Research on Operational Condition Monitoring Strategy for Experimental Equipment of Space Environment Simulation and Research Infrastructure

For the space environment simulation test field of complete sets or individual experimental equipment, the condition monitoring is necessary to ensure the normal operation of the equipment. In this paper, we propose a condition monitoring strategy for experimental equipment based on power load characteristic analysis technology. First, the typical state of the experimental equipment is analyzed and the data on the electrical load of the equipment is collected. Secondly, the K-means clustering algorithm was used to classify the collected data and construct a library of features corresponding to each typical state; after that, a neural network model was built and model optimization was carried out to achieve the function of equipment condition monitoring; finally, the feasibility of the proposed strategy is verified by two types of equipment in the EMBED dataset. The simulation results show that the proposed equipment condition monitoring strategy can realize the condition monitoring of experimental equipment to a certain extent, and the condition monitoring effect is better for the equipment with rapid state switching.