J.E. Ruzzante

Higher Order Statistics and Independent Component Analysis for Spectral Characterization of Acoustic Emission Signals in Steel Pipes

Higher order statistics (HOS) are used to characterize acoustic emission events in ring-type samples from steel pipes for the oil industry. Cumulants are used twofold. First, diagonal bispectrum 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. Second, a cumulant-based independent component analysis may be used before the bispectrum, as a preprocessing complement, in the case of multiple-source and multiple-channel recordings. This algorithm suppresses the mutual influence of the sources and sensors. Sample registers were acquired by wide-frequency-range transducers (100-800 kHz) and digitalized by a 2.5-MHz, 12-bit analog-to-digital converter.

Relation between Amplitude and Duration of Acoustic Emission Signals

The correlation between A and D in the AE signals coming from deformation tests in steel samples and explosion tests in Zry tubes is analysed. The graph for the logarithm of relative Amplitude versus Duration of AE signals is a band between two slightly convex lines. If each burst is a simple event a unique straight line is expected. Following a previous model of our Group, bursts were modelled as superposition of a number of events.

Higher order analysis of acoustic emission signals

Bispectrum enhances the identi£cation of echoes in acous- tic emission (AE) events occurring in samples from steel pipes under transversal strain. Diagonal bispectra (a time-varying version is sug- gested) allow the separation of the primary event and the secondary ones (echoes). Distorted reections contain frequency components revealing non-linearities which characterize the reecting surfaces. AE sequences were acquired by a wide frequency range transducer (100-800 kHz) and digitalized by a 2.5 MHz 8-bit ADC.

Identification of AE Bursts by Classification of Physical and Statistical Parameters

Physical and statistical parameters obtained with the Principal Components method, extracted from Acoustic Emission bursts coming from triaxial deformation tests were analyzed. The samples came from seamless steel tubes used in the petroleum industry and some of them were provided with a protective coating. The purpose of our work was to identify bursts originated in the breakage of the coating, from those originated in damage mechanisms in the bulk steel matrix. Analysis was performed by statistical distributions, fractal analysis and clustering methods.

Barkhausen Effect and Acoustic Emission in a Metallic Glass—Preliminary Results

Magneto Acoustic Emission, which is Barkhausen Noise (BN) and Acoustic Emission (AE), depends on microstructure and existing residual stresses in magnetic materials. Preliminary results obtained by magnetization along two perpendicular directions on a metal glass foil are presented. Signals were analyzed with Statistic, Fast Fourier and Wavelet methods. Results are part of a Joint Research Project of the Faculty of Science, Cantabria University, Spain, and the Elastic Waves Group of the National Atomic Energy Commission, Argentina.

ACOUSTIC EMISSION MEASUREMENTS FOR TOOL WEAR EVALUATION IN DRILLING

In this work, the tool condition in a drilling process of SAE 1040 steel samples was studied by means of acoustic emission. The studied drill bits were modified with artificial and real failures, such as different degrees of wear in the cutting edge and in the outer corner. Some correlation between mean power of the acoustic emission parameters and the drill bit wear condition was found.

Ruido Barkhausen y emisión magneto acústica para la caracterización de materiales ferromagnéticos

The techniques of Barkhausen Noise (BN) and Magneto Acoustic Emission (MAE) were applied to a ferromagnetic material with the objective of correlating the obtained signals with the micro structural changes. The first step was the development of an Integral Measurement System (IMS) for the BN and MAE signals. Different devices as magnetic yoke, current source, amplifiers, frequency filters and different magnetic sensors were constructed. The first measurements were developed on an AISI 1020 steel, with low carbon content, submitted to different thermo mechanical treatments. One of the test probe was used as standard, the others were subjected to surface decarburation, grain growth and plastic deformation. The effects of radiation damage were also studied with BN and MAE. Other two smaller test pieces were prepared. The irradiated specimen was subject to a low flow of fast neutrons (3.6 1015 n/cm2) and the other, was used as standard. In all cases the result were relevant. In particular, the RMS value of the BN and MAE signals resulted as an appropriated parameter whose variation followed adequately the microstuctural changes. The changes and the peaks in the signals are owed to the anchors of the magnetic domain walls. The present paper lead to recommend this IMS as a valuable tool in No Destructive Testting (NDT) for ferromagnetic materials, all times the system be previously calibrated with a standard probe.

Wavelet Transform and Bispectrum Applied to Acoustic Emission Signals from Adherence Scratch-Tests on Corroded Galvanized Coatings

Wavelet analysis and bispectrum was applied to Acoustic Emission (AE) signals from scratch tests on corroded hot-dip galvanized samples in order to achieve the detection of corrosion products in pieces non reachable by visual inspection. AE signals were correlated with the fracture mechanisms occurring during scratch tests, while the contact force increased. Results were corroborated by Scanning Electron Microscopy (SEM), Energy Dispersive X-ray spectroscopy (EDX) and X-Ray Diffraction (XRD).

Higher-order spectra and independent component analysis used for identification and SNR enhancement of acoustic emission signals

Higher order statistics are used twofold: to characterize, and to enhance and to de-noise primary signals and their echoes in acoustic emission events occurring in samples of steel pipes under transversal strain. First, the characterization by diagonal bi-spectra is performed and it allows the spectral separation of the primary event from the secondary ones (echoes). Secondly, a cumulant-based independent component analysis is applied for blind sources separation in a low-SNR scenario. The method is first validated considering a synthetic of acoustic signals. Then, the developed algorithm is applied to a sequence of quartets of primary bursts and their first three echoes. The denoising capability of ICA is assessed by comparing the power spectra of the sources vs. the separated signals. Data were acquired by wide frequency-range transducers (100-800 kHz) and digitalized by a 2.5 MHz, 8-bit ADC

Statistical analysis of Acoustic Emission events generated from a welded joint

The acoustic emission events generated in a defective welding were studied. A cap was welded at the end of a high resistance steel oil pipe. Three acoustic emission transducers were distributed on the tube. The different parameters of the events produced at different pressures (amplitude, duration and rise-time) were analyzed. Some sets of different characteristics for each group were found.