Airport multi-platform multi-device complex system health management method

Abstract

The invention relates to an airport multi-platform multi-device complex system health management method, which combines nominal values of components for physical modeling, can simply perform physicalmodeling on different components, and can estimate the remaining life of the components in real time according to the change of the environment. Meanwhile, a data modeling mode is used for an automatic switch type device, data models of pull-in time and time of different types of components under different working conditions can be established, accurate service life prediction is carried out, important reference is provided for predictive diagnosis, and the failure rate of an airport electrical system is effectively reduced.

Description

technical field [0001] The present invention relates to a technology in the field of airport equipment control, specifically a health management method for multi-platform and multi-equipment complex systems in an airport, which can accurately predict the aging rate and remaining life of each component of the airport electrical system, and achieve the goal of eliminating hidden troubles in advance Fault warning effect. Background technique [0002] With the rapid increase in the number of domestic civil aviation passenger aircraft, and the increasingly complex services and structures such as airport air traffic control, higher and higher requirements are placed on the reliability of the airport, and the corresponding maintenance tasks and maintenance costs are also increasing. obviously increase. While enjoying the convenience of the new mode of transportation, civil aviation airliners, people also pay attention to the service quality of air transportation. With the intelli...

AI Technical Summary

Problems solved by technology

This method has many disadvantages: after a fault occurs, it takes time to diagnose, locate the fault point, and repair after finding the cause of the fault. It takes a long time, and a lot of manpower is spent on maintenance preparation and rush repair, and the maintenance personnel have heavy tasks.

However, predictive maintenance is faced with problems such as difficulty in component modeling and sparse data. It is necessary to establish a set of different mathematical models for different components. The establishment of the model requires a lot of energy and time, and it is difficult to engineer.

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