Residual signal feature extraction for gearbox planetary stage fault detection

(04/11/2019) Residual signal feature extraction for gearbox planetary stage fault detection Faults in planetary gears and related bearings, e.g. planet bearings and planet carrier bearings, pose inherent difficulties on their accurate and consistent detection associated mainly to the low energy in slow rotating stages and the operating complexity of planetary gearboxes. In this work, statistical features measuring the signal energy and Gaussianity are calculated from the residual signals between each pair from the first to the fifth tooth mesh frequency of the meshing process in a multi-stage wind turbine gearbox. The suggested algorithm includes resampling from time to angular domain, identification of the expected spectral signature for proper residual signal calculation and filtering of any frequency component not related to the planetary stage. Two field cases of planet carrier bearing defect and planet wheel spalling are presented and discussed, showing the efficiency of the followed approach and the possibility of characterizing a fault as localized or distributed.

[1]  William D. Mark,et al.  A simple frequency-domain algorithm for early detection of damaged gear teeth , 2010 .

[2]  C. J. Crabtree,et al.  Side-band algorithm for automatic wind turbine gearbox fault detection and diagnosis , 2014 .

[3]  Keith Worden,et al.  A time–frequency analysis approach for condition monitoring of a wind turbine gearbox under varying load conditions , 2015 .

[4]  José R. Perán,et al.  Angular resampling for vibration analysis in wind turbines under non-linear speed fluctuation , 2011 .

[5]  Saravanan Natarajan Gear Box Fault Diagnosis using Hilbert Transform and Study on Classification of Features by Support Vector Machine , 2014 .

[6]  Zijun Zhang,et al.  Fault Analysis and Condition Monitoring of the Wind Turbine Gearbox , 2012, IEEE Transactions on Energy Conversion.

[7]  Liu Hong,et al.  An explanation of frequency features enabling detection of faults in equally spaced planetary gearbox , 2014 .

[8]  Jae-Kyung Lee,et al.  Development of a Novel Power Curve Monitoring Method for Wind Turbines and Its Field Tests , 2014, IEEE Transactions on Energy Conversion.

[9]  Radoslaw Zimroz,et al.  Vibration condition monitoring of planetary gearbox under varying external load , 2009 .

[10]  Yingning Qiu,et al.  Monitoring wind turbine gearboxes , 2013 .

[11]  Ming J. Zuo,et al.  Fault level diagnosis for planetary gearboxes using hybrid kernel feature selection and kernel Fisher discriminant analysis , 2013 .

[12]  Mohamed AbuAli,et al.  A comparative study on vibration‐based condition monitoring algorithms for wind turbine drive trains , 2014 .

[13]  Haitao Liu,et al.  The Statistical Meaning of Kurtosis and Its New Application to Identification of Persons Based on Seismic Signals , 2008, Sensors.

[14]  David He,et al.  Gearbox Tooth Cut Fault Diagnostics Using Acoustic Emission and Vibration Sensors — A Comparative Study , 2014, Sensors.

[15]  Paula J. Dempsey,et al.  Investigation of data fusion applied to health monitoring of wind turbine drivetrain components , 2013 .

[16]  Ming J. Zuo,et al.  Joint amplitude and frequency demodulation analysis based on local mean decomposition for fault diagnosis of planetary gearboxes , 2013 .

[17]  Georgios Alexandros Skrimpas,et al.  Employment of Kernel Methods on Wind Turbine Power Performance Assessment , 2015, IEEE Transactions on Sustainable Energy.

[18]  Huaguang Zhang,et al.  Chaotic Dynamics in Smart Grid and Suppression Scheme via Generalized Fuzzy Hyperbolic Model , 2014 .

[19]  Fouad Slaoui-Hasnaoui,et al.  Wind Turbine Condition Monitoring: State-of-the-Art Review, New Trends, and Future Challenges , 2014 .

[20]  Erkki Oja,et al.  Independent component analysis: algorithms and applications , 2000, Neural Networks.

[21]  Ahmet Kahraman,et al.  A theoretical and experimental investigation of modulation sidebands of planetary gear sets , 2009 .

[22]  Fakher Chaari,et al.  Gearbox Vibration Signal Amplitude and Frequency Modulation , 2012 .

[23]  H. White,et al.  On More Robust Estimation of Skewness and Kurtosis: Simulation and Application to the S&P500 Index , 2003 .

[24]  Quanxin Zhu,et al.  The Interval Stability of an Electricity Market Model , 2014 .

[25]  Yingning Qiu,et al.  Wind turbine condition monitoring: technical and commercial challenges , 2014 .

[26]  Eva Colebunders,et al.  Fixed points of contractive maps on dcpo's , 2013, Math. Struct. Comput. Sci..

[27]  Huageng Luo,et al.  Amplitude modulations in planetary gears , 2014 .

[28]  Eric Bechhoefer,et al.  Investigation on Fault Detection for Split Torque Gearbox Using Acoustic Emission and Vibration Signals , 2009 .

[29]  J. Moors,et al.  A quantile alternative for kurtosis , 1988 .

[30]  Zhipeng Feng,et al.  Fault diagnosis of wind turbine planetary gearbox under nonstationary conditions via adaptive optimal kernel time–frequency analysis , 2014 .

[31]  Annick Lesne,et al.  Shannon entropy: a rigorous notion at the crossroads between probability, information theory, dynamical systems and statistical physics , 2014, Mathematical Structures in Computer Science.

[32]  Cristián Molina Vicuña,et al.  Theoretical frequency analysis of vibrations from planetary gearboxes , 2012 .

[33]  Huageng Luo,et al.  Effective and accurate approaches for wind turbine gearbox condition monitoring , 2014 .