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American Gear Manufacturers Association 2022 Fall Technical Meeting, FTM 2022

作者： Wagner, Matthew Mark, William D. Isaacson, Aaron Applied Research Laboratory Pennsylvania State University United States Applied Research Laboratory Graduate Program in Acoustics Pennsylvania State University United States

ISBN: (纸本)9781713862246

The ability to detect the onset of a gear system failure via accelerometer measurements is of interest in a research environment as well as in gear systems deployed in the field. The goal in either case is to detect a failure as early as possible while simultaneously minimizing the risk of false shutdowns. The accelerometer based gear health monitoring system described herein was developed for use in a laboratory setting for monitoring power recirculating gear tests. In this environment it is desirable to detect the early onset of failures so the primary failure mode as well as the time to failure can be accurately determined. Minimizing false shutdowns is also critical to maximizing rig uptime and testing efficiency. The signal processing algorithm uses even-angle resampling along with time synchronous averaging to minimize the influence of vibration sources other than the gear mesh. In the case of hunting tooth ratio gearsets it is shown that it is possible to completely separate the damage response of each gear in the pair from a single accelerometer signal using these techniques. The average log ratio (ALR) algorithm is then employed to monitor the time synchronous averaged signals for the onset of damage. A summary of these signal processing concepts is given, along with an overview of system hardware, signal processing workflow, and sample data. Copyright ©2022.

关键词： Accelerometers

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Acoustic source separation of transient events from background noise 31

Acoustic source separation of transient events from backgrou...

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31st International Conference on Noise and Vibration Engineering, ISMA 2024 and 10th International Conference on Uncertainty in Structural Dynamics, USD 2024

作者： Paprocki, C. Barnard, A. Dare, T. Cody, K. Newman, P. Penn State Graduate Program in Acoustics 201 Applied Science Building University ParkPA16802 United States Naval Nuclear Laboratory 814 Pittsburgh McKeesport Blvd West MifflinPA15122 United States Penn State Recreation Park and Tourism Management 201 Applied Science Building University ParkPA16802 United States

ISBN: (纸本)9789082893175

The United states National Park Service relies heavily on acoustic monitoring to assess the ecological health of its areas and quantify the impact of anthropogenic sound on protected wilderness. Historically the evaluation of the acoustic data is done by human listeners analyzing spectrograms while listening to audio data. By implementing machine learning, future research aims to increase the data throughput volume while improving the annotation accuracy of the acoustic sources. The aim of this paper is to evaluate various signal processing techniques and determine their feasibility in separating transient acoustic events from background noise of the environment. This research aims to separate all transient sources from jet aircraft to bird calls from the ambient sound, this work serves as a framework to validate the accuracy of data labels during the curation of a training dataset. © 2024 Proceedings of ISMA 2024 - International Conference on Noise and Vibration Engineering and USD 2024 - International Conference on Uncertainty in Structural Dynamics. All rights reserved.

关键词： Background noise

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Photoacoustic measurement of the optical absorption of aerosols 182

Photoacoustic measurement of the optical absorption of aeros...

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182nd Meeting of the Acoustical Society of America, ASA 2022

作者： Case, John Arthur Smith, Robert W. Graduate Program in Acoustics The Pennsylvania State University University ParkPA16802 United States Applied Research Laboratory The Pennsylvania State University State CollegePA16801 United States

Atmospheric optical absorption, including the contribution from aerosols, is important in modeling propagation of laser beams and for climate research. Photoacoustic methods have been used in the past for this purpose, as they directly measure absorption, rather than the sum of absorption and scattering. Acoustic operating frequency determines the sensed aerosol size range. The design of a windowless double-Helmholtz photoacoustic aerosol sensing system, operating at an acoustic frequency near 150 Hz, with the capability to measure an optical absorption with a 1/e length of 1 Mm, will be presented. In this design, environmental noise limits the instrument sensitivity. To improve immunity to environmental noise with an open resonator, an aluminum acoustic enclosure was built around the system, with welded vacuum flanges for inputs and outputs. Inlet and outlet acoustic mufflers were designed to limit the in-band noise entering the measurement system. A modulated 10 W 1064 nm fiber laser was used as the excitation source, and a high-sensitivity microphone placed within the open-cell cavity was used to measure the internal photoacoustic pressure fluctuations. The system was modeled using various computational tools, including MATLAB, Simulink, and ANSYS, and then was constructed and tested. Lab measurement results will be presented. © 2022 Acoustical Society of America.

关键词： Light absorption

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Learning-Based Tone Mapping to Improve 3D SAS ATR

Learning-Based Tone Mapping to Improve 3D SAS ATR

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Gregory D. Vetaw Benjamin Cowen Daniel C. Brown David P. Williams Suren Jayasuriya School of Electrical Computer and Energy Engineering Arizona State University The Applied Research Laboratory Graduate Program in Acoustics Pennsylvania State University

Automatic target recognition (ATR) for 3D synthetic aperture sonar (SAS) imagery is an intrinsic challenge in highly cluttered ocean environments, especially for objects partially or completely buried in the sediment. Conventional dynamic range compression (DRC) techniques such as log-compression, which is a type of tone mapping intended to appeal to the human visual system, can further obscure the sonar signatures of these already physically occluded objects and lead to suboptimal downstream ATR performance, particularly for convolutional neural networks (CNNs). In this paper, we present a novel machine learning-based approach for tone mapping sub-bottom SAS imagery as a pre-processing stage in the 3D SAS ATR pipeline. This learned tone mapping function can be jointly optimized with a CNN-based ATR algorithm. We train and validate our method on measured volumetric SAS data captured by the Sediment Volume Search Sonar (SVSS) system.

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Design, Fabrication, and Testing of Stiffened Composite Panels with Acoustically Tailored Stiffeners 79

Design, Fabrication, and Testing of Stiffened Composite Pane...

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79th Vertical Flight Society Annual Forum and Technology Display, FORUM 2023

作者： Brown, Avery Moorhouse, Anna M. Bakis, Charles E. Smith, Edward C. Beck, Ben S. Shepherd, Micah R. Engineering Science and Mechanics Dept. Pennsylvania State Univ. University ParkPA United States Graduate Program in Acoustics Pennsylvania State Univ. University ParkPA United States Aerospace Engineering Dept. Pennsylvania State Univ. University ParkPA United States Engineering Acoustics Dept. Applied Research Laboratory State CollegePA United States Physics and Astronomy Dept. Brigham Young Univ. ProvoUT United States

ISBN: (纸本)9781713874799

Carbon fiber reinforced epoxy composites are being increasingly used for load bearing structural elements in large transport rotorcraft. However, due to the increased stiffness-to-density ratio of composites, problems associated with vibration and interior noise can be exacerbated by the use of composites in airframes. The current investigation explores the potential of optimized composite meta-structures to reduce vibration and interior noise in rotorcraft. The approach involves designing a taper into the thickness of blade stiffeners which, when combined with a limited amount of absorbing material in the thinned region, creates what is known as an "acoustic black hole" (ABH) in the stiffener. It is hypothesized that optimally designed ABH stiffeners can reduce broadband vibrations in stiffened panels. An optimization routine has been developed to determine the tradeoffs between vibration, mass, and buckling load due to compression parallel to the stiffeners. The tradeoffs are visualized using a 3-D Pareto front that helps the designer decide on the optimal design. Carbon/epoxy panels were made using vacuum-bag-oven processing with out-of-autoclave prepreg and verified to be of good quality. Panel vibration, buckling onset, and post-buckling load-deflection behavior were simulated using finite elements and measured using model testing and compression testing. Based on preliminary results, ABH-tapered stiffened panels have the ability to reduce radiated noise without a significant penalty in the mass and compressive load-bearing capability. Copyright © 2023 by the Vertical Flight Society. All rights reserved.

关键词： Optimal systems

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Quantifying sound transmission of building structures for optimization in early-stage design 50

Quantifying sound transmission of building structures for op...

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50th International Congress and Exposition of Noise Control Engineering, INTER-NOISE 2021

作者： Broyles, Jonathan M. Shepherd, Micah R. Brown, Nathan C. Department of Architectural Engineering Pennsylvania State University 104 Engineering Unit A University ParkPA United States Applied Research Laboratory Graduate Program in Acoustics Pennsylvania State University 201 Applied Science Building University ParkPA United States

ISBN: (纸本)9781732598652

Technological advancements in computational building modeling have enabled designers to conduct simulations at the component level. With the evolution of parametric modeling, designers can evaluate multiple design options and identify the best performing solutions before expending resources on physical testing and eventual construction. However, to conduct design space exploration or optimization, an objective function is necessary to evaluate performance. While defined objectives exist for design goals such as sustainability, energy usage, structural performance, and daylighting, there does not yet exist an encompassing objective that can accurately quantify the sound transmission of structures in building design. This paper proposes four transmission rating strategies that can describe air-borne and impact performance of floors, walls, or other acoustic barriers. The approaches consider air-borne and structure-borne sound for four floor constructions, revealing strengths and weaknesses across the methods. Implementation of the approaches in an optimization framework is discussed, along with practical methods for optimizing the objectives in early-stage design. © INTER-NOISE 2021 .All right reserved.

关键词： Floors

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SINR: Deconvolving Circular SAS Images Using Implicit Neural Representations

arXiv

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arXiv 2022年

作者： Reed, Albert Blanford, Thomas Brown, Daniel C. Jayasuriya, Suren The School of Electrical Computer and Energy Engineering Arizona State University United States The Applied Research Laboratory United States The Applied Research Laboratory Graduate Program in Acoustics Pennsylvania State University United States The School of Arts Media and Engineering The School of Electrical Computer and Energy Engineering Arizona State University United States

Circular synthetic aperture sonars (CSAS) capture multiple observations of a scene to reconstruct high-resolution images. We can characterize resolution by modeling CSAS imaging as the convolution between a scene's underlying point scattering distribution and a system-dependent point spread function (PSF). The PSF is a function of the system bandwidth and determines a fixed degree of blurring on reconstructed imagery. In theory, deconvolution overcomes bandwidth limitations by reversing the PSF-induced blur and recovering the scene's scattering distribution. However, deconvolution is an ill-posed inverse problem and sensitive to noise. We propose an optimization method that leverages an implicit neural representation (INR) to deconvolve CSAS images. We highlight the performance of our SAS INR pipeline, which we call SINR, by implementing and comparing to existing deconvolution methods. Additionally, prior SAS deconvolution methods assume a spatially-invariant PSF, which we demonstrate yields subpar performance in practice. We provide theory and methods to account for a spatially-varying CSAS PSF, and demonstrate that doing so enables SINR to achieve superior deconvolution performance on simulated and real acoustic SAS data. We provide code (click here) to encourage reproducibility of research. © 2022, CC BY.

关键词： Optical transfer function

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A 3D GAN Architecture for Volumetric Synthetic Aperture Sonar

A 3D GAN Architecture for Volumetric Synthetic Aperture Sona...

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OCEANS

作者： Greg D. Vetaw Albert Reed Daniel C. Brown Suren Jayasuriya School of Electrical Computer and Energy Engineering Arizona State University The Applied Research Laboratory Graduate Program in Acoustics Pennsylvania State University

ISBN: (纸本)9781665427883

Synthetic aperture sonar (SAS) is used extensively in underwater imaging for visualizing the seafloor and objects present on it. However, processing SAS images can be time-consuming and tedious, with machine learning techniques being ineffective due to the lack of available data. In particular, automated target recognition (ATR) with 3D SAS data for machine learning is challenging in many ways due to the complexity with working with 3D volumetric data. Recently, researchers have introduced generative adversarial networks (GANs) to help perform 2D SAS image generation for data augmentation. Following this line of work in this paper, we introduce a 3D-GAN architecture to generate photorealistic 3D SAS data which matches the fidelity of real data. In particular, we discuss novel latent space sampling and normalization to help 3D GANs overcome mode collapse for generating volumetric SAS information. Experimental results are shown on real 3D SAS data, showing the potential of using 3D GANs for dataset augmentation in the future.

关键词： Three-dimensional displays Target recognition Image synthesis Sea floor Imaging Data visualization Machine learning

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Implicit Neural Representations for Deconvolving SAS Images

Implicit Neural Representations for Deconvolving SAS Images

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OCEANS

作者： Albert Reed Thomas Blanford Daniel C. Brown Suren Jayasuriya School of Electrical Computer and Energy Engineering Arizona State University The Applied Research Laboratory Pennsylvania State University Graduate Program in Acoustics Pennsylvania State University

ISBN: (纸本)9781665427883

Synthetic aperture sonar (SAS) image resolution is constrained by waveform bandwidth and array geometry. Specifically, the waveform bandwidth determines a point spread function (PSF) that blurs the locations of point scatterers in the scene. In theory, deconvolving the reconstructed SAS image with the scene PSF restores the original distribution of scatterers and yields sharper reconstructions. However, deconvolution is an ill-posed operation that is highly sensitive to noise. In this work, we leverage implicit neural representations (INRs), shown to be strong priors for the natural image space, to deconvolve SAS images. Importantly, our method does not require training data, as we perform our deconvolution through an analysis-by-synthesis optimization in a self-supervised fashion. We validate our method on simulated SAS data created with a point scattering model and real data captured with an in-air circular SAS. This work is an important first step towards applying neural networks for SAS image deconvolution.

关键词： Deconvolution Oceans Neural networks Training data Scattering Bandwidth Image restoration

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Implicit Neural Representations for Deconvolving SAS Images

arXiv

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arXiv 2021年

作者： Reed, Albert Blanford, Thomas Brown, Daniel C. Jayasuriya, Suren School of Electrical Computer and Energy Engineering Arizona State University United States The Applied Research Laboratory United States Graduate Program in Acoustics Pennsylvania State University United States

关键词： Optical transfer function

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