Fiber-optic interferometric acoustic sensors were first proposed for US Navy applications 36 years ago. This paper will review the origin, development and deployment of these sensors. Future applications will also be discussed.
We present progress we have made in developing a structural acoustic-based methodology allowing interior fault detection and localization in plate-like structures using only processed vibration data readily available on the structure's surface. Our methods use measurements of surface displacement associated with vibration of the structure caused by externally applied forces. These forces can be created simply by a local actuator in direct contact with the structure or in some cases by an incident airborne acoustic wave. The measured normal surface displacements, uz(x, y), are then inverted locally using various mathematically optimized algorithms in order to obtain a desired material parameter, for example, the elastic modulus, whose spatial variation then serves to detect and localize the fault. This structural acoustic approach is not limited to any particular length scale requiring only that the structure be mechanically excited at frequencies for which the structural wavelength is within an order of magnitude of the fault dimension and that the dynamic surface displacements be mapped with a spatial resolution smaller than the fault size. We present the results of deploying the structural acoustic technique in the US Capitol Building to locate faults within plaster walls and ceilings bearing large expanses of precious nineteenth century frescoes, in composite airframe skins in laboratory experiments to detect and locate de-bonding of thin (~1mm) stiffeners and frames, and in micro-structures to detect and locate faults in silicon micro-oscillators and their supporting structures with resolutions approaching 1μm.
In this paper, we investigate the feasibility of both detecting and localizing inclusions in structures given a knowledge of dynamic surface displacements. Provided with such displacement information, the equations of motion are utilized to estimate local material parameters through inversion, as well as to indicate the locations of inclusions using a novel generalized force mapping technique. Using a finite element code, numerical simulations were carried out for the determination of the normal surface displacements for both steel and mortar rectangular plates subject to monochromatic point actuation. The data is generated for both homogeneous plates and inhomogeneous plates within which a small rectangular inclusion of differing material parameters is present, and three algorithms are applied to the calculated displacement data. The first two are local inversion techniques which provide a spatial map of the elastic modulus normalized by density, while the third technique utilizes the inhomogeneous form of the equations of motion to obtain an induced force distribution caused by the inclusion. It will be demonstrated that the algorithms can both detect and locate inclusions in structures even when the materail parameter difference of the inclusions and the background medium is relatively low.
A large-scale survey (~700 m2) of frescos and wall paintings was undertaken in the U.S. Capitol Building in Washington, D.C. to identify regions that may need structural repair due to detachment, delamination, or other defects. The survey encompassed eight pre-selected spaces including: Brumidi's first work at the Capitol building in the House Appropriations Committee room; the Parliamentarian's office; the House Speaker's office; the Senate Reception room; the President's Room; and three areas of the Brumidi Corridors. Roughly 60% of the area surveyed was domed or vaulted ceilings, the rest being walls. Approximately 250 scans were done ranging in size from 1 to 4 m2. The typical mesh density was 400 scan points per square meter. A common approach for post-processing time series called Proper Orthogonal Decomposition, or POD, was adapted to frequency-domain data in order to extract the essential features of the structure. We present a POD analysis for one of these panels, pinpointing regions that have experienced severe substructural degradation.
A wave-based matching-pursuits algorithm is used to parse multi-aspect time-domain backscattering data into its underlying wavefront-resonance constituents, or features. Consequently, the N multi-aspect waveforms under test are mapped into N feature vectors, yn. Target identification is effected by fusing these N vectors in a maximum-likelihood sense, which we show, under reasonable assumptions, can be implemented via a hidden Markov model (HMM). In this paper, we utilize a continuous-HMM paradigm, and compare its performance to its discrete counterpart. Algorithm performance is assessed by considering measured acoustic scattering data from five similar submerged elastic targets.
The Naval Research Laboratory is using its world-renowned structural acoustics facilities (originally developed for scaled submarine programs) to study the broad band (1 - 150 kHz) acoustic scattering from proud and buried underwater mines. The objective is to discover what information is contained in the broad-band properties of the scattered signal which might be exploited for target identification purposes. Current acoustic mine-hunting systems form acoustic images that replicate the rough geometric shape of the target. To obtain sufficient resolution, these systems must operate at frequencies that are too high for anything but time-consuming, close-in looks at the target. Even then, they often confuse mines with mine-like targets such as oil drums. In contrast, structural acoustic clues such as mine resonances, elastic wave propagation, internal structure scattering, etc., are available at lower frequencies (1 - 10 kHz), allowing for much longer ranges of operation as well as the construction of unique 'fingerprints' by which to identify the target as a mine. Additionally, at lower frequencies the ocean sediment is more readily penetrated by acoustic waves, creating the possibility for buried mine detection. This paper examines the feasibility of exploiting such very low frequency structural acoustic clues for long range identification of proud and buried mines.
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