期刊文献+

Development of a research platform for BEPCⅡ accelerator fault diagnosis

原文传递
导出
摘要 Background BEPCⅡis an electron-positron collider with a beam energy of 1.89 GeV and luminosity of 1×10^(33)cm^(-2)s^(-1).Being a complex accelerator,fault diagnostics both in an accurate manner and in time are often challenging during its operation.Purpose The fault diagnosis research platform is capable of locating and resolving faults on time,thus can largely improve the operation of BEPCⅡby reducing downtime due to machine fault and consequently satisfying the demand for fault diagnosis of a complex system.Methods Making full use of the existing large-scale hardware foundation,abundant data resources and previous experiences,a fault diagnosis research platform was built by applying data mining,machine learning and other related algorithms to carry out specific fault analysis on the entire system.Conclusion The fault diagnosis research platform has been developed with implemented functions of data query,statistics and analysis.Partial fault diagnoses of the three subsystems have been realized.The 4W1 power supply fault has been successfully discovered,and effective solutions have been subsequently provided.
出处 《Radiation Detection Technology and Methods》 CSCD 2020年第3期269-276,共8页 辐射探测技术与方法(英文)
基金 provided by Youth Innovation Promotion Asso-ciation of the Chinese Academy of Sciences(Grant No.2016011)
作者简介 Yanfeng Sui,syf@ihep.ac.cn
  • 相关文献

参考文献2

二级参考文献48

  • 1[1]Fasulo, D. An analysis of recent work on clustering algorithms. Technical Report, Department of Computer Science and Engineering, University of Washington, 1999. http://www.cs.washington.edu.
  • 2[2]Baraldi, A., Blonda, P. A survey of fuzzy clustering algorithms for pattern recognition. IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics), 1999,29:786~801.
  • 3[3]Keim, D.A., Hinneburg, A. Clustering techniques for large data sets - from the past to the future. Tutorial Notes for ACM SIGKDD 1999 International Conference on Knowledge Discovery and Data Mining. San Diego, CA, ACM, 1999. 141~181.
  • 4[4]McQueen, J. Some methods for classification and Analysis of Multivariate Observations. In: LeCam, L., Neyman, J., eds. Proceedings of the 5th Berkeley Symposium on Mathematical Statistics and Probability. 1967. 281~297.
  • 5[5]Zhang, T., Ramakrishnan, R., Livny, M. BIRCH: an efficient data clustering method for very large databases. In: Jagadish, H.V., Mumick, I.S., eds. Proceedings of the 1996 ACM SIGMOD International Conference on Management of Data. Quebec: ACM Press, 1996. 103~114.
  • 6[6]Guha, S., Rastogi, R., Shim, K. CURE: an efficient clustering algorithm for large databases. In: Haas, L.M., Tiwary, A., eds. Proceedings of the 1998 ACM SIGMOD International Conference on Management of Data. Seattle: ACM Press, 1998. 73~84.
  • 7[7]Beyer, K.S., Goldstein, J., Ramakrishnan, R., et al. When is 'nearest neighbor' meaningful? In: Beeri, C., Buneman, P., eds. Proceedings of the 7th International Conference on Data Theory, ICDT'99. LNCS1540, Jerusalem, Israel: Springer, 1999. 217~235.
  • 8[8]Ester, M., Kriegel, H.-P., Sander, J., et al. A density-based algorithm for discovering clusters in large spatial databases with noises. In: Simoudis, E., Han, J., Fayyad, U.M., eds. Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining (KDD'96). AAAI Press, 1996. 226~231.
  • 9[9]Ester, M., Kriegel, H.-P., Sander, J., et al. Incremental clustering for mining in a data warehousing environment. In: Gupta, A., Shmueli, O., Widom, J., eds. Proceedings of the 24th International Conference on Very Large Data Bases. New York: Morgan Kaufmann, 1998. 323~333.
  • 10[10]Sander, J., Ester, M., Kriegel, H.-P., et al. Density-Based clustering in spatial databases: the algorithm GDBSCAN and its applications. Data Mining and Knowledge Discovery, 1998,2(2):169~194.

共引文献88

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