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As a high-performance precision reduction transmission equipment with large transmission ratio, high transmission efficiency, high transmission accuracy, stable transmission and low noise, reducer has been widely used in many fields such as industrial robots. The definition of the reducer is studied, the development history of the reducer is briefly introduced, and the classification and characteristics of the reducer are summarized, including traditional reducer, RV reducer, harmonic planetary reducer and cycloid reducer, A new generation of reducer and influencing factors such as transmission accuracy, transmission efficiency, backlash, torsional stiffness, noise, and assembly. The problem of precision reducer is analyzed, and finally the application and development trend of reducer are studied.

Convolutional networks are powerful visual models that transform images into more effective representations. To make full use of this technique, we propose a new method based on deep learning and convolutional network to effectively get the discharge information of an ultraviolet (UV) image. We firstly segment the equipment region and the UV spot region separately by using the DeepLab network, and then several properties which can show the discharge information are extracted on the basis of the segmentation. The use of the DeepLab network helps to get a reliable segmentation result, and more accurate discharge information. We take 5000 UV images to test our network, and use the concept mean IOU to evaluate its performance. The results show the advantage of the method, and it can meet the demands of further fault diagnosis.