A methodology for identifying information rich frequency bands for diagnostics of mechanical components-of-interest under time-varying operating conditions

Abstract Performing condition monitoring on rotating machines such as wind turbines, which operate inherently under time-varying operating conditions, remains a challenge. The signal components generated by incipient damage are masked by other signal components that are not of interest and high noise levels. In this work, a new method, referred to as the IFBI α gram, is proposed that is capable of identifying frequency bands that are rich with diagnostic information related to specific cyclic components. This allows the optimal frequency band to be determined for diagnosing the component-of-interest. It is shown on numerical and experimental gearbox data that this method is not only capable of detecting incipient damage, but is also robust to time-varying operating conditions. Therefore, it can be used to independently determine the condition of different mechanical components and it is robust to spurious transients.

[1]  Jérôme Antoni,et al.  Envelope analysis of rotating machine vibrations in variable speed conditions: A comprehensive treatment , 2017 .

[2]  Dong Wang,et al.  An extension of the infograms to novel Bayesian inference for bearing fault feature identification , 2016 .

[3]  Alessandro Fasana,et al.  The Autogram: An effective approach for selecting the optimal demodulation band in rolling element bearings diagnosis , 2018 .

[4]  Yaguo Lei,et al.  A review on empirical mode decomposition in fault diagnosis of rotating machinery , 2013 .

[5]  Darryll J. Pines,et al.  A review of vibration-based techniques for helicopter transmission diagnostics , 2005 .

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

[7]  Pietro Borghesani,et al.  A statistical methodology for the design of condition indicators , 2019, Mechanical Systems and Signal Processing.

[8]  P. D. McFadden,et al.  APPLICATION OF SYNCHRONOUS AVERAGING TO VIBRATION MONITORING OF ROLLING ELEMENT BEARINGS , 2000 .

[9]  Jérôme Antoni,et al.  Order-frequency analysis of machine signals , 2017 .

[10]  Jérôme Antoni,et al.  Angle⧹time cyclostationarity for the analysis of rolling element bearing vibrations , 2015 .

[11]  Peter W. Tse,et al.  The design of a new sparsogram for fast bearing fault diagnosis: Part 1 of the two related manuscripts that have a joint title as “Two automatic vibration-based fault diagnostic methods using the novel sparsity measurement – Parts 1 and 2” , 2013 .

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

[13]  Robert B. Randall,et al.  Application of spectral kurtosis for detection of a tooth crack in the planetary gear of a wind turbine , 2009 .

[14]  Mohamed Benbouzid,et al.  Wind turbine high-speed shaft bearings health prognosis through a spectral Kurtosis-derived indices and SVR , 2017 .

[15]  Konstantinos Gryllias,et al.  A pre-processing methodology to enhance novel information for rotating machine diagnostics , 2019, Mechanical Systems and Signal Processing.

[16]  Yaguo Lei,et al.  Envelope harmonic-to-noise ratio for periodic impulses detection and its application to bearing diagnosis , 2016 .

[17]  Robert B. Randall,et al.  Optimised Spectral Kurtosis for bearing diagnostics under electromagnetic interference , 2016 .

[18]  Pietro Borghesani,et al.  Optimal demodulation-band selection for envelope-based diagnostics: A comparative study of traditional and novel tools , 2019 .

[19]  Wenyi Wang,et al.  EARLY DETECTION OF GEAR TOOTH CRACKING USING THE RESONANCE DEMODULATION TECHNIQUE , 2001 .

[20]  Jérôme Antoni,et al.  The spectral analysis of cyclo-non-stationary signals , 2016 .

[21]  P. S. Heyns,et al.  Online shaft encoder geometry compensation for arbitrary shaft speed profiles using Bayesian regression , 2016 .

[22]  J. Antoni.  Cyclic spectral analysis in practice , 2007 .

[23]  Robert B. Randall,et al.  Rolling element bearing diagnostics—A tutorial , 2011 .

[24]  Peter W. Tse,et al.  An enhanced Kurtogram method for fault diagnosis of rolling element bearings , 2013 .

[25]  Tomasz Barszcz,et al.  A novel method for the optimal band selection for vibration signal demodulation and comparison with the Kurtogram , 2011 .

[26]  Dany Abboud,et al.  Deterministic-random separation in nonstationary regime , 2016 .

[27]  Robert B. Randall,et al.  Application of cepstrum pre-whitening for the diagnosis of bearing faults under variable speed conditions , 2013 .

[28]  Jérôme Antoni,et al.  Self-running bearing diagnosis based on scalar indicator using fast order frequency spectral coherence , 2019, Measurement.

[29]  Robert B. Randall,et al.  Cyclo-non-stationary Based Bearing Diagnostics of Planetary Gearboxes , 2018, Applied Condition Monitoring.

[30]  F. Combet,et al.  Optimal filtering of gear signals for early damage detection based on the spectral kurtosis , 2009 .

[31]  P. S. Heyns,et al.  USING VIBRATION MONITORING FOR LOCAL FAULT DETECTION ON GEARS OPERATING UNDER FLUCTUATING LOAD CONDITIONS , 2002 .

[32]  J. Antoni,et al.  Fast computation of the spectral correlation , 2017 .

[33]  Fulei Chu,et al.  Application of the wavelet transform in machine condition monitoring and fault diagnostics: a review with bibliography , 2004 .

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

[35]  Jérôme Antoni,et al.  Cyclostationary modelling of rotating machine vibration signals , 2004 .

[36]  Tomasz Barszcz,et al.  Diagnostics of bearings in presence of strong operating conditions non-stationarity—A procedure of load-dependent features processing with application to wind turbine bearings , 2014 .

[37]  P. Borghesani,et al.  A faster algorithm for the calculation of the fast spectral correlation , 2018, Mechanical Systems and Signal Processing.

[38]  J. Antoni.  Cyclostationarity by examples , 2009 .

[39]  Jérôme Antoni,et al.  The infogram: Entropic evidence of the signature of repetitive transients , 2016 .

[40]  Robert B. Randall,et al.  The spectral kurtosis: application to the vibratory surveillance and diagnostics of rotating machines , 2006 .

[41]  Jérôme Antoni,et al.  Application of averaged instantaneous power spectrum for diagnostics of machinery operating under non-stationary operational conditions , 2012 .

[42]  Anas Sakout,et al.  Gearbox condition monitoring in wind turbines: A review , 2018, Mechanical Systems and Signal Processing.

[43]  Jérôme Antoni,et al.  Non-intrusive rattle noise detection in non-stationary conditions by an angle/time cyclostationary approach , 2016 .

[44]  Paolo Pennacchi,et al.  The relationship between kurtosis- and envelope-based indexes for the diagnostic of rolling element bearings , 2014 .

[45]  Yu Yang,et al.  The envelope order spectrum based on generalized demodulation time–frequency analysis and its application to gear fault diagnosis , 2010 .

[46]  Konstantinos Gryllias,et al.  A methodology using the spectral coherence and healthy historical data to perform gearbox fault diagnosis under varying operating conditions , 2020 .