Research On The Technology Of Prediction And Diagnosis Of Equipment Fault In Satellite Ground Station System

As the satellite ground station gradually develops towards the direction of automatic transmission and reception,integration of detection,large-scale and complex,this requires the satellite ground station system equipment to have higher availability within the working safety range.However,the traditional satellite ground station detection technology has fallen behind the development progress of the times and cannot meet these needs.In response to these problems,domestic and foreign scientific researchers apply the knowledge of statistics and machine learning to the detection of satellite ground station system equipment.However,the current domestic research in this area is still in the exploratory stage,mostly concentrated in academic theories,and there are relatively few engineering applications.For this reason,it is necessary to conduct in-depth research and active exploration of related technologies for satellite ground station system equipment failure detection.First of all,this article briefly analyzes the composition of the entire satellite ground station system,expounding the working principle,function and performance of each device in the entire system.Secondly,it explores the technical aspects of failure prediction.Since the satellite ground station system equipment has no special fault characteristics in the early stage of operation,how to efficiently identify the fault and evaluate the health status of the current working equipment is one of the key contents of this research.In response to this problem,this paper adopts a fault prediction method based on hidden Markov model and nearest neighbor propagation clustering algorithm to divide the entire system state into healthy,sub-healthy and faulty states,which can describe the state of the entire system truly and effectively.The final experimental simulation shows that the fault prediction method studied in this paper can classify the state of the system equipment failure in the early stage,and can also understand the working state of the equipment in time to facilitate detection and replacement.Thirdly,it explores the research of fault diagnosis technology.For the satellite ground station system equipment during operation,the communication signal has the characteristics of non-linearity and large amount of information processing calculation.In this paper,the nuclear principal component analysis algorithm is used to extract the main influence state characteristic values from the collected data.For the typical fault characteristics of the satellite ground station system equipment during operation,and the problem of determining and optimizing the parameters of the least square support vector machine in the fault detection and recognition,the particle swarm algorithm is used to solve it.The final experimental simulation shows that the fault diagnosis method studied in this paper not only helps to improve the effective diagnostic rate of the entire system,but also locates the fault more quickly,finds and solves the problem.Finally,the software design and implementation of the satellite ground station system.Based on the above research content,complete the design and implementation of an automated detection system integrating fault prediction and fault diagnosis.