The current-based fault diagnosis method is a feasible way to replace the conventional vibration-based method, as it is more economical, implemental, and reliable. With the deep learning (DL) method applied, the current-based methods have achieved satisfactory diagnosis accuracy. DL methods, however, demand large quantities of training samples, which are difficult to implement in real industrial sites. To tackle this problem, this paper proposes a novel lightweight fault diagnosis method based on Convolutional Neural Network (CNN), called CombFilterNet (CF-Net). The first convolutional layer of CF-Net is called CF-layer, where the convolution kernel is the comb filter kernel (CF-kernel). Each CF-kernel only has three parameters to be updated, achieving a lightweight design that makes CF-Net suitable for limited training sample conditions. The effectiveness and generalization ability of the proposed method are validated by a laboratory-acquired current dataset and an open-source vibration dataset. The results demonstrate that the proposed method is superior to the comparative methods under limited training sample conditions.

Cloud-induced atmospheric extinction and occlusion significantly affect the effectiveness and quality of telescope observations. Real-time cloud-cover distribution and long-term statistical data are essential for astronomical siting and telescope operations. Visual inspection is currently the primary approach for analyzing cloud distribution at ground-based astronomical sites. However, the main disadvantages of manual observation methods are human subjectivity, heavy workloads, and poor real-time performance. Therefore, a real-time automatic cloud image classification method is desperately needed. This paper presents a novel cloud identification method named the PSO+XGBoost model, which combines eXtreme Gradient Boosting (XGBoost) with particle-swarm optimization (PSO). The entire cloud image is divided into 37 sub-regions to identify the distribution of the clouds more precisely. Nineteen features, including the sky background, star density, lighting conditions, and subregion grayscale values, are extracted. The experimental results have shown that the overall classification accuracy is 96.91%, and our model can outperform several state-of-the-art baseline methods. Our approach achieves high accuracy in comparison with the manual observation methods. Moreover, this method meets telescope real-time scheduling requirements.

AST3-2 is the second of the three Antarctic Survey Telescopes, aimed at wide-field time-domain optical astronomy. It is located at Dome A, Antarctica, which is by many measures the best optical astronomy site on the Earth’s surface. Here we present the data from the AST3-2 automatic survey in 2016 and the photometry results. The median 5σ limiting magnitude in i-band is 17.8 mag and the light curve precision is 4 mmag for bright stars. The data release includes photometry for over 7 million stars, from which over 3,500 variable stars were detected, with 70 of them newly discovered. We classify these new variables into different types by combining their light curve features with stellar properties from surveys such as StarHorse.

The third Antarctic Survey Telescope array instrument at Dome A in Antarctica, the AST3-3 telescope, has been in commissioning from March 2021. We deployed AST3-3 at the Yaoan astronomical station in Yunnan Province for an automatic time-domain survey and follow-up observations with an optimised observation and protection system. The telescope system of AST3-3 is similar to that of AST3-1 and AST3-2, except that it is equipped with a 14 K × 10 K QHY411 CMOS camera. AST3-3 has a field of view of $1.{65}^{\circ }\times 1.{23}^{\circ }$ and is currently using the g band filter. During commissioning at Yaoan, AST3-3 aims to conduct an extragalactic transient survey, coupled with prompt follow-ups of opportunity targets. In this paper, we present the architecture of the AST3-3 automatic observation system. We demonstrate the data processing of observations by representatives SN 2022eyw and GRB 210420B.

Context . Astronomical observations at millimeter and submillimeter wavelengths strongly depend on the amount of precipitable water vapor (PWV) in the atmosphere, which directly affects the sky transparency and decreases the signal-to-noise ratio of the signals received by radio telescopes. Aims . Predictions of PWV at different forecasting horizons are crucial to supporting the telescope operations, engineering planning, scheduling and observational efficiency of radio observatories installed in the Chajnantor area in northern Chile. Methods . We developed and validated a long short-term memory (LSTM) deep-learning-based model to predict PWV at forecasting horizons of 12, 24, 36, and 48 hours using historical data from two 183 GHz radiometers and a weather station in the Chajnantor area. Results . We find that the LSTM method is able to predict PWV in the 12- and 24-hour forecasting horizons with a mean absolute percentage error of ~22% compared to ~36% for the traditional Global Forecast System method used by the Atacama Pathfinder Experiment, and the root mean square error is reduced by ~50%. Conclusions . We present a first application of deep learning techniques for preliminary predictions of PWV in the Chajnantor area. The prediction performance shows significant improvements compared to traditional methods in 12- and 24-hour time windows. We also propose strategies to improve our method on shorter (36 hour) forecasting timescales.