The purpose of shock absorbers are to dissipate impact energy, and control tire force variation, the shock absorber has great influence on both ride and handling performance of vehicles, and a great many previous researches have been done on modeling and simulation of the shock absorber. In this paper, a detailed model of shock absorber is established, which contains rebound chamber, compression chamber, piston valve assembly, base valve assembly and so on. Those models are built using modelica language, modelica is a language for modeling of physical systems, designed to support effective library development and model exchange. It is a modern language built on a causal modeling with mathematical equations and object-oriented constructs to facilitate reuse of modeling knowledge.
The performance of door assembly is very significant for the vehicle design. In the present paper, the finite element method is used in the development processes of the door assembly. The stiffness, strength, modal characteristic and anti-extrusion of a newly developed passenger vehicle door assembly are calculated and evaluated by several finite element analysis commercial software. The structural problems discovered by FE analysis have been modified and finally achieved the expected door structure performance target of this new vehicle. The issue in focus is to predict the performance of the door assembly by powerful finite element analysis software, and optimize the structure to meet the design targets. It is observed that this method can be used to forecast the performance of vehicle door efficiently when it's designed.
Data measurement of roller bearings condition monitoring is carried out based on the Shannon sampling theorem, resulting in massive amounts of redundant information, which will lead to a big-data problem increasing the difficulty of roller bearing fault diagnosis. To overcome the aforementioned shortcoming, a two-stage compressed fault detection strategy is proposed in this study. First, a sliding window is utilized to divide the original signals into several segments and a selected symptom parameter is employed to represent each segment, through which a symptom parameter wave can be obtained and the raw vibration signals are compressed to a certain level with the faulty information remaining. Second, a fault detection scheme based on the compressed sensing is applied to extract the fault features, which can compress the symptom parameter wave thoroughly with a random matrix called the measurement matrix. The experimental results validate the effectiveness of the proposed method and the comparison of the three selected symptom parameters is also presented in this paper.
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