Fault Diagnosis of the LAMOST Fiber Positioner Based on a Long Short-term Memory(LSTM)Deep Neural Network

The Large Sky Area Multi-Object Fiber Spectroscopic Telescope(LAMOST)has been in normal operation for more than 10 yr,and routine maintenance is performed on the fiber positioner every summer.The positioning accuracy of the fiber positioner directly affects the observation performance of LAMOST,and incorrect fiber positioner positioning accuracy will not only increase the interference probability of adjacent fiber positioners but also reduces the observation efficiency of LAMOST.At present,during the manual maintenance process of the positioner,the fault cause of the positioner is determined and analyzed when the positioning accuracy does not meet the preset requirements.This causes maintenance to take a long time,and the efficiency is low.To quickly locate the fault cause of the positioner,the repeated positioning accuracy and open-loop calibration curve data of each positioner are obtained in this paper through the photographic measurement method.Based on a systematic analysis of the operational characteristics of the faulty positioner,the fault causes are classified.After training a deep learning model based on long short-term memory,the positioner fault causes can be quickly located to effectively improve the efficiency of positioner fault cause analysis.The relevant data can also provide valuable information for annual routine maintenance methods and positioner designs in the future.The method of using a deep learning model to analyze positioner operation failures introduced in this paper is also of general significance for the maintenance and design optimization of fiber positioners using a similar double-turn gear transmission system.

A New Fiber Detection Method for LAMOST Based on the Front-illuminated Method

The double revolving fiber positioning technology is one of the key technologies for the success of the Large Sky Area Multi-Object Fiber Spectroscopic Telescope(LAMOST).The accuracy of fiber positioning will directly affect the observation efficiency of LAMOST.To achieve higher fiber positioning accuracy,the original open-loop controlled fiber positioning system urgently needs to be upgraded into a closed-loop control system.The fiber detection is the most important part of the closed-loop controlled fiber positioning system.The back-illuminated detection method is usually used to detect the fiber position by directly detecting the light spot generated at the fiber end in the multi-fiber spectral surveys.In this paper,we introduce a new method to measure the fiber position based on the image of the front-illuminated LAMOST focal plane.The front-illuminated image does not require lighting devices inside the spectrograph,and it could reduce the instability and light pollution in the spectrograph end.Our method measures the fiber position by fitting the profile of the fiber pinhole with a 2D Gaussian function.A series of tests show that the relative position measurement precision of the front-illuminated method is about 012,and the method could have the same accuracy as the back-illuminated method once the system bias is calibrated by a simple radial correction function.The required fiber positioning accuracy of LAMOST is 04,and the new method satisfies the requirement of LAMOST fiber detection accuracy and could be used in the closed-loop fiber control system.