable grinding, has been proposed. Initially, a dynamic model of the high-speed grinding process system for non-circular profiles was established, integrating the geometric kinematic characteristics of non-circular grinding with the time-delay regenerative effect, and its grinding stability was analyzed. Subsequently, a multi-dimensional nonlinear mapping relationship between grinding process parameters and the system’s chatter behavior as well as surface quality was constructed. The machining stability and surface quality of the workpiece were taken into consideration to propose an optimization strategy for grinding process parameters oriented toward stable grinding. With the chatter stability boundary and surface roughness as constraints, and the maximum material removal rate as the objective, the spindle speed and grinding depth were optimized. This methodology was implemented in the context of grinding an automotive camshaft component, culminating in the attainment of an optimal grinding depth and spindle speed. This enhancement in machining efficiency was found to be approximately 100%.

Failure caused by loosening of bolted connections is a common problem in practice. To reasonably optimize the factors influencing the behavior of bolted connections, this paper proposed a method for analyzing the correlation grades of...