Research And Development Of EAM System Based On Equipment Monitor And Maintenance Strategy

In recent years, the rapid growth of cargo handling capacity in Chinese port has made China one of the most important port transportation countries. The port as technology intensive enterprises, the modernization level of equipment and the appropriate assets management mode has become the major effect in the port functions and business scope. So far, there exist many realistic problems that need to be solve: weak management planning, large number of equipment which leads to the difficult monitoring, the inefficient management mode etc. Therefore, in order to realize the fast development of the port enterprise, this dissertation conducts research on the Enterprise Asset Management system(EAM), which combines the purchase management, inventory management into a sharing of information system, with the goal of improving the maintenance efficiency, decreasing the operational and maintenance cost.The dissertation mainly aiming at the EAM system of port enterprise, the detailed contents are as follows:1. Aiming at the dilemmas lies in equipment management, this dissertation developed an intelligent enterprise asset management(EAM) systems, Firstly,it introduced the EAM system’s research background and significance,analyzed research status at home and abroad presently,and discussed the related theory and development technology of EAM sysem.Then,according to the present actual equipment management situation of port enterprise,through in-depth analysis of the demand research, based on the detailed need analysis,designed the system.2. The development method and the key technology of the monitoring subsystem are studied in this paper,. Aiming at the uncertainty and diversity of the EAM data, some work is conducted on the data fusion problem, according to the comparison analysis of the commonly used algorithm, such as Kalman filtering, bayes estimation, statistical decision theory, evidence reasoning, fuzzy reasoning etc. together with the characteristic of the device monitoring application, the weighted average algorithm is designed as the appropriate fusion method for EAM system2. The development method and key technology of equipment management subsystem are studied in this paper. As for the maintenance subsystem in the EAM system, in order to guarantee the safe and reliable operation of equipment, an improved KNN strategy is adopted to conduct the fault prediction, according to the history fault record collected from the EAM, use the KNN strategy to get the fault variation tendency, moreover genetic algorithm is utilized to optimize the feature weight to improve the accuracy of the prediction results.