Analysis of Autogram Performance for Rolling Element Bearing Diagnosis by Using Different Data Sets

  • Ali MoshrefzadehEmail author
  • Alessandro Fasana
  • Luigi Garibaldi
Conference paper
Part of the Applied Condition Monitoring book series (ACM, volume 15)


Rolling element bearings are one of the most important component in every rotating machinery. As a result, their diagnosis before occurrence of any catastrophic failure is of vital importance and vibration based diagnosis is very popular approach. In this paper, the performance of a recently proposed method, Autogram, will be investigated on different data sets provided by Politecnico di Torino and University of Cincinnati. The results will be compared with other well-established methods such as Fast Kurtogram and Spectral Correlation.


Rolling element bearing Diagnosis Autogram Fast Kurtogram Fast Spectral Correlation Experimental data 


  1. 1.
    Antoni J (2007) Fast computation of the kurtogram for the detection of transient faults. Mech Syst Signal Process 21(1):108–124CrossRefGoogle Scholar
  2. 2.
    Antoni J, Xin G, Hamzaoui N (2017) Fast computation of the spectral correlation. Mech Syst Signal Process 92:248–277CrossRefGoogle Scholar
  3. 3.
    Moshrefzadeh A, Fasana A (2018) The autogram: an effective approach for selecting the optimal demodulation band in rolling element bearings diagnosis. Mech Syst Signal Process 105:294–318CrossRefGoogle Scholar
  4. 4.
    Antoni J (2007) Cyclic spectral analysis in practice. Mech Syst Signal Process 21(2):597–630CrossRefGoogle Scholar
  5. 5.
    Qiu H, Lee J, Lin J, Yu G (2006) Wavelet filter-based weak signature detection method and its application on rolling element bearing prognostics. J Sound Vib 289(4–5):1066–1090CrossRefGoogle Scholar
  6. 6.

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Ali Moshrefzadeh
    • 1
    Email author
  • Alessandro Fasana
    • 1
  • Luigi Garibaldi
    • 1
  1. 1.Politecnico di TorinoTorinoItaly

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