An improved mathematical model used to study the coupling characteristic of the multi-span transmission lines is developed. Based on the solution method for single-span cable, an expression for the dynamic stiffness of two-span transmission lines with an arbitrary inclination angle is formulated. The continuity of displacements and forces at a suspension point is used to derive the dynamic stiffness. Interactions between insulator strings and adjacent spans are accommodated. Considering the infinite dynamic stiffness corresponds to the natural frequencies of the transmission lines, the finite-element method (FEM) is employed to assess the validity of the dynamic stiffness. In the numerical investigation, attention is focused on the effect of the inclination angles, Irvine parameter, insulator string length and damping parameter. In addition, the modal function corresponding to the natural frequencies is derived. Then, the results of comprehensive parametric studies are presented and discussed. Special attention is paid to the effect of the Irvine parameter and damping parameter on the in-plane modal shapes. Finally, according to the theoretical model of two-span transmission lines, the generalized dynamic stiffness of transmission lines with an arbitrary number of spans and inclination angles is derived. The method can be used as the basis of the vibration analysis on a wide variety of multi-span transmission lines.

In this paper, a novel similarity classifier which synthesizes the adaptive resonance theory (ART) and the similarity classifier based on the Yu’s norm is proposed. The proposed ART-similarity classifier can not only carry out training without forgetting previously trained patterns but also be adaptive to changes in the environment. In order to test the proposed classifier, it is applied to the fault diagnosis of rolling element bearings. Before application to the fault diagnosis of bearings, considering computation burden principal component analysis (PCA) is proposed to reduce the number of features. The PCs are input the proposed classifier to diagnose the faulty bearings. The experiment results testify that the proposed classifier can identify the faults accurately. Furthermore, in order to validate the effectiveness of the proposed classifier further, it compares with other neural networks, such as the fuzzy ART, self-organising feature maps (SOFMs) and radial basis function (RBF) neural network through diagnosing the bearings under the same conditions. The comparison results confirm the superiority of the proposed method.

In machine dynamics the modal analysis of tool holder-spindle assembly is carried out to verify the reasonability of spindle speed, recognize the impaction of vibration modes on machining accuracy and optimize the design of spindle. This paper presents modeling and modal analysis of tool holder-spindle assembly utilizing FEA. Bearing supports is simulated by four uniformly distributed translational spring-damper elements and the radial stiffness of bearings is calculated based on Hertz contact theory. The connection at the tool holder-spindle interface is assumed to be the rigid-connection. This study also proposes two numerical methods in the finite element analysis software ANSYS to simulate the rigid-connection. Consequently, the present modeling and analysis approach by use of FEA is feasible for analyzing tool holder-spindle assembly dynamics.

Process of high-speed trains manufacturing is a complicated task requiring strong professional background and practical experience. However, in China, the production mode is staying at the step that explore in actual production constantly, continue to discover and solve problems, low productivity but high manufacturing costs. In order to try to solve above problems, applications of virtual simulation in manufacturing process of high-speed trains is applied for the goals. According to the workshop’s two-dimensional (2D) designing, a digital factory is constructed using simulation software based on production manufacturing process of high-speed trains. Then production model is disaggregated analysis. When virtual simulation environment is accomplished, an assembly process turns to be run. An example of plug-door assembly process is presented to illustrate the effect and feasibility of the application of virtual simulation.

During machining some ultra-intense and special-shaped parts, unstability and surface ablation often turn up. Some large and heavy parts are often machined with heavy duty NC machine tool. For avoiding the processing defects of the above parts, an online monitoring, diagnosis and control system is designed for heavy duty NC machine tool. The system can online monitor the operation condition of heavy duty NC machine tool and real-time control the processing. Some methods of signal analysis and processing are adopted such as spectrum analysis, wavelet analysis, Wigner-Ville distribution and Artificial Neural Network etc. The system is developed with C language and Qt based on Linux operation system. Optical fiber communication is adopted between industrial computer and NC system. Experiment platform of the system is machining an aircraft landing gear with five-axis NC machine tool. The scheme is verified feasible after preliminary test.