Intelligent condition monitoring of railway catenary systems: A Bayesian Network approach

This study proposes a Bayesian network (BN) dedicated for the intelligent condition monitoring of railway catenary systems. It combines five types of measurements related to catenary condition, namely the contact wire stagger, contact wire height, pantograph head displacement, pantograph head vertical acceleration and pantograph-catenary contact force, as inputsbased on their physical meanings and correlations. It outputs an integrated indicator of catenary condition level. The BN parameters are learned from historical measurement data. Preliminary results shows the applicable ability of the BN to integrate multiple types of parameter while make sense of the output to facilitate maintenance decision making.Railway Engineerin

Automatic Defect Detection of Fasteners on the Catenary Support Device Using Deep Convolutional Neural Network

The excitation and vibration triggered by the long-term operation of railway vehicles inevitably result in defective states of catenary support devices. With the massive construction of high-speed electrified railways, automatic defect detection of diverse and plentiful fasteners on the catenary support device is of great significance for operation safety and cost reduction. Nowadays, the catenary support devices are periodically captured by the cameras mounted on the inspection vehicles during the night, but the inspection still mostly relies on human visual interpretation. To reduce the human involvement, this paper proposes a novel vision-based method that applies the deep convolutional neural networks (DCNNs) in the defect detection of the fasteners. Our system cascades three DCNN-based detection stages in a coarse-to-fine manner, including two detectors to sequentially localize the cantilever joints and their fasteners and a classifier to diagnose the fasteners' defects. Extensive experiments and comparisons of the defect detection of catenary support devices along the Wuhan-Guangzhou high-speed railway line indicate that the system can achieve a high detection rate with good adaptation and robustness in complex environments.Railway Engineerin

The dead line for oil and gas and implication for fossil resource prediction

Fossil fuel resources are invaluable to economic growth and social development. Understanding the formation and distribution of fossil fuel resources is critical for the search and exploration of them. Until now, the vertical distribution depth of fossil fuel resources has not been confirmed due to different understandings of their origins and the substantial variation in reservoir depths from basin to basin. Geological and geochemical data of 13 634 source rock samples from 1286 exploration wells in six representative petroliferous basins were examined to identify the maximum burial depth of active source rocks in each basin, which is referred to in this study as the active source rock depth limit (ASDL). Beyond the ASDL, source rocks no longer generate or expel hydrocarbons and become inactive. Therefore, the ASDL also sets the maximum depth for fossil fuel resources. The ASDLs of basins around the world are found to range from 3000 to 16 000 m, while the thermal maturities (Ro) of source rocks at the ASDLs are almost the same, with Ro ≈ 3:5±0:5 %. The Ro of 3.5% can be regarded as a general criterion to identify ASDLs. High heat flow and more oil-prone kerogen are associated with shallow ASDLs. In addition, tectonic uplift of source rocks can significantly affect ASDLs; 21.6 billion tons of reserves in six representative basins in China and 52 926 documented oil and gas reservoirs in 1186 basins around the world are all located above ASDLs, demonstrating the universal presence of ASDLs in petroliferous basins and their control on the vertical distribution of fossil fuel resources. The data used in this study are deposited in the repository of the PANGAEA database at: https://doi.org/10.1594/PANGAEA.900865 (Pang et al., 2019).Applied Geolog