Third-order spectral characterization of acoustic emission signals in ring-type samples from steel pipes for the oil industry $

Abstract Third-order cumulant spectra are used to characterize acoustic emission events in ring-type samples from steel pipes for the oil industry. A cut segment of chord allows the coupling between a sample and the mechanical excitation device. Diagonal bi-spectrum allows the separation of the primary (original) deformation from the reflections produced mainly in the suppressed chord. These longitudinal reflections can hardly be extracted via second-order methods, e.g. wavelet packets and power spectra, because they are partially masked by both Gaussian and non-Gaussian noise. Sample registers were acquired by wide frequency-range transducers (100–800 kHz) and digitalized by a 2.5 MHz, 8-bit ADC.

[1]  J. Bendat,et al.  Random Data: Analysis and Measurement Procedures , 1971 .

[2]  A. Nandi.  Blind estimation using higher-order statistics , 1999 .

[3]  Lawrence E. Kinsler,et al.  Fundamentals of acoustics , 1950 .

[4]  C. L. Nikias,et al.  Signal processing with higher-order spectra , 1993, IEEE Signal Processing Magazine.

[5]  H. Vincent Poor,et al.  Fault Diagnostics Using Statistical Change Detection in the Bispectral Domain , 2000 .

[6]  I. G. Scott,et al.  Basic Acoustic Emission , 1990 .

[7]  David,et al.  A comparative experimental study on the use of acoustic emission and vibration analysis for bearing defect identification and estimation of defect size , 2006 .

[8]  Antolino Gallego,et al.  Coating adherence in galvanized steel assessed by acoustic emission wavelet analysis , 2005 .

[9]  D. Ponge,et al.  Improvement of the work hardening rate of ultrafine grained steels through second phase particles , 2005 .

[10]  Yoong-Ho Jung,et al.  A study of the characteristics of the acoustic emission signals for condition monitoring of check valves in nuclear power plants , 2006 .

[11]  M. Armeite,et al.  Identification of AE Bursts by Classification of Physical and Statistical Parameters , 2005 .

[12]  M. Hinich.  Detecting a transient signal by bispectral analysis , 1990 .

[13]  Juan Manuel Górriz,et al.  An Application of the Independent Component Analysis to Monitor Acoustic Emission Signals Generated by Termite Activity in Wood , 2004, ICINCO.

[14]  Stanislaw Osowski,et al.  Higher order statistics and neural network for tremor recognition , 2002, IEEE Transactions on Biomedical Engineering.

[15]  Juan Manuel Górriz,et al.  Higher-order statistics to detect and characterise termite emissions , 2004 .

[16]  Javier Rodríguez Fonollosa,et al.  Motion estimation using higher order statistics , 1996, IEEE Trans. Image Process..

[17]  Jerry M. Mendel,et al.  Tutorial on higher-order statistics (spectra) in signal processing and system theory: theoretical results and some applications , 1991, Proc. IEEE.

[18]  Antolino Gallego,et al.  Ti and Cr nitride coating/steel adherence assessed by acoustic emission wavelet analysis , 2005 .