Wavelet packet transform for detection of single events in acoustic emission signals

Abstract Acoustic emission signals in tribology can be used for monitoring the state of bodies in contact and relative motion. The recorded signal includes information which can be associated with different events, such as the formation and propagation of cracks, appearance of scratches and so on. One of the major challenges in analyzing these acoustic emission signals is to identify parts of the signal which belong to such an event and discern it from noise. In this contribution, a wavelet packet decomposition within the framework of multiresolution analysis theory is considered to analyze acoustic emission signals to investigate the failure of tribological systems. By applying the wavelet packet transform a method for the extraction of single events in rail contact fatigue test is proposed. The extraction of such events at several stages of the test permits a classification and the analysis of the evolution of cracks in the rail.

[1]  Simon Iwnicki,et al.  Handbook of railway vehicle dynamics , 2006 .

[2]  Ronald R. Coifman,et al.  Entropy-based algorithms for best basis selection , 1992, IEEE Trans. Inf. Theory.

[3]  R. Stock,et al.  The effects of top of rail friction modifier on wear and rolling contact fatigue : Full-scale rail-wheel test rig evaluation, analysis and modelling , 2008 .

[4]  I. Daubechies.  Orthonormal bases of compactly supported wavelets , 1988 .

[5]  I. Johnstone,et al.  Density estimation by wavelet thresholding , 1996 .

[6]  Wolfgang Schoch,et al.  CONTACT GEOMETRY AND SURFACE FATIGUE: MINIMIZING THE RISK OF HEAD- CHECK FORMATION.. , 2002 .

[7]  O Arias-Cuevas,et al.  A Method for the Prediction of Head Checking Initiation Location and Orientation under Operational Loading Conditions , 2010 .

[8]  C. Stein,et al.  Estimation with Quadratic Loss , 1992 .

[9]  I. Johnstone,et al.  Threshold selection for wavelet shrinkage of noisy data , 1994, Proceedings of 16th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[10]  Ronald R. Coifman,et al.  Wavelet analysis and signal processing , 1990 .

[11]  M. Takikawa,et al.  Laboratory simulations with twin-disc machine on head check , 2008 .

[12]  N. Tandon,et al.  A review of vibration and acoustic measurement methods for the detection of defects in rolling element bearings , 1999 .

[13]  Francis Franklin,et al.  Rolling contact fatigue and wear behaviour of the infrastar two-material rail , 2005 .

[14]  J. Byrnes.  Wavelets and their applications , 1994 .

[15]  Hans Muster,et al.  Rail rolling contact fatigue. The performance of naturally hard and head-hardened rails in track , 1996 .

[16]  Giovanni Battista Rossi,et al.  Wheel-flat diagnostic tool via wavelet transform , 2006 .

[17]  Michele Meo,et al.  A new damage detection technique based on wave propagation for rails , 2006 .

[18]  Masayasu Ohtsu,et al.  Acoustic Emission Testing , 2006, Advanced Materials Research.

[19]  Alfredo Cigada,et al.  Rail inspection in track maintenance: A benchmark between the wavelet approach and the more conventional Fourier analysis , 2007 .

[20]  H.A. Toliyat,et al.  Rail defect diagnosis using wavelet packet decomposition , 2002, Conference Record of the 2002 IEEE Industry Applications Conference. 37th IAS Annual Meeting (Cat. No.02CH37344).

[21]  David Dornfeld,et al.  Application of acoustic emission techniques in manufacturing , 1992 .

[22]  Stéphane Mallat,et al.  On denoising and best signal representation , 1999, IEEE Trans. Inf. Theory.

[23]  Roberto Roberti,et al.  The competitive role of wear and RCF in a rail steel , 2005 .