Development And Application Of The Cloud Service System For Aeroengine Prognostics And Health Management

With the merger and acquisition(M&A) of airlines and the formation of large airline groups, resource integration has become the key to improve the management level of the airline groups. Now the airline groups and its subsidiaries geographically dispersed, resource sharing is not sufficient. In order to improve the level of Engine Health Management(EHM) airlines have invested a lot of money to purchase the relevant hardware and software. How to use cloud computing technology to break the area-isolation, realize the resource sharing sufficiently, reduce repeat investment, has become the key to improve the comprehensive utilization of the airline’s resources and enhance the core competitiveness of the industry. In order to support the integration and sharing of software and hardware resources among various subsidiaries, this paper makes a research on the related algorithms and systems for the construction of the aviation EHM service platform based on cloud computing.Firstly, this paper starts from the airline’s requirement of cloud computing technology, the overall scheme of aero engine cloud service system is defined, the framework of aero engine cloud service system is designed and the function model and information model of cloud service system are described.Secondly, the deployment and dynamic migration of virtual machine are studied from the point of optimal energy consumption. The virtual machine is described and the energy consumption structure of the cloud data center is studied, compare the number of running physical machines and energy consumption in different virtual machine deployment algorithms as well as the number of virtual machine migration and energy consumption in different virtual machine migration algorithm. Finally determine the BT-MPA and TT algorithm as the cloud service system in the virtual machine deployment and dynamic migration strategy.Thirdly, the resource scheduling problem in cloud service system are studied from the point of load balancing. We take the average resource utilization and load balancing as the scheduling objectives of the system. Average utilization rate of all kinds of resources and system load balance are compared to determine the PSO-ACO(Particle Swarm optimization and Ant Colony Optimization) algorithm as the resource scheduling algorithm of cloud service system.Finally, completed the development of aeroengine health management cloud service platform and taking the existing aviation engine washing system of the research group as an example to describe the process of the system resource access cloud service platform and introduce how to order and call the system resources in the aviation engine cloud service system. Based on this, the correlation algorithm, the cloud service platform and the final construction of aero engine health management system are verified.