Rolling bearing is the core component of industrial machines, but it is difficult for common single signal source-based fault diagnosis methods to ensure reliable results since sensor signals are vulnerable to the pollution of background noises and the attenuation of transmitted information. Recently, multi-source information-based fault diagnosis methods have become popular, but the information redundancy between multiple signals is a tough problem that will negatively impact the representational capacity of deep learning algorithms and the precision of fault diagnosis methods. Besides that, the characteristics of various signals are actually different, but this problem was usually omitted by researchers, and it has potential to further improve the diagnosing performance by adaptively adjusting the feature extraction process for every input signal source. Aimed at solving the above problems, a novel model for bearing fault diagnosis called multi-branch selective fusion deep residual network is proposed in this paper. The model adopts a multi-branch structure design to enable every input signal source to have a unique feature processing channel, avoiding the information of multiple signal sources blindly coupled by convolution kernels. And in each branch, different convolution kernel sizes are assigned according to the characteristics of every input signal, fully digging the precious fault components on respective information sources. Lastly, the dropout technique is used to randomly throw out some activated neurons, alleviating the redundancy and enhancing the quality of the multiscale features extracted from different signals. The proposed method was experimentally compared with other intelligent methods on two authoritative public bearing datasets, and the experimental results prove the feasibility and superiority of the proposed model.

The performance of a spindle will gradually decline, the vibration intensity will increase, and the temperature rise will become abnormal with the accumulation of service time. Consequently, the accuracy of the machining product will not meet its production requirements. Studies on the variation characteristics of the spindle unit have clarified the reasons for its abnormal vibration and temperature rise, in principle, to help enterprises conduct preventive maintenance before any serious failure occurs and improve production efficiency. Based on the Timoshenko beam model and rotor dynamics theory, this study uses the finite element method (FEM) to analyze the vibration characteristics of the spindle rotor system. Moreover, it thoroughly analyzes the influence of the spindle bearing heat on the stiffness of the spindle system and identifies the resonance conditions of the spindle rotor within the power frequency range. This research has identified that when the vibration frequency of the spindle operates at a speed of 12,000 r/min, if its vibration frequency is lower than 200 Hz, it will be affected by abnormal vibrations during startup, which will weaken the health status of the spindle and reduce its service life. Similarly, when the working speed of the spindle is 30,000 r/min, if its vibration frequency is lower than 50 Hz, abnormal vibration will occur during startup and operation, thereby reducing its service life.

The reliability of mechanical systems hinges on analyzing the actual surface-to-surface contact area, which critically influences dynamic behavior, friction, material performance, and thermal dissipation. Uneven surfaces lead to incomplete contact, where only a fraction of asperities touch, creating a nominal contact area. This study proposes a novel fractal contact model for various mechanical behaviors between mechanical contact surfaces, integrating the Weierstrass–Mandelbrot fractal function and nonuniform rational B-spline interpolation (NURBS) to model material-dependent actual contact conditions. Furthermore, this research delved into the changes in thermal conductivity across the surfaces of metal materials within a simulated setting. It maintained a contact ratio ranging from 0.038% to 15.2%, a factor that remained unaffected by contact pressure. Both experimental and simulated findings unveiled an actual contact rate spanning from 0.44% to 1.06%, thereby underscoring the distinctive interface behaviors specific to different materials. The proposed approach provides fresh perspectives for investigating material–contact interactions and tackling associated engineering hurdles.