Study On Image Analysis And Recognition Of Ferrograph For Boarding Bridge System

The boarding bridge is connected to the airport terminal and aircraft traffic corridor. It is the special electrical, hydraulic, computer-control equipment in the airport. Because of its high-efficiency and economy, ferrographic analysis technique has already became one of the most effective solutions for machine status monitoring and fault diagnosis.It has been a very good application in many fields of fault diagnosis of mechanical equipment. However,as the traditional ferrographic analysis technique could not meet the actual requirement due to shortages such as subjectivity, low precision and time consuming, It has not been able to satisfy the need of actual production, and seriously affect the popularization and use. Therefore, the recognition technique for wear debris has become one of the important directions of ferrographic technique development and application. In recent years many researchers, whether in overseas or domestic, have paid more attention on it.In this paper, we will use ferrographic analysis technique to analyse and diagnose the fault of boarding bridge system.According to the relevant literatures, the development and status of wear particle analysis technique at home and abroad are evaluated synthetically. Wear mechanism and classification including wear particle classification and characters are analyzed and discussed.This paper selects color wear particle image of the boarding bridge system after ferrographic analysis, and use Matlab 2012 as software as a platform for image processing tools.In order to achieve the goal of ferrography technique intelligentization, the current techniques for wear particle segmentation and feature extraction have been given more in-depth study.By using computer image processing technique,the ferrography image smoothing,filtering and morphology processing,it successfully reduces the noise and separate wear particle from background. By analyzing and calculating the shape,size and color characteristic parameters of wear particles, a complete set of wear particle quantification characterization comes into being.In this paper, we will combine equipment fault diagnosis, the research and the method in this paper can be used for other machines and equipments. It is of great development and wide application of the intelligent oil-monitoring technique.