• Journal of Internet of Things and Convergence
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• v.8 no.6
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• pp.55-63
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• 2022

In recent years, with the rapid development of large and medium-sized urban rail transit in China, the total operating mileage of high-speed railway and the total number of EMUs(Electric Multiple Units) are rising. The system complexity of high-speed EMU is constantly increasing, which puts forward higher requirements for the safety of equipment and the efficiency of maintenance.At present, the maintenance mode of high-speed EMU in China still adopts the post maintenance method based on planned maintenance and fault maintenance, which leads to insufficient or excessive maintenance, reduces the efficiency of equipment fault handling, and increases the maintenance cost. Based on the intelligent operation and maintenance technology of PHM(prognostics and health management). This thesis builds an integrated PHM platform of "vehicle system-communication system-ground system" by integrating multi-source heterogeneous data of different scenarios of high-speed EMU, and combines the equipment fault mechanism with artificial intelligence algorithms to build a fault prediction model for traction motors of high-speed EMU.Reliable fault prediction and accurate maintenance shall be carried out in advance to ensure safe and efficient operation of high-speed EMU.

• International Commerce and Information Review
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• v.15 no.2
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• pp.3-23
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• 2013

In 1997, 180 countries signed the Kyoto Protocol in Kyoto, Japan. The Kyoto Protocol came into force in February 2005. The agreement calls for industrialized nations to cut greenhouse gas emissions by 5 percent from 1990 levels by 2008 to 2012. One of those polices is a modal shift that change from road freight to sea, inland waterway and railroad transportation that is eco-friendly. The increase of road freight brings road congestion, accidents, logistic costs, air pollution and greenhouse gases. Railroads are superior than the other modes of transportation in mass transportability, high speed, timeliness, safety and environmental-friendliness, but the railway industry has been pushed behind in competition. Korean railroads were used by passengers and freight transport popularly until the middle of 20th century, however, by the sudden change of logistics environments, a shaving time efficiency being most important, railroad logistic lost its competitive power against the transportation by truck. From the research which sees consequently investigated a various policy, a system and a law about Chinese logistics industry and present condition of the Chinese goods enterprise and instance analysis of the large Chinese corporation that branch out to undeveloped markets led and a Chinese logistics industry and problem point escape hereafter the heightening of competitiveness plan which is rational under prsenting boil.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.16 no.2
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• pp.128-139
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• 2017

Railway is one of the public transportation systems along with shipping and aviation. With the recent introduction of high speed train, its proportion is increasing rapidly, which results in the higher risk of catastrophic failures. The wheel bearing to support the train is one of the important components requiring higher reliability and safety in this aspect. Recently, many studies have been made under the name of prognostics and health management (PHM), for the purpose of fault diagnosis and failure prognosis of the bearing under operation. Among them, the most important step is to extract a feature that represents the fault status properly and is useful for accurate remaining life prediction. However, the conventional features have shown some limitations that make them less useful since they fluctuate over time even after the signal de-noising or do not show a distinct pattern of degradation which lack the monotonic trend over the cycles. In this study, a new method for feature extraction is proposed based on the observation of relative frequency energy shifting over the cycles, which is then converted into the feature using the information entropy. In order to demonstrate the method, traditional and new features are generated and compared using the bearing data named FEMTO which was provided by the FEMTO-ST institute for IEEE 2012 PHM Data Challenge competition.