Stochastic Analysis of Redundant RF Amplifiers with Priority Repair and Data-Driven Parameters

Abstract

RF amplifier modules provide fundamental and indispensable functionality across diverse domains such as aerospace, defence, medical imaging, industrial automation, and scientific research by ensuring reliable operation and robust signal transmission. Their criticality is amplified in high-end applications for example cellular base stations, radar, and real-time networks dependent. The current work develops a stochastic model for the reliability analysis of a parallel redundant system comprising a primary and a backup RF Power Amplifier Module. Utilizing the semi-Markov Regenerative Point Technique (SMRPT), we compute key metrics like Mean Time to System Failure (MTSF), availability and system’s profit. The system is initially fully operational and fails only when both units fail. A dedicated server performs immediate, flawless repairs upon failure, prioritizing the primary module. The study assumes constant failure rates and exponentially distributed repair times. To estimate the failure and repair rates from empirical data, a linear regression technique which is one of the fundamental supervised learning methods in AI and machine learning, is utilized. The observed repair and failure times are considered as dependent variables and the rates are approximated as the inverse of the mean observed times.