Residual useful life predictions for train’s rolling bearing based on proportional hazard model

This paper studies the rolling bearing of the train and it puts forward a method of predicting the residual useful life (RUL) of the train’s rolling bearing using the proportional hazard model (PHM). First, the problem of RUL predictions for the train’s rolling bearing is described. Secondly, PHM is introduced, including the basic form, sample data, parameter estimation and prediction. Then, the method of RUL predictions using PHM is put forward. Finally, PHM has been validated by the total life data of the train’s rolling bearing

Reliability Technology Based on Meta-Action for CNC Machine Tool

Computer numerical control (CNC) machines are a category of machining tools that are computer driven and controlled, and are as such, complicated in nature and function. Hence, analyzing and controlling a CNC machine’s overall reliability may be difficult. The traditional approach is to decompose the major system into its subcomponents or parts. This, however, is regarded as not being an accurate method for a CNC machine tool, since it encompasses a dynamic working process. This chapter proposes a meta-action unit (MU) as the basic analysis and control unit, the resulting combined motion effect of which is believed to optimize the CNC’s overall function and performance by improving each meta-action’s reliability. An overview of reliability technology based on meta-action is introduced

Hierarchical Point-based Active Learning for Semi-supervised Point Cloud Semantic Segmentation

Impressive performance on point cloud semantic segmentation has been achieved by fully-supervised methods with large amounts of labelled data. As it is labour-intensive to acquire large-scale point cloud data with point-wise labels, many attempts have been made to explore learning 3D point cloud segmentation with limited annotations. Active learning is one of the effective strategies to achieve this purpose but is still under-explored. The most recent methods of this kind measure the uncertainty of each pre-divided region for manual labelling but they suffer from redundant information and require additional efforts for region division. This paper aims at addressing this issue by developing a hierarchical point-based active learning strategy. Specifically, we measure the uncertainty for each point by a hierarchical minimum margin uncertainty module which considers the contextual information at multiple levels. Then, a feature-distance suppression strategy is designed to select important and representative points for manual labelling. Besides, to better exploit the unlabelled data, we build a semi-supervised segmentation framework based on our active strategy. Extensive experiments on the S3DIS and ScanNetV2 datasets demonstrate that the proposed framework achieves 96.5% and 100% performance of fully-supervised baseline with only 0.07% and 0.1% training data, respectively, outperforming the state-of-the-art weakly-supervised and active learning methods. The code will be available at https://github.com/SmiletoE/HPAL.Comment: International Conference on Computer Vision (ICCV) 202

基于非径向BML-DEA模型的中国地区工业环境绩效测度

Combining the characteristics of BM direction distance function, non- radial DEA model and Luenberger productivity indicators, we develop a non-radial BML-DEA model to measure Environmental Performance. And by using the panel data of 30 provinces from 1997 to 2011 in China, we measure and analysis the regional industrial eco-efficiency. The results show that the overall growth of industrial Environmental Performance of the region comes mainly from technological progress rather than efficiency improvements, the highest average annual growth rate of areas are Beijing, Shanghai, Jiangsu and Guangdong. The effect from three pollutants on industrial Environmental Performance by descending is SO2, CO2 and Smoker, and the contribution of the three pollutants deal more balanced. There are significant differences among industrial Environmental Performance and its decomposition ingredients in different regions. The growth rate of industrial Environmental Performance in east is significantly higher than other regions, but the growth rate of efficiency is not superior to the center and west, and even slightly lower than the center. Therefore, regions need to enhance the efficiency in using the resources

基于非径向BML-DEA模型的中国地区工业环境绩效测度

Combining the characteristics of BM direction distance function, non- radial DEA model and Luenberger productivity indicators, we develop a non-radial BML-DEA model to measure Environmental Performance. And by using the panel data of 30 provinces from 1997 to 2011 in China, we measure and analysis the regional industrial eco-efficiency. The results show that the overall growth of industrial Environmental Performance of the region comes mainly from technological progress rather than efficiency improvements, the highest average annual growth rate of areas are Beijing, Shanghai, Jiangsu and Guangdong. The effect from three pollutants on industrial Environmental Performance by descending is SO2, CO2 and Smoker, and the contribution of the three pollutants deal more balanced. There are significant differences among industrial Environmental Performance and its decomposition ingredients in different regions. The growth rate of industrial Environmental Performance in east is significantly higher than other regions, but the growth rate of efficiency is not superior to the center and west, and even slightly lower than the center. Therefore, regions need to enhance the efficiency in using the resources

Staged Premium Screen Completion Design for Horizontal Well Based on Laboratory Test: A Successful Application in Block 451, Shengli Oil Field

Staged premium screen has been applied to balance the inflow profile of horizontal well under open hole completion in Shengli Oil Field, China. Perforation density of base pipe is the key parameter of staged screen for inflow control, however, it is used to be determined by only considering drawdown due to perforation itself, ignoring two dominant parts caused by formation sand filter cake around screen and sand retention material, therefore the decision plan is not suitable. To solve this problem, a laboratory apparatus was run to test a screen sample, and thoroughly analyzed the effect of perforation density on total inflow control drawdown. A base pipe was also tested, working as a reference to screen sample. The test simulated down hole flowing condition in Well DXY451P21, Block 451, Shengli Oil Field, test results were directly utilized to design staged premium screen for this target well. Finally, the following conclusions can be drawn: pressure drop in screen sample is much sensitive to perforation density, and is obviously larger than that in base pipe sample; flowing pressure is mainly lost in formation sand filter cake and sand retention material, where the flow pattern is seepage flow; perforations on base pipe of premium screen mainly play a part in regulating flowing field, i.e. smaller perforation density will lead produced liquid to flow a longer distance in seepage media and bring a larger additional drawdown; the production performance of Well DXY451P21 is much better than adjacent wells, and promotes economic benefits

MSGNet: multi-source guidance network for fish segmentation in underwater videos

Fish segmentation in underwater videos provides basic data for fish measurements, which is vital information that supports fish habitat monitoring and fishery resources survey. However, because of water turbidity and insufficient lighting, fish segmentation in underwater videos has low accuracy and poor robustness. Most previous work has utilized static fish appearance information while ignoring fish motion in underwater videos. Considering that motion contains more detail, this paper proposes a method that simultaneously combines appearance and motion information to guide fish segmentation in underwater videos. First, underwater videos are preprocessed to highlight fish in motion, and obtain high-quality underwater optical flow. Then, a multi-source guidance network (MSGNet) is presented to segment fish in complex underwater videos with degraded visual features. To enhance both fish appearance and motion information, a non-local-based multiple co-attention guidance module (M-CAGM) is applied in the encoder stage, in which the appearance and motion features from the intra-frame salient fish and the moving fish in video sequences are reciprocally enhanced. In addition, a feature adaptive fusion module (FAFM) is introduced in the decoder stage to avoid errors accumulated in the video sequences due to blurred fish or inaccurate optical flow. Experiments based on three publicly available datasets were designed to test the performance of the proposed model. The mean pixel accuracy (mPA) and mean intersection over union (mIoU) of MSGNet were 91.89% and 88.91% respectively with the mixed dataset. Compared with those of the advanced underwater fish segmentation and video object segmentation models, the mPA and mIoU of the proposed model significantly improved. The results showed that MSGNet achieves excellent segmentation performance in complex underwater videos and can provide an effective segmentation solution for fisheries resource assessment and ocean observation. The proposed model and code are exposed via Github1