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DATA DRIVEN MODELING FOR POWER TRANSFORMER LIFESPAN EVALUATION

Journal of Systems Science and Systems Engineering 2014年第1期23卷 80-93页

作者： Charles V.TRAPPEY Amy J.C.TRAPPEY Lin MA Wan-Ting TSAO Department of Management Science National Chiao Tung University Department of Industrial Engineering and Engineering Management National Tsing Hua University School of Chemistry Physics and Mechanical EngineeringQueensland University of Technology

Large sized power transformers are important parts of the power supply chain. These very critical networks of engineering assets are an essential base of a nation＇s energy resource infrastructure. This research identifies the key factors influencing transformer normal operating conditions and predicts the asset management lifespan. Engineering asset research has developed few lifespan forecasting methods combining real-time monitoring solutions for transformer maintenance and replacement. Utilizing the rich data source from a remote terminal unit （RTU） system for sensor-data driven analysis, this research develops an innovative real-time lifespan forecasting approach applying logistic regression based on the Weibull distribution. The methodology and the implementation prototype are verified using a data series from 161 kV transformers to evaluate the efficiency and accuracy for energy sector applications. The asset stakeholders and suppliers significantly benefit from the real-time power transformer lifespan evaluation for maintenance and replacement decision support.

关键词： Condition based maintenance (CBM) prognostics and health management (PHM) logisticregression remaining life prediction sustainable engineering asset management

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Public priorities and consumer preferences for selected attributes of automated vehicles

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Journal of Modern Transportation 2018年第1期26卷 72-79页

作者： Paulina Lustgarten Scott Le Vine Department of Geography State University of New York (SUNY) at New Paltz School of Transportation and Logistics Southwest Jiaotong University Department of Civil and Environmental Engineering Imperial College London

Public opinion and consumer preferences are among the various constraints on the rollout of automated cars, as they will affect the decision-making of both automotive industry actors and public-sector regulators. This study contributes to the growing body of the literature regarding this issue, through a moderate-scale survey （n = 370） that incorporated both prioritization/attitudinal questions （regarding public opinion） and a stated-prefer- ence module （to identify consumer preferences）. The sur- vey protocol includes a stated-preference approach to investigate consumers＇ preferences for the possibility of very high rates of speed in automated cars on long-distance journeys. We found separately identifiable effects for average travel speeds （manifested as journey duration） and maximum travel speed in the stated-preference scenarios. In the ＇prioritization＇ component of the survey, respondents ranked having the ＇highest possible level of safety＇ as the single most important benefit that they would like auto- mated cars to deliver, ahead of benefits such as being able to performing activities while traveling or having traffic congestion reduced. This result has consequences for the car-following distances that are programmed into the control algorithms of automated cars. Documenting this finding is important, as decisions must be made in the near future by driving-algorithm designers, public-sector regu- lators, and ultimately the judiciary regarding the guidelines for acceptable automated driving-behavior instructions.

关键词： Automated car Stated-preference logisticregression

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