Research on Fault Diagnosis of Hydrogen Maser Based on Machine Learning

Hydrogen maser, as a precise time-keeping and punctual instrument, plays an important role in scientific research and engineering application. However, at present, hydrogen maser in China still has problems such as equipment failure and poor reliability. In order to simplify the troubleshooting process for technicians and improve maintenance efficiency, this article proposes a method that uses machine learning to take hydrogen maser historical data as training samples, combines DBSCAN and artificial neural network algorithms to obtain a clock diagnostic model, thereby greatly simplifying the troubleshooting process. The trained model was deployed on the embedded system in the experiment, then real-time prediction result was given to the technical staff as a reference, which confirmed the feasibility and effectiveness of this method.