Characterization of gear faults in variable rotating speed using Hilbert-Huang Transform and instantaneous dimensionless frequency normalization

Abstract The objective of this research is to investigate the feasibility of utilizing the instantaneous dimensionless frequency (DLF) normalization and Hilbert-Huang Transform (HHT) to characterize the different gear faults in case of variable rotating speed. The normalized DLF of the vibration signals are calculated based on the rotating speed of shaft and the instantaneous frequencies of Intrinsic Mode Functions (IMFs) which are decomposed by Empirical Mode Decomposition (EMD) process. The faulty gear features on DLF-energy distribution of vibration signal can be extracted without the presence of shaft rotating speed, so that the proposed approach can be applied for characterizing the malfunctions of gearbox system under variable shaft rotating speed. A test rig of gear transmission system is performed to illustrate the gear faults, including worn tooth, broken tooth and gear unbalance. Different methods to determine the instantaneous frequency are employed to verify the consistence of characterization results. The DLF-energy distributions of vibration signals are investigated in different faulty gear conditions. The analysis results demonstrate the capability and effectiveness of the proposed approach for characterizing the gear malfunctions at the DLFs corresponding to the meshing frequency as well as the shaft rotating frequency. The support vector machine (SVM) is then employed to classify the vibration patterns of gear transmission system at different malfunctions. Using the energy distribution at the characteristic DLFs as the features, the different fault types of gear can be identified by SVM with high accuracy.

[1]  Dejie Yu,et al.  A gear fault diagnosis using Hilbert spectrum based on MODWPT and a comparison with EMD approach , 2009 .

[2]  Jian-Da Wu,et al.  Faulted gear identification of a rotating machinery based on wavelet transform and artificial neural network , 2009, Expert Syst. Appl..

[3]  Min-Chun Pan,et al.  Investigation on improved Gabor order tracking technique and its applications , 2006 .

[4]  A. R. Mohanty,et al.  Fault Detection in a Multistage Gearbox by Demodulation of Motor Current Waveform , 2006, IEEE Transactions on Industrial Electronics.

[5]  Norden E. Huang,et al.  Ensemble Empirical Mode Decomposition: a Noise-Assisted Data Analysis Method , 2009, Adv. Data Sci. Adapt. Anal..

[6]  Yu Yang,et al.  Application of time–frequency entropy method based on Hilbert–Huang transform to gear fault diagnosis , 2007 .

[7]  Keith Worden,et al.  Classification of faults in gearboxes — pre-processing algorithms and neural networks , 1997, Neural Computing & Applications.

[8]  Anand Parey,et al.  Dynamic modelling of spur gear pair and application of empirical mode decomposition-based statistical analysis for early detection of localized tooth defect , 2006 .

[9]  G. Meltzer,et al.  Fault detection in gear drives with non-stationary rotational speed: Part I: The time-frequency approach , 2003 .

[10]  Chun-Chieh Wang,et al.  Applications of fault diagnosis in rotating machinery by using time series analysis with neural network , 2010, Expert Syst. Appl..

[11]  M. Zuo,et al.  Gearbox fault detection using Hilbert and wavelet packet transform , 2006 .

[12]  Yaguo Lei,et al.  Gear crack level identification based on weighted K nearest neighbor classification algorithm , 2009 .

[13]  Yu-Liang Chung,et al.  A looseness identification approach for rotating machinery based on post-processing of ensemble empirical mode decomposition and autoregressive modeling , 2012 .

[14]  N. Huang,et al.  A study of the characteristics of white noise using the empirical mode decomposition method , 2004, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[15]  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 .

[16]  K. I. Ramachandran,et al.  A comparative study on classification of features by SVM and PSVM extracted using Morlet wavelet for fault diagnosis of spur bevel gear box , 2008, Expert Syst. Appl..

[17]  Chris J. Harris,et al.  Hybrid Computed Order Tracking , 1999 .

[18]  K. I. Ramachandran,et al.  Fault diagnosis of spur bevel gear box using discrete wavelet features and Decision Tree classification , 2009, Expert Syst. Appl..

[19]  Markus Timusk,et al.  Fault detection in variable speed machinery: Statistical parameterization , 2009 .

[20]  Zhongkui Zhu,et al.  Cyclostationarity analysis for gearbox condition monitoring: Approaches and effectiveness , 2005 .

[21]  Robert B. Randall,et al.  State of the art in monitoring rotating machinery. Part 2 , 2004 .

[22]  Amiya R Mohanty,et al.  Monitoring gear vibrations through motor current signature analysis and wavelet transform , 2006 .

[23]  P. D. McFadden,et al.  APPLICATION OF WAVELETS TO GEARBOX VIBRATION SIGNALS FOR FAULT DETECTION , 1996 .

[24]  Yang Yu,et al.  The application of energy operator demodulation approach based on EMD in machinery fault diagnosis , 2007 .

[25]  Norden E. Huang,et al.  On Instantaneous Frequency , 2009, Adv. Data Sci. Adapt. Anal..

[26]  Yu-Liang Chung,et al.  Looseness Diagnosis of Rotating Machinery Via Vibration Analysis Through Hilbert–Huang Transform Approach , 2010 .

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

[28]  Jian-Da Wu,et al.  An order-tracking technique for the diagnosis of faults in rotating machineries using a variable step-size affine projection algorithm , 2005 .

[29]  B. Samanta,et al.  Gear fault detection using artificial neural networks and support vector machines with genetic algorithms , 2004 .

[30]  Peter W. Tse,et al.  Use of autocorrelation of wavelet coefficients for fault diagnosis , 2009 .

[31]  Cheng-Kuo Sung,et al.  Locating defects of a gear system by the technique of wavelet transform , 2000 .

[32]  T. Y. Wu,et al.  Misalignment diagnosis of rotating machinery through vibration analysis via the hybrid EEMD and EMD approach , 2009 .

[33]  Giorgio Dalpiaz,et al.  Effectiveness and Sensitivity of Vibration Processing Techniques for Local Fault Detection in Gears , 2000 .

[34]  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.