Bearing is an important mechanical component that easily fails in a bad working environment. Support vector machines can be used to diagnose bearing faults; however, the recognition ability of the model is greatly affected by the kernel function and its parameters. Unfortunately, optimal parameters are difficult to select. To address these limitations, an escape mechanism and adaptive convergence conditions were introduced to the ALO algorithm. As a result, the EALO method was proposed and has been applied to the more accurate selection of SVM model parameters. To assess the model, the vibration acceleration signals of normal, inner ring fault, outer ring fault, and ball fault bearings were collected at different rotation speeds (1500 r/min, 1800 r/min, 2100 r/min, and 2400 r/min). The vibration signals were decomposed using the variational mode decomposition (VMD) method. The features were extracted through the kernel function to fuse the energy value of each VMD component. In these experiments, the two most important parameters for the support vector machine—the Gaussian kernel parameter σ and the penalty factor C—were optimized using the EALO algorithm, ALO algorithm, genetic algorithm (GA), and particle swarm optimization (PSO) algorithm. The performance of these four methods to optimize the two parameters was then compared and analyzed, with the EALO method having the best performance. The recognition rates for bearing faults under different tested rotation speeds were improved when the SVM model parameters optimized by the EALO were used.
Misalignment fault is the main factor that affects the normal running of dual-rotor system. Quantitative identification the misalignment fault is an important way to ensure the safe and stable service of the dual-rotor system, while the identification accuracy of traditional methods is low. Aiming at the above problems, this paper proposed a dual-rotor misalignment fault quantitative identification method based on DBN and D-S evidence theory improved by mutual information measure (MIMD-S). Seven groups experiments were conducted and several vibration signals were collected. By comparing it with the traditional methods D-S, and Pignistic improved D-S (PD-S) evidence theory, the results show that the method proposed in this paper improves the accuracy of the misalignment fault quantitative identification of the dual-rotor, the identification error rate was only 0.36%.
Double-row tapered roller bearings have been widely used in various equipment recently due to their compact structure and ability to withstand large loads. The dynamic stiffness is composed of contact stiffness, oil film stiffness and support stiffness, and the contact stiffness has the most significant influence on the dynamic performance of the bearing. There are few studies on the contact stiffness of double-row tapered roller bearings. Firstly, the contact mechanics calculation model of double-row tapered roller bearing under composite loads has been established. On this basis, the influence of load distribution of double-row tapered roller bearing is analyzed, and the calculation model of contact stiffness of double-row tapered roller bearing is obtained according to the relationship between overall stiffness and local stiffness of bearing. Based on the established stiffness model, the influence of different working conditions on the contact stiffness of the bearing is simulated and analyzed, and the effects of radial load, axial load, bending moment load, speed, preload, and deflection angle on the contact stiffness of double row tapered roller bearings have been revealed. Finally, by comparing the results with Adams simulation results, the error is within 8%, which verifies the validity and accuracy of the proposed model and method. The research content of this paper provides theoretical support for the design of double-row tapered roller bearings and the identification of bearing performance parameters under complex loads.
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