Acoustic signature

A PLS regression model based on X (acoustic spectra from sensor A) and Y (crystallization temperature) was established. The X matrix contains 13 objects, each with 1024 variables (frequencies 0-25kHz). An overview of the X data is shown in Figure 9.8, in which one can observe systematic changes in the acoustic signatures following the object (samples) succession. 

The pilot study showed good prospects for predicting crystallization point temperatures directly from the acoustic signatures of the liquid feed into the granulator with an indicated prediction error (root mean square error of prediction) RMSEP = 1.1 °C. 

Acoustic emission from fluid flow through an orifice plate inserted in a pipeline contains a wealth of information, which can be used to predict, for example composition, flow or density [5]. Acoustic signatures from fluid flow are affected by several physical factors such as flow rate differential pressure over the orifice plate static pressure as well as chemical-physical factors - density, composition, viscosity. It is the objective of PLS modeling to extract the relevant features from the acoustic spectra and make use of these embedded signals in indirect multivariate calibration [1,2]. Several successful examples, including prediction of trace concentrations of oil in water, have been reported [5]. 

Acoustic emission (AE) is a technique that has been successfully employed to study fracture events in composites, where potentially, each failure mechanism has a unique acoustic signature (17-191. FE is another technique, which can be used in parallel with AE, and offers better sensitivity to the various microfracture processes. We have shown that interfacial failure between fiber and matrix in a composite produces significantly more intense emission and longer lasting decay... 

In real physical objects, in contrast with purely mathematical creations, selfsimilarity can only exist over a limited range of length scales, but this is a technical rather than a conceptual limitation. A more serious difficulty arises in recorded mechanical or acoustic signatures, such as the force-displacement or sound intensity-time relationships of brittle cellular foods, where the force or sound intensity is logged... 

After selection of the mission profile for the acoustic test was determined, NAS Patuxent River s Propulsion Department utilized their flight simulator and cycle deck for the engine to determine both aircraft and engine parameters needed to compute ground-based acoustic signatures. Table 4.2 lists all parameters needed to exercise the NASA LaRC systems noise prediction code ANOPP. In Table 4.2,... 

ARS [4] is a technique that can classify CW munitions by analyzing the effect that the fill has on the container s mechanical resonances. ARS accomplishes this classification by comparing a measured acoustic signature to a known standard or template. ARS does not measure physical characteristics of an object directly, but rather infers them by comparison to known items. 

The resonance spectrum, obtained by continuously exciting the object over a wide frequency range (sine-wave fi quency sweep) and measuring its response, provides an acoustic signature of the object. The measurements can be made with direct contact transducers, such as piezoelectric crystals, or with a non-contact setup using a speaker for excitation and a laser vibrometer for response measurement. Typicalfy, the frequency sweep range used for chemical munitions lies between 1 KHz and 30 KHz and the entire frequency sweep can be carried out in less than 60 seconds. 

The acoustic resonance spectrum thus contains the information necessary to determine the fill material by a comparative process. The ARS instrument measures the acoustic spectrum of the object and extracts the acoustic signature. These frequencies are compared to a library of signature templates. Each template represents a different potential fill material, for example, the chemical or high explosive fills that are foimd in artillery shells. 

The soimd radiated by the rotorcraft into its environment is what the critical observer perceives as noise pollution and what delivers a characteristic acoustic signature for aircraft detection and classification. The tjq)ical rotor-craft sound is composed of several components with destinctive directivity and intensity, depending on the flight conditions. In general, at a distance, the main rotor noise is dominant, the high frequency emissions of the tail rotor have some relevance, and the engine noise is secondary. 

Signature Recognition of Acoustic Emission from FRP Structures. 

A resonance in the layered stracture occurs when echoes between two boundaries travel back and forth due to differences in acoustic impedances at the boundaries. For multi-layer structures a number of resonances can be observed depending on their geometry and condition. For each particular defect-free structure and given transducer we obtain a characteristic resonance pattern, an ultrasonic signature, which can be used as a reference. 

Apart from inversions, there is another way to determine whether or not there is mixing in the Sun. Any spherically symmetric, localized sharp feature or discontinuity in the Sun s internal structure leaves a definite signature on the solar p-mode frequencies. Gough (1990) showed that changes of this type contribute a characteristic oscillatory component to the frequencies z/ / of those modes which penetrate below the localized perturbation. The amplitude of the oscillations increases with increasing severity of the discontinuity, and the wavelength of the oscillation is essentially the acoustic depth of the sharp-feature. Solar modes... 

Cox, B. N. and Addison, R. C. (1984). Modelling the acoustic material signature in the presence of a surface-breaking crack. In Review of progress in quantitative nondestructive evaluation (ed. D. O. Thompson and D. E. Chimenti), pp. 1173-84. Plenum Press, New York. [271]... 

Weglein, R. D. (1979a). A model for predicting acoustic materials signatures. Appl. 

Weglein, R. D. and Wilson, R. G. (1978). Characteristic materials signatures by acoustic microscopy. Electron. Lptt. 14, 352-4. [100]... 

Applicability of piezoelectric acoustic emission sensors to end-point determination has been studied since the beginning of this century.F l The technique is very promising, especially because it is non-invasive, sensitive, and relatively inexpensive. Granulation process signatures obtained with acoustic transducer can be used to monitor changes in particle size, flow, and... 

A nondestructive thermoacoustic material signature (TAMS) imaging method in scanning acoustic microscopy (SAM) was recently utilized to study the matrix and tempers signatures of ancient ceramics (90). 

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