Fault-Tolerant Design in Space Electronics with AIDriven Real-Time Error Correction

Space electronics are essential for maintaining reliable operation in space missions, where radiation, extreme temperatures, and micro-gravity conditions challenge electronic devices. Standard space electronics devices are susceptible to single event upsets (SEUs) and permanent hardware degradation, which results in mission failure. Conventional measures for fault tolerance include Triple Modular Redundancy (TMR) and error correction codes (ECC), which use redundancy and error correctness strategies. These conventional methods are resource-consuming and lack effectiveness in handling dynamic faults in real-time applications. Avoiding these shortcomings, this approach proposes AI-Enhanced Fault Tolerant Electronic System (AIFTES), which combines Real-Time Error Prediction (RTEP) and Dynamic Error Correction (DEC) based on state-of-the-art machine learning algorithms. AIFTES utilizes Reinforcement Learning (RL) for adaptive error detection along with Correction Neural Networks (CNN) for real-time error correction and data correctness under real-world conditions. AIFTES distinguishes from conventional ones in that it adapts in real-time for dynamic environmental conditions, achieving improved fault detection accuracy by 0.20% and reducing computing overhead by 0.30%. This solution provides robust and autonomous space operations, optimizing mission reliability, reducing energy consumption, and maximizing system performance.