首页> 外文期刊>Quality Control, Transactions >Requirements for Big Data Adoption for Railway Asset Management
【24h】

Requirements for Big Data Adoption for Railway Asset Management

机译:铁路资产管理大数据采用要求

获取原文
获取原文并翻译 | 示例
获取外文期刊封面目录资料

摘要

Nowadays, huge amounts of data have been captured along with the day-to-day operation of assets including railway systems. Hence, we have come to the era of big data. The utilization of big data technologies for asset condition information management is becoming indispensable for improving asset management decision making. The vital information such as precursor information collected on failure modes and knowledge that may be available for analysis is hidden within the large extent of data. There are analysis tools incorporated with techniques such as multiple regression analysis and machine learning that are facilitated by the availability of big data. Therefore, the utilization of big data technologies for asset condition information management is becoming indispensable for improving asset management decision making. This paper provides a review of the requirements and challenges for big data analytics applications to railway asset management. The review focuses on railway asset data collection, data management, data applications with the implementation of Blockchain technology as well as big data analytics technologies. The need for, and the importance of big data analytics in railway asset management; and the requirement for the asset condition data collection in the railway industry are highlighted. Research challenges in railway asset management via application of big data analytics are identified and the future research directions are presented.
机译:如今,已经捕获了大量数据以及包括铁路系统在内的资产的日常运营。因此,我们已经到了大数据的时代。利用资产条件信息管理的大数据技术对于改善资产管理决策变得不可或缺。在可能可用的故障模式和知识上收集的重要信息,例如可以用于分析的知识在很大程度上隐藏。还有分析工具,其中包含了多元回归分析和机器学习,通过大数据的可用性促进了多元回归分析和机器学习。因此,利用资产条件信息管理的大数据技术正在变得不可或缺于改善资产管理决策。本文介绍了对铁路资产管理的大数据分析应用的要求和挑战。审查重点介绍铁路资产数据收集,数据管理,通过实施区块链技术以及大数据分析技术。需要,以及大数据分析在铁路资产管理中的重要性;并突出了铁路行业资产条件数据收集的要求。确定了通过应用大数据分析的铁路资产管理研究挑战,并提出了未来的研究方向。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号