Acoustic emission technique for leak detection in an end shield of a pressurised heavy water reactor

This paper discusses a successful application of the Acoustic Emission Technique (AET) for the detection and location of leak paths present on an inaccessible side of an end shield of a Pressurised Heavy Water Reactor (PHWR). The methodology was based on the fact that air- and water-leak AE signals have different characteristic features. Baseline data was generated from a sound end shield of a PHWR for characterising the background noise. A mock-up end shield system with saw-cut leak paths was used to verify the validity of the methodology. It was found that air-leak signals under pressurisation (as low as 3 psi) could be detected by frequency domain analysis. Signals due to air leaks from various locations of defective end shield were acquired and analysed. It was possible to detect and locate leak paths. The presence of detected leak paths was further confirmed by an alternative test.

Application of acoustic emission in drilling of composite laminates

Acoustic emission (AE) technique was used to characterise drilling of composite laminates. Uni-directional glass fibre reinforced plastic (GFRP) laminates consisting of 12-layers and 16-layers (0/90)(s) were drilled using a twist drill and the generated AE was monitored. Results of the investigations reveal that the complexion of the acoustic emission root mean square (AE-RMS) signal response changes from the drill entry to the exit thus giving an overall understanding about the different events that take place during drilling. Also, AE-RMS signal level increases with an increase in the applied thrust and further reveals that it is possible to evaluate the drill induced damages in composites through AE signal characterisation. (C) 2000 Elsevier Science Ltd. All rights reserved.

Evolution and decay of cylindrical and spherical nonlinear acoustic waves generated by a sinusoidal source

The present work gives a comprehensive numerical study of the evolution and decay of cylindrical and spherical nonlinear acoustic waves generated by a sinusoidal source. Using pseudospectral and predictor–corrector implicit finite difference methods, we first reproduced the known analytic results of the plane harmonic problem to a high degree of accuracy. The non-planar harmonic problems, for which the amplitude decay is faster than that for the planar case, are then treated. The results are correlated with the known asymptotic results of Scott (1981) and Enflo (1985). The constant in the old-age formula for the cylindrical canonical problem is found to be 1.85 which is rather close to 2, ‘estimated’ analytically by Enflo. The old-age solutions exhibiting strict symmetry about the maximum are recovered; these provide an excellent analytic check on the numerical solutions. The evolution of the waves for different source geometries is depicted graphically.

Narrowband Detection of Acoustic Source in Shallow Ocean using Vector Sensor Array

The problem of narrowband CFAR (constant false alarm rate) detection of an acoustic source at an unknown location in a range-independent shallow ocean is considered. If a target is present, the received signal vector at an array of N sensors belongs to an M-dimensional subspace if N exceeds the number of propagating modes M in the ocean. A subspace detection method which utilises the knowledge of the signal subspace to enhance the detector performance is presented in thisMpaper. It is shown that, for a given number of sensors N, the performance of a detector using a vector sensor array is significantly better than that using a scalar sensor array. If a target is detected, the detector using a vector sensor array also provides a concurrent coarse estimate of the bearing of the target.

Filter design using radial basis function neural network and genetic algorithm for improved operational health monitoring

The problem of denoising damage indicator signals for improved operational health monitoring of systems is addressed by applying soft computing methods to design filters. Since measured data in operational settings is contaminated with noise and outliers, pattern recognition algorithms for fault detection and isolation can give false alarms. A direct approach to improving the fault detection and isolation is to remove noise and outliers from time series of measured data or damage indicators before performing fault detection and isolation. Many popular signal-processing approaches do not work well with damage indicator signals, which can contain sudden changes due to abrupt faults and non-Gaussian outliers. Signal-processing algorithms based on radial basis function (RBF) neural network and weighted recursive median (WRM) filters are explored for denoising simulated time series. The RBF neural network filter is developed using a K-means clustering algorithm and is much less computationally expensive to develop than feedforward neural networks trained using backpropagation. The nonlinear multimodal integer-programming problem of selecting optimal integer weights of the WRM filter is solved using genetic algorithm. Numerical results are obtained for helicopter rotor structural damage indicators based on simulated frequencies. Test signals consider low order polynomial growth of damage indicators with time to simulate gradual or incipient faults and step changes in the signal to simulate abrupt faults. Noise and outliers are added to the test signals. The WRM and RBF filters result in a noise reduction of 54 - 71 and 59 - 73% for the test signals considered in this study, respectively. Their performance is much better than the moving average FIR filter, which causes significant feature distortion and has poor outlier removal capabilities and shows the potential of soft computing methods for specific signal-processing applications.

Correlation of acoustic and thermal anomalies in ferroelectric crystals

The Pippard-Janovec relations are derived for correlating the anomalous elastic coefficient and the anomalous specific heat near the phase transitions of ferroelectric crystals. These relations are verified in the case of ferroelectric triglycine selenate crystal.

Filter design using radial basis function neural network and genetic algorithm for improved operational health monitoring

The problem of denoising damage indicator signals for improved operational health monitoring of systems is addressed by applying soft computing methods to design filters. Since measured data in operational settings is contaminated with noise and outliers, pattern recognition algorithms for fault detection and isolation can give false alarms. A direct approach to improving the fault detection and isolation is to remove noise and outliers from time series of measured data or damage indicators before performing fault detection and isolation. Many popular signal-processing approaches do not work well with damage indicator signals, which can contain sudden changes due to abrupt faults and non-Gaussian outliers. Signal-processing algorithms based on radial basis function (RBF) neural network and weighted recursive median (WRM) filters are explored for denoising simulated time series. The RBF neural network filter is developed using a K-means clustering algorithm and is much less computationally expensive to develop than feedforward neural networks trained using backpropagation. The nonlinear multimodal integer-programming problem of selecting optimal integer weights of the WRM filter is solved using genetic algorithm. Numerical results are obtained for helicopter rotor structural damage indicators based on simulated frequencies. Test signals consider low order polynomial growth of damage indicators with time to simulate gradual or incipient faults and step changes in the signal to simulate abrupt faults. Noise and outliers are added to the test signals. The WRM and RBF filters result in a noise reduction of 54 - 71 and 59 - 73% for the test signals considered in this study, respectively. Their performance is much better than the moving average FIR filter, which causes significant feature distortion and has poor outlier removal capabilities and shows the potential of soft computing methods for specific signal-processing applications. (C) 2005 Elsevier B. V. All rights reserved.

Coupling of surface acoustic waves propagating in two separated piezoelectric media

The coupling of surface acoustic waves propagating in two separated piezoelectric media is studied using the perturbation theory of Auld. The results of the analysis are applied to two configurations using Bi12GeO20 and CdS crystals. It is found that the loss due to coupling is about 7 dB at 50 MHz in the cases of (111)-cut, [110]-prop. Bi12GeO20 and Y-cut, 60°-X prop. CdS combination. On étudie le couplage des ondes acoustiques de surface se propageant sur deux milieux piezo-eléctriques par la théorie de perturbation de Auld. Les resultats d'analyse sont appliqué's aux deux configurations des cristanx Bi12GeO20 et CdS. On trouve que la perte par couplage est environ de 7 dB a 50 MHz dans le cas de combination de (111)-coupe, [110]-prop. Bi12GeO20 et Y-coupe, 60°-X prop. CdS.

A study of the spectrum of an acoustic field in shallow water due to noise sources at the surface

An expression for the spectrum and cross spectrum of an acoustic field measured at two vertically separated sensors in shallow water has been obtained for any correlated noise sources distributed over the surface. Numerical results are presented for the case where the noise sources, white noise and wind-induced colored noise, are contained within a circular disk centered over the sensors. The acoustic field is generally inhomogeneous except when the channel is deep. The coherence function becomes real for a large disk, for a radius greater than 25 times the depth of the channel, decreases with further increase of the size of the disk, and finally tapers off after certain limiting size, approximately given by 1/alpha, where alpha is the attenuation coefficient.

On uniqueness, transfer and combination of acoustic sources in one-dimensional systems

The use of electroacoustic analogies suggests that a source of acoustical energy (such as an engine, compressor, blower, turbine, loudspeaker, etc.) can be characterized by an acoustic source pressure ps and internal source impedance Zs, analogous to the open-circuit voltage and internal impedance of an electrical source. The present paper shows analytically that the source characteristics evaluated by means of the indirect methods are independent of the loads selected; that is, the evaluated values of ps and Zs are unique, and that the results of the different methods (including the direct method) are identical. In addition, general relations have been derived here for the transfer of source characteristics from one station to another station across one or more acoustical elements, and also for combining several sources into a single equivalent source. Finally, all the conclusions are extended to the case of a uniformly moving medium, incorporating the convective as well as dissipative effects of the mean flow.

Ion-acoustic solutions and shock waves in multicomponent plasmas

The present investigation of ion-acoustic waves is based on the study of the nonlinearity of plasma waves in a dispersive medium. Here the authors study ion-acoustic solitary waves in a warm ion plasma with non-isothermal electrons and then the results for solitary waves in a plasma with isothermal electrons are obtained. Incorporating the previous results obtained from the solitary wave solutions, the authors generalize the effect of negative ions on ion-acoustic waves in plasmas consisting of either a warm or cold ion species. A reflection phenomenon of ions in these waves is also studied. These results can be generalized, but the discussion is limited to a particular model of the plasma.