Suspension gaps identification based on cluster analysis

Too large suspension gaps will affect the service performance of the vehicle seriously. Therefore it is necessary to identify the suspension gap accurately. The traditional suspension gaps identification technology works in harsh environment and the detecting process not only can not achieve automation but also with lower identification precision. The flexible four-link mechanical model of wheel assembly and suspension gap detecting mechanical model were established by mechanical analyzing and simplifying the traveling agencies. Besides, the suspension gap identification mathematical model based on Fisher ordered cluster analysis was also established. We can cognize the existence of the suspension gap. Therefore the ordered samples optimal partition method was selected to solve the clustering problem of the driving force. By dividing into the optimal subsection number, finding the optimal subsection interval points, and calculating the displacement value according to the pointer, then the suspension gap can be identified and extracted accurately. Some vehicles such as JettaGix and BJ2020SA were selected for suspension gap identification testing. The testing results showed that the identification error was within 5% which not only satisfied the error request but also better than that of other identification methods, that proved the feasibility of this method.