Computer Science ›› 2020, Vol. 47 ›› Issue (11A): 504-507.doi: 10.11896/jsjkx.191000086

• Big Data & Data Science • Previous Articles     Next Articles

Incremental FFT Based on Apache Storm and Its Application

ZHAO Xin1,2, MA Zai-chao1,2, LIU Ying-bo1,2, DING Yu-ting1,2, WEI Mu-heng3   

  1. 1 Institute of Information System and Engineering,School of Software,Tsinghua University,Beijing 100084,China
    2 Beijing Key Laboratory for Industrial Big Data System and Application,Beijing 100084,China
    3 CSSC Systems Engineering Research Institute,Beijing 100070,China
  • Online:2020-11-15 Published:2020-11-17
  • About author:ZHAO Xin,born in 1994,Ph.D candidate.His main research interests includes schema evolution and data integration.
  • Supported by:
    This work was supported by the National Key Research and Development Program of China (2016YFB0501504) and National Natural Science Foundation of China (U1509213).

Abstract: The conventional Fast Fourier Transform which is difficult to process the industrial big data in real time is a stand-alone algorithm with the batch processing techniques.In this paper,an Incremental FFT based on Apache storm is proposed.A non-recursive computational logic is first designed in Apache Storm.Then,a rotor misalignment experiment is performed on a Bently rotor test bench.With the rotor vibration data,a visual monitoring interface is developed by DataWay Framework.The result shows that the frequency spectrum of the stream data can be updated in real time with the proposed method and its realization.

Key words: Apache storm, Axis orbit, DWF, Incremental FFT, Rotor

CLC Number: 

  • TP311.1
[1] LI G J,CHENG X Q.Research Status and Scientific Thinking of Big Data[J].Bulletin of Chinese Academy of Sciences,2012,27(6):647-657.
[2] YAN X,SUN Y,WAN J,et al.Industrial Big Data for Fault Diagnosis:Taxonomy,Review,and Applications[J].IEEE Access,2017,PP(99):1-1.
[3] LEI Y G,JIA F,KONG D T,et al.Opportunity and challenge of mechanical intelligent fault diagnosis under big data[J].Journal of Mechanical Engineering,2018,54(5):94-104.
[4] LYONS RG.Understanding digital signal processing[M].Englewood Cliffs,New Jersy:Prentice Hall,2010.
[5] CHEN JL,LI ZP,PAN J,et al.Wavelet transform based on inner product in fault diagnosis of rotating machinery:A review[J].Mechanical Systems and Signal Processing,2016,70:1-35.
[6] QIN SJ.Process Data Analytics in the Era of Big Data[J].Aiche Journal,2014,60(9):3092-3100.
[7] JIANG X C,SHENG G G.Research and application of big data analysis of power equipment state[J].High Votage Enginee-ring,2018,44(4):1041-1050.
[8] QIAO X,LIU F,YU B H.Design of distributed digital signalprocessing algorithm library based on spark [J].Computer Systems & Applications,2018,27(8):214-218.
[9] YANG C,BAO W,ZHU X,et al.A Parallel Fast Fourier Transform Algorithm for Large-Scale Signal Data Using Apache Spark in Cloud[C]//International Conference onAlgorithms and Architectures for ParallelProcessing.Cham:Springer International Publishing,2018:293-310.
[10] JI P,LI H,CHEN M,et al.Dofft:a fast Fourier transformmethod based on Distributed Database[J].Computer and Moder-nization,2018(6):19-24,29.
[11] ZHANG S M,MAO D,WANG B Y.Application of big dataprocessing technology in gearbox fault diagnosis and early war-ning of wind turbine[J].Automation of Electric Power Systems,2016,40(14):129-134.
[12] HU H,BO T,GONG XJ,et al.Intelligent fault diagnosis of the high-speed train with big data based on deep neural networks[J].IEEE Transactions on Industrial Informatics,2017,13(4):2106-2116.
[13] LIU Y,YANG Q,AN D,et al.An improved fault diagnosismethod based on deep wavelet neural network[C]//2018 Chinese Control And Decision Conference.2018:1048-1053.
[14] LI H,ZHANG Q,QIN X R,et al.Bearing fault diagnosis method based on STFT and convolution neural network[J].Journal of Vibration and Shock,2018,37(19):124-131.
[15] COOLEY JW,TUKEY JW.An algorithm for the machine calculation of complex Fourier series[J].Mathematics of Computation,1965,19(90):297-301.
[16] JAIN A,NALYA A.Learning Apache Storm[M].New York: Packt Publishing,2014.
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[8] . [J]. Computer Science, 2007, 34(5): 280-284.
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