Research on Memory in Orbit Fault Diagnosis Algorithm Based on Machine Learning

As a key component in space products, memory will have a significant impact on space electronic systems if it fails on orbit. This will result in serious challenges to the safety and reliability of the entire system operation. Traditional fault diagnosis methods, such as “timed detection” and “post maintenance”, can no longer meet the reliability requirements of spacecraft complex systems. Machine learning has the advantages of fast speed and high accuracy in the field of fault diagnosis. By utilizing machine learning based fault diagnosis methods and constructing fitting functions through internal complex algorithms, fault data can be fully utilized to improve the accuracy of fault diagnosis. This paper researches a memory fault diagnosis algorithm based on machine learning, which can be modeled and trained using the memory feature dataset. When a system failure occurs, the trained fault diagnosis model can be used to analyze the memory fault parameters. This algorithm can identify and diagnose memory faults quickly and accurately. Based on the diagnostic results, quick treatment can be made, and conductive to improving the safety and reliability of the system operation.