Pattern Recognition

ML Pattern Recognition for Recurring Fault Modes

Machine-learning pattern recognition for recurring fault modes is an automated diagnostic system executed on radiation-hardened NOR flash and an ML accelerator, consuming 1 W of power. It applies frequent pattern mining and root-cause clustering to subsystem fault histories to detect an average of 20 recurring failure modes per year. Operating over a 100-year design lifetime exposed to deep-space radiation without magnetospheric protection, the system relies on authenticated pattern definitions and audit trails to track multi-decade component degradation. With a 500,000-hour mean time between failures, it issues recurring-pattern alerts that drive preventive engineering redesigns and operational procedure updates.

ML pattern recognition identifying recurring fault modes — informing preventive design changes and procedure updates.