This review introduces several areas of importance in acoustic emission (AE) technology, starting from signal attenuation. Signal loss is a critical issue in any large-scale AE monitoring, but few systematic studies have appeared. Information on damping and attenuation has been gathered from metal, polymer, and composite fields to provide a useful method for AE monitoring. This is followed by discussion on source location, bridge monitoring, sensing and signal processing, and pressure vessels and tanks, then special applications are briefly covered. Here, useful information and valuable sources are identified with short comments indicating their significance. It is hoped that readers note developments in areas outside of their own specialty for possible cross-fertilization.

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Review on Structural Health Evaluation with Acoustic Emission

A DEVICE FOR THE ACTIVATION OF ANAPPARATUS FOR MEASURING ACOUSTIC EMISSION BY DETECTION OF BACKGROUND NOISE Abstract of the Disclosure An apparatus for measuring acoustic emission is activated only at critical periods which are determined by detection of the background noise from a reference level. An acoustic emission transducer continuously transmits a signal to a detection circuit which delivers the top modulation envelope of the background noise to a comparator at a reference level delivered by a control unit. When the mean level of the background noise exceeds the reference level, the comparator delivers a signal which activates the input/output assembly of the measuring apparatus.

Device for the activation of an apparatus for measuring acoustic emission by detection of background noise

The need for improved methods of analysing acoustic emission signals and relating observed data to the original failure events is identified. The potential and limitations of current methods of frequency and amplitude analysis are discussed and some suggestions made. Mechanisms of transfer of energy from the source event via acoustic waves in the structure to electrical energy in the instrumentation are investigated, the theoretical relationships between various electrical and acoustic parameters are described, and the electrical and acoustic energies are related to the loss in stored strain energy by the failure event. A need for improved understanding of transducer response is demonstrated and a number of practical problems are discussed.

Acoustic emission parameters and their interpretation

The feasibility of using acoustic-emission techniques for the characterization of fracture resistance in AISI 4340 and SA533B steels is examined. The critical value of J-integral (JIC) is measured with a single small specimen (compact tension) loaded in the elastic-plastic range at room temperature. Initiation is detected during loading by acoustic emission. A new AE procedure for crack-growth monitoring in fracture-toughness specimens has been proposed. The possibility of discriminating AE signals from noncritical sources, such as void nucleation during crack-tip plastic deformation and signals from a growing crack, is discussed.

Acoustic-emission crack monitoring in fracture-toughness tests for AISI 4340 and SA533B steels: Purpose of this investigation was to examine the features of acoustic emission associated with the onset of subcritical crack extension and crack propagation

Author Adriano F de Almeida Title Neural Network Detection of Fatigue Crack Growth in Riveted Joints Using Acoustic Emission Institution Embry-Riddle Aeronautical University Degree Master of Science in Aerospace Engineering Year 1994 The purpose of this research was to demonstrate the capability of neural networks to discriminate between individual acoustic emission (AE) signals originating from crack growth and rivet rubbing (fretting) in aluminum lap joints AE waveforms were recorded during tensile fatigue cycling of six notched and riveted 7075-T6 specimens using a broadband piezoelectric transducer and a computer interfaced oscilloscope The source of 1,311 signals was identified based on triggering logic, amplitude relationships, and time of arrival data collected from the broad-band transducer and three additional 300 Hz resonant transducers bonded to the specimens The power spectrum of each waveform was calculated and normalized to correct for variable specimen geometry and wave propagation effects In order to determine the variation between individual signals of the same class, the normalized spectra were clustered onto a two-dimensional feature space using a Kohonen self organizing map (SOM) Then 132 crack growth and 137 rivet rubbing spectra were used to train a back-propagation neural network to provide automatic pattern classification Although there was some overlap between the clusters mapped in the Kohonen feature space, the trained back-propagation neural network was able to classify the remaining 463 crack growth signals with a 94% accuracy and the 367 rivet rubbing signals with a 99% accuracy

Neural network detection of fatigue crack growth in riveted joints using acoustic emission

A means for quickly and easily determining the broadband frequency content of acoustic bursts as short as 20 μs in duration has been developed using a video tape recorder and a standard spectrum analyzer. It is shown by examples from several tests on laboratory specimens and on large structures that the frequency content of an acoustic burst is related to the mechanism which produced it and is not affected substantially by the specimen size or by mode conversion due to multiple reflections in the structure. The frequency content of the burst can be changed in two ways, however: by the frequency-dependent attenuation of the propagation medium and in the cases where the medium is dispersive. Results of measurements on the effect of these factors in a variety of structures are given. Although acoustic emissions from many materials tend to be "white noise," several examples of acoustic emissions and exwaneous background noise bursts having distinctive frequency spectra are given which suggest possibilities for discriminating true acoustic emission signals from background noise on the basis of frequency content alone.

LJ Graham

Papers

Acoustic Emission in the Frequency Domain