An Automated Tool for Dynamic Field Reliability Assessment Using Life Data Analysis

Reliability analysis is essential for ensuring optimal system performance in real-world conditions. Traditional methods rely on static models, limiting adaptability to dynamic environments. This paper introduces an automated Dynamic Field Reliability (DFR) analysis approach using field data. It streamlines reliability evaluations by automatically processing downtime logs and computing key metrics. The methodology includes failure time extraction, lifecycle phase determination via Laplace trend tests, data distribution estimation using median rank regression and Weibull techniques, and sensor reliability evaluation based on system performance. A case study on remote sensing onboard sensor geometric performance and antenna control servo system performance is presented.