Parametric Time-Frequency Map and its Processing for Local Damage Detection in Rotating Machinery

The detection of local damage in rotating machinery (gears, bearings) via vibration signal analysis is one of the most powerful techniques in condition monitoring. However, in some cases, especially in heavy industrial machinery, it is difficult to detect damage because of the poor signal-to-noise ratio of the measured vibration. Therefore it is necessary to use unconventional advanced techniques to enhance the signal. In this paper, a novel approach based on parametric time-frequency analysis and further processing for: i) time-varying spectral content modelling, ii) the identification of informative frequency bands by statistical analysis, iii) local damage detection and iv) cycle identification via cepstral analysis, is presented. The proposed procedure is validated using real vibration data from bearings and gearboxes. It is worth noting that this methodology can be also successfully used in time-varying speed conditions (with limited fluctuation).

[1]  Radoslaw Zimroz,et al.  Application of Schur Filtering for Local Damage Detection in Gearboxes , 2012 .

[2]  Gabriel Rilling,et al.  Empirical mode decomposition as a filter bank , 2004, IEEE Signal Processing Letters.

[3]  R. Randall,et al.  Cepstrum Analysis and Gearbox Fault Diagnosis , 2007 .

[4]  Paul R. White,et al.  THE ENHANCEMENT OF IMPULSIVE NOISE AND VIBRATION SIGNALS FOR FAULT DETECTION IN ROTATING AND RECIPROCATING MACHINERY , 1998 .

[5]  Radoslaw Zimroz,et al.  Application of Empirical Mode Decomposition for Impulsive Signal Extraction to Detect Bearing Damage – Industrial Case Study , 2012 .

[6]  Tomasz Barszcz.  Decomposition of Vibration Signals into Deterministic and Nondeterministic Components and its Capabilities of Fault Detection and Identification , 2009, Int. J. Appl. Math. Comput. Sci..

[7]  M. Morf,et al.  Recursive least squares ladder estimation algorithms , 1981 .

[8]  P. D. McFadden,et al.  Vibration monitoring of rolling element bearings by the high-frequency resonance technique — a review , 1984 .

[9]  Jan Zarzycki,et al.  Multidimensional Nonlinear Schur Parametrization of NonGaussian Stochastic Signals. Part One: Statement of the Problem , 2004, Multidimens. Syst. Signal Process..

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

[11]  Leonid M. Gelman,et al.  Adaptive vibration condition monitoring technology for local tooth damage in gearboxes , 2005 .

[12]  Cristian Secchi,et al.  An algorithm to diagnose ball bearing faults in servomotors running arbitrary motion profiles , 2010 .

[13]  Ming J. Zuo,et al.  GEARBOX FAULT DIAGNOSIS USING ADAPTIVE WAVELET FILTER , 2003 .

[14]  Jérôme Antoni,et al.  Detection of signal component modulations using modulation intensity distribution , 2012 .

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

[16]  Fakher Chaari,et al.  Modelling of gearbox dynamics under time-varying nonstationary load for distributed fault detection and diagnosis , 2010 .

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

[18]  Fakher Chaari,et al.  Study of a spur gear dynamic behavior in transient regime , 2011 .

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

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

[21]  Radoslaw Zimroz,et al.  Gearbox Condition Estimation Using Cyclo-Stationary Properties of Vibration Signal , 2009 .

[22]  Yuesheng Xu,et al.  Gearbox fault diagnosis using empirical mode decomposition and Hilbert spectrum , 2006 .

[23]  Radoslaw Zimroz,et al.  Application of Adaptive Filtering for Weak Impulsive Signal Recovery for Bearings Local Damage Detection in Complex Mining Mechanical Systems Working under Condition of Varying Load , 2011 .

[24]  Radoslaw Zimroz,et al.  A procedure for weighted summation of the derivatives of reflection coefficients in adaptive Schur filter with application to fault detection in rolling element bearings , 2013 .

[25]  Radoslaw Zimroz,et al.  Adaptive Bearings Vibration Modelling for Diagnosis , 2011, ICAIS.

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

[27]  C. Laplanche,et al.  Non-Stationary Time-Series Segmentation Based on the Schur Prediction Error Analysis , 2005, IEEE/SP 13th Workshop on Statistical Signal Processing, 2005.