Feedback on the Surveillance 8 challenge: Vibration-based diagnosis of a Safran aircraft engine

This paper presents the content and outcomes of the Safran contest organized during the International Conference Surveillance 8, October 20–21, 2015, at the Roanne Institute of Technology, France. The contest dealt with the diagnosis of a civil aircraft engine based on vibration data measured in a transient operating mode and provided by Safran. Based on two independent exercises, the contest offered the possibility to benchmark current diagnostic methods on real data supplemented with several challenges. Outcomes of seven competing teams are reported and discussed. The object of the paper is twofold. It first aims at giving a picture of the current state-of-the-art in vibration-based diagnosis of rolling-element bearings in nonstationary operating conditions. Second, it aims at providing the scientific community with a benchmark and some baseline solutions. In this respect, the data used in the contest are made available as supplementary material.

[1]  Paul R. White,et al.  Instantaneous frequency estimation at low signal-to-noise ratios using time-varying notch filters , 2008, Signal Process..

[2]  Robert B. Randall,et al.  Use of the acceleration signal of a gearbox in order to perform angular resampling (with limited speed fluctuation) , 2005 .

[3]  L. D. Avendaño,et al.  Improvement of power line model in ECG for interference reduction using EKF , 2007 .

[4]  Chen Min,et al.  The Application of Stochastic Resonance Theory for Early Detecting Rub-Impact Fault of Rotor System , 2003 .

[5]  J. Antoni The spectral kurtosis: a useful tool for characterising non-stationary signals , 2006 .

[6]  Tomasz Barszcz,et al.  A two-step procedure for estimation of instantaneous rotational speed with large fluctuations , 2013 .

[7]  Alan V. Oppenheim,et al.  Discrete-Time Signal Pro-cessing , 1989 .

[8]  N. Huang,et al.  The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis , 1998, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[9]  K. R. Fyfe,et al.  Computed order tracking applied to vibration analysis of rotating machinery , 1991 .

[10]  Michel Verhaegen,et al.  Subspace identification of multivariable linear parameter-varying systems , 2002, Autom..

[11]  A. K. Ziarani,et al.  A method of extraction of nonstationary sinusoids , 2004, Signal Process..

[12]  James McNames,et al.  Multiharmonic tracking using marginalized particle filters , 2008, 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[13]  Genshiro Kitagawa Introduction to Time Series Modeling , 2010 .

[14]  K. R. Fyfe,et al.  ANALYSIS OF COMPUTED ORDER TRACKING , 1997 .

[15]  Michael Feldman,et al.  Decomposition of non-stationary signals into varying time scales: Some aspects of the EMD and HVD methods , 2011 .

[16]  J. Antoni Fast computation of the kurtogram for the detection of transient faults , 2007 .

[17]  F. Guillet,et al.  New applications of the real cepstrum to gear signals, including definition of a robust fault indicator , 2004 .

[18]  Michael Feldman,et al.  Time-varying vibration decomposition and analysis based on the Hilbert transform , 2006 .

[19]  Alessandro Paolo Daga,et al.  Bearing damage detection techniques and their enhancement: comparison over real data , 2015 .

[20]  Quentin Leclere,et al.  A multi-order probabilistic approach for Instantaneous Angular Speed tracking debriefing of the CMMNO׳14 diagnosis contest , 2016 .

[21]  Jing Lin,et al.  A review and strategy for the diagnosis of speed-varying machinery , 2014, 2014 International Conference on Prognostics and Health Management.

[22]  Germán Castellanos-Domínguez,et al.  Nonlinear model for condition monitoring of non-stationary vibration signals in ship driveline application , 2014 .

[23]  Robert B. Randall,et al.  Editorial for the special issue on Instantaneous Angular Speed (IAS) processing and angular applications , 2014 .

[24]  Barbara F. La Scala,et al.  Design of an extended Kalman filter frequency tracker , 1996, IEEE Trans. Signal Process..

[25]  Michael Feldman,et al.  TOWARD HELICOPTER GEARBOX DIAGNOSTICS FROM A SMALL NUMBER OF EXAMPLES , 2000 .

[26]  Robert B. Randall,et al.  THE RELATIONSHIP BETWEEN SPECTRAL CORRELATION AND ENVELOPE ANALYSIS IN THE DIAGNOSTICS OF BEARING FAULTS AND OTHER CYCLOSTATIONARY MACHINE SIGNALS , 2001 .

[27]  Q. Leclere,et al.  An Analysis of Instantaneous Angular Speed Measurement Errors , 2013 .

[28]  Sergio M. Savaresi,et al.  On the parametrization and design of an extended Kalman filter frequency tracker , 2000, IEEE Trans. Autom. Control..

[29]  Robert B. Randall,et al.  Rolling element bearing diagnostics using the Case Western Reserve University data: A benchmark study , 2015 .

[30]  Sunghan Kim,et al.  Multiharmonic Frequency Tracking Method Using The Sigma-Point Kalman Smoother , 2010, EURASIP J. Adv. Signal Process..