期刊文献+

基于CBM的信息设备状态检修研究与尝试 被引量:4

Research and Trying of Information Devices Status Maintenance Based on CBM
下载PDF
导出
摘要 随着计算机体系结构、计算规模的不断扩大,相比于单一计算节点,集群内部出现故障的可能性显著提升,故障已经成为一种常态。主动冗余技术,是保证系统可靠性的常用方式。故障预测,在主动冗余技术中起着至关重要的作用。通过故障预测,可以对集群中计算节点的运行状态进行评估、判断,保证计算节点在真正的故障出现之前,完成节点的失效转移,从而提高系统的可靠性。提出适用于信息设备的故障预测的相关定义、评估标准,并提出一种适用于企业级应用部署的状态检修方案。 With the enlargement of computing scale, faults are more likely to appear in computing factory compared with single computing node, and faults have been becoming a common problem. Active Redundancy is the most effective method to guarantee the robustness of system. Faults prediction is of vital importance in active redundancy. By faults prediction, devices" health status can be evaluated and side effects of faults can be detected before the real faults appear in order to failover. Describes the relevant definition, evaluation standard of faults prediction in information devices area, puts forward a CBM based scheme adapt to enterprise level application, development and deploy- ment.
作者 张杰
出处 《现代计算机》 2016年第3期70-74,80,共6页 Modern Computer
关键词 故障预测 CBM 规则集合 BP神经网络 Fault Prediction CBM Rules Set BP Neural Network
  • 相关文献

参考文献8

  • 1Chakravorty S., Mendes C.L., and Kale L.V. Proactive Fauh Tolerance in MPI Applications Via Task Migration[M]. High Performance Computing-HiPC, 2006:485-496.
  • 2Bosila G., EtaI.MPICH-V: Toward a Scalable Fault Tolerant MPI for Volatile Nodes[C]. In Supercomputing. ACM/IEEE 2002 Confer- ence, 2002.
  • 3Chen G., Jin H., Zou D.Q., Zhou B.B., Qiang W.Z. A Lightweight Software System in the Cloud Environment[J]. Concurrency and Com- putation-Practice & Experience, 2015,27 (12) :2982-2998.
  • 4Dai H.J., Zhao S.L., Zhang J.T., Qiu M.K., Tao L.X. Security Enhancement of Cloud Servers with a Redundancy-Based Fauh-Tolerant Cache Structure[J]. Future Generation Computer Systems-The International Journal of Grid Computing and Science , 2015,52:147 - 155.
  • 5Liu Dong. A Fault-Tolerant Architecture for ROIA in Cloud[J]. Journal of Ambient Intelligence and Humanized Computing, 2015,6 (5): 587-595.
  • 6徐皑冬,于海斌,郭前进.基于状态的设备维护─CBM技术研究[J].工程机械,2005,36(6):9-13. 被引量:9
  • 7侯晓凯,李师谦,王杰琼,胡彬,邓晶.一种基于神经网络的网络设备故障预测系统[J].山东理工大学学报(自然科学版),2014,28(6):29-34. 被引量:11
  • 8严然,孟由,钱德沛,等.故障预测技术研究综述[J].高性能计算发展与应用,2013,43(2):38-48.

二级参考文献11

  • 1Mitchell S.Lebold, Karl M.Reichard,Daniel Ferullo,David Boylan. Open system architecture for Condition-Based Maintenance: Overview and Training Material, http://www.osacbm.org.
  • 2Thurston, M. and Lebold M. Standards Development For Condition-Based Maintenance Systems, Improving Productivity Through Applications of Condition Monitoring, 55th Meeting of the Society for Machinery Failure Prevention Technology, April,2001.
  • 3Lebold,M and Thurston,M.Open Standards for Condition-Based Maintenance and Prognostic Systems, Maintenance and Reliability Conference(MARCON), May 6-9, 2001.
  • 4Mitchell Lebold, Karl Reichard, Petr Hejda, Jail Bezdicek, Mike Thurston, A framework for next generation machinery monitoring and diagnostics, http://www.osacbm.org.
  • 5Jan Bezdicek, Dan Cernohorsky, Petr Hejda, OSA/CBM for COM/DCOM, http://www.osacbm.org.
  • 6Mitchell Lebold,DanFerullo and Karl Reichard,An XML-Based implementation of the OSA-CBM standard using SOAP over HTTP, http://www.osacbm.org.
  • 7徐萍,基于小波分析和神经网络的BFI预测研究[D],大连:大连海事大学,2005.
  • 8李春.故障预测与健康管理(PHM)技术介绍[J].中国高新技术企业,2008(15):43-44. 被引量:7
  • 9刘志伟,刘锐,徐劲松,李毅,周黎明.复杂系统故障预测与健康管理(PHM)技术研究[J].计算机测量与控制,2010,18(12):2687-2689. 被引量:21
  • 10孙妍姑.基于BP神经网络的图像识别技术研究[J].淮南师范学院学报,2010,12(5):22-23. 被引量:13

共引文献19

同被引文献19

引证文献4

二级引证文献4

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部