Nearfield acoustical holography (NAH) is a useful tool for visualizing noise sources. However, to avoid spatial Fourier transform-related truncation effects, the measurement, or hologram, surface must extend beyond the source to a region where the sound pressure drops to a level significantly lower than the peak level within the measurement aperture. Statistically optimized nearfield acoustical holography (SONAH), first derived by Steiner and Hald in planar geometry, is based on a formulation similar to that of NAH. However, in SONAH, surface-to-surface projection of the sound field is performed by using a transfer matrix defined in such a way that all propagating waves and a weighted set of evanescent waves are projected with optimal average accuracy: i.e., no spatial Fourier transforms are performed. Thus the requirement that the measurement surface be extended is eliminated without compromising the accuracy of the procedure. In the present work, SONAH was re-formulated in cylindrical coordinates and was applied to the measurement of the sound field radiated by a refrigeration compressor. It was found that it is possible to visualize source regions accurately by using SONAH while using fewer measurement positions than would be required to achieve a similar level of accuracy when using conventional NAH procedures.
Small direct current (DC) motors are widely used due to their low cost and compact structure. Small DC motors of various designs are available on the market in different sizes. The smaller the motor, the more closely it may be used by individuals. Contrary to the size and simplicity of these motors in terms of structural design, sources of motor noise and vibration can be quite diverse and complicated. In this study, the source of motor noise and vibration was visualized over a very wide range of frequencies. The particle velocity of the motor was reconstructed from nearfield sound pressure measurements of motor noise. In addition to noncontact measurements conducted on a motor running at constant speed, the particle velocity of a stationary motor due to the impulse of an impact hammer was measured with an accelerometer. Furthermore, motor noise was measured under motor run-up conditions with different rotational speeds. As a result, by combination of these three methods, the sources of motor noise were accurately identified over a wide range of frequencies.
Phased array signal processing techniques such as beamforming have a long history in applications such as sonar for detection and localization of far-field sound sources. Two sometimes competing challenges arise in any type of spatial processing; these are to minimize contributions from directions other than the look direction and minimize the width of the main lobe. To tackle this problem a large body of work has been devoted to the development of adaptive procedures that attempt to minimize side lobe contributions to the spatial processor output. In this paper, two adaptive beamforming procedures-minimum variance distorsionless response and weight optimization to minimize maximum side lobes-are modified for use in source visualization applications to estimate beamforming pressure and intensity using near-field pressure measurements. These adaptive techniques are compared to a fixed near-field focusing technique ͑both techniques use near-field beamforming weightings focusing at source locations estimated based on spherical wave array manifold vectors with spatial windows͒. Sound source resolution accuracies of near-field imaging procedures with different weighting strategies are compared using numerical simulations both in anechoic and reverberant environments with random measurement noise. Also, experimental results are given for near-field sound pressure measurements of an enclosed loudspeaker.
Acoustical holography procedures make high-resolution visualization possible via estimation of the sound intensity on surfaces closer to the sources than the near-field measurement surface. Another source localization technique, beamforming, has been used to estimate the direction of arrival of sound from sources that typically lie in the far-field. However, little work has been done using beamforming as a visualization technique based on near-field measurements. As a result, the performance of beamforming and acoustical holography in terms of source resolution capabilities has not been directly compared when using near-field measurements. In this work, point source beamforming was used to visualize sources based on near-field measurements. Acoustic intensity estimated from beamformed pressure measurements was compared with the absolute intensity estimated using acoustical holography techniques. In addition to noise-free, anechoic simulations, cases of measurement pressure with random noise were generated and used to compare source resolution accuracy of acoustical holography and beamforming techniques in the presence of measurement noise. It was found that intensity estimated using acoustical holography provided the clearest image of sources when the measurement surface was conformal with the source geometry. However, sources can be resolved more accurately using near-field beamforming than acoustical holography at high frequencies when the sources are not located perfectly on a surface conformal with the measurement geometry.
Accurate sound visualization of noise sources is required for optimal noise control. Typically, noise measurement systems require microphones, an analog-digital converter, cables, a data acquisition system, etc., which may not be affordable for potential users. Also, many such systems are not highly portable and may not be convenient for travel. Handheld personal electronic devices such as smartphones and digital voice recorders with relatively lower costs and higher performance have become widely available recently. Even though such devices are highly portable, directly implementing them for noise measurement may lead to erroneous results since such equipment was originally designed for voice recording. In this study, external microphones were connected to a digital voice recorder to conduct measurements and the input received was processed for noise visualization. In this way, a low cost, compact sound visualization system was designed and introduced to visualize two actual noise sources for verification with different characteristics: an enclosed loud speaker and a small air compressor. Reasonable accuracy of noise visualization for these two sources was shown over a relatively wide frequency range. This very affordable and compact sound visualization system can be used for many actual noise visualization applications in addition to educational purposes.
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