Research on Online Monitoring and Prediction Technology of Signal Processing Devices in Small Array Systems

Abstract

This article focuses on the problem of fault monitoring and diagnosis in small array systems. We analyzed the composition of the small array system and BIT settings, and proposed a modeling sensitive parameter set for system health management. Based on the fault sensitive parameter set, we provide a feature processing method and process for sensitive parameters. We use machine learning classification methods and time series methods to diagnose and predict faults in small array systems, respectively, achieving high classification and prediction accuracy. The data analysis results indicate that using data-driven small array systems for monitoring and prediction has good engineering application prospects.