Unanticipated Fault Detection Technology for Tip/Tilt Mirror Control System of Large Aperture Telescope

Large Aperture telescopes are usually placed in some special environment. Once a malfunction occurs, it's inconvenient for maintenance. This paper presents those unanticipated failures in tip/tilt mirror control system of large aperture telescopes, and tries to propose a detection strategy based on BP neural network. Firstly, the paper analyses the failure mechanism of the software and the hardware of the tip/tilt mirror control system. Then the known fault data are used to train the BP neural network in order to obtain an accurate detection model. Finally, the unanticipated data are mixed into the known fault data, and the model is used to detect unanticipated faults. The simulation results show that the model can be used to detect the unanticipated fault in tip/tilt mirror control system effectively.