The Intelligent Fault Auxiliary Diagnosis System of Astronomical Telescope Based on Observation Image Recognition

We designed and built an intelligent auxiliary system for fault diagnosis of telescope based on observation image recognition. The system collects the measured image and sensor information of the telescope to comprehensively judge the faults during observation, and gives corresponding solutions. We use the convolution neural network method to intelligently identify images and use fault tree analysis to find the weak points of the telescope system. The simulation and actual tests prove that the system can effectively find, judge faults and give suggestions. It improves the reliability of the telescope and accumulates experience for the intelligentization of fault diagnosis technology.