As one of the important parameters of bearing operation, temperature is a key metric to diagnose the state of service of a bearing. However, there are still some shortcomings in the study of the temperature variation law for damaged bearings. In this paper, according to the structural characteristics of bearings, the influence law of thermal-solid coupling effect on bearing structure is considered, and a novel transient temperature analysis model of damaged bearings is established. First, a quasi-static analysis of the bearing is performed to obtain the variation laws of the key parameters of the bearing under thermal expansion. Then, the load variation law of the bearing under the condition of damage is discussed, and the heat generation and heat transfer of the damaged bearing during operation are studied. Based on the thermal grid method, a transient temperature analysis model of the damaged bearing is developed. Finally, the model is tested experimentally and the influence of the rotate speed and load on the bearing temperature variation is analyzed. The results show that the established model can effectively predict the temperature variation and thermal equilibrium temperature of damaged bearings.

Tool wear prediction is of great significance in industrial production. Current tool wear prediction methods mainly rely on the indirect estimation of machine learning, which focuses more on estimating the current tool wear state and lacks effective quantification of random uncertainty factors. To overcome these shortcomings, this paper proposes a novel method for predicting cutting tool wear. In the offline phase, the multiple degradation features were modeled using the Brownian motion stochastic process and a SVR model was trained for mapping the features and the tool wear values. In the online phase, the Bayesian inference was used to update the random parameters of the feature degradation model, and the future trend of the features was estimated using simulation samples. The estimation results were input into the SVR model to achieve in-advance prediction of the cutting tool wear in the form of distribution densities. An experimental tool wear dataset was used to verify the effectiveness of the proposed method. The results demonstrate that the method shows superiority in prediction accuracy and stability.

In the assembly process of aerospace parts, drilling is essential for carbon fiber-reinforced materials. However, due to the extreme thermal sensitivity of these composites, continuous drilling often leads to irreparable defects such as hole wall burns and exit delamination caused by concentrated cutting heat, resulting in the scrapping of parts. To address this issue, this paper explores the impact of temperature characteristics on drilling quality, providing guidance for optimizing the composite drilling process. A simulation model for single and continuous drilling was established to analyze the temperature distribution on the tool surface during drilling. A drilling temperature measurement system based on thin-film thermocouple technology was developed, enabling real-time online temperature monitoring. Continuous drilling experiments were conducted, analyzing the correlation between maximum drilling temperature and hole quality. Results show that temperatures from −25.75 °C to −9.75 °C and from 182 °C to 200.75 °C cause significant exit damage, while optimal hole quality is achieved between −1.25 °C and 168 °C.

This paper addresses the issue of fault diagnosis in high-speed train bogie bearings under complex working conditions and proposes a method for calculating the characteristic frequency of rolling bearings that takes into account the influence of radial clearance. By establishing a five-degree-of-freedom nonlinear dynamic model, this study systematically analyzes the modulation mechanism of radial clearance on the fault characteristic frequency of bearings and verifies the findings through an experimental platform. The results indicate that an increase in clearance not only leads to significant attenuation of the fault characteristic frequency amplitude, but also induces sideband modulation effects, thereby interfering with fault diagnosis accuracy. The experimental data show good agreement with the theoretical calculations, verifying the effectiveness of the proposed method. Specifically, the nonlinear stiffness-based characteristic frequency calculation reduces the prediction error from 6.9–5.7% under traditional theory to 2.3–3.4% across a wide range of rotational speeds. Meanwhile, the clearance-induced amplitude attenuation predicted by the model is also experimentally confirmed, with measured amplitude reductions of 35–42% as clearance increases from 0.2 μm to 0.5 μm. These results not only demonstrate the accuracy and engineering applicability of the method but also provide new theoretical foundations and practical references for health monitoring and early fault diagnosis of high-speed train bearings.