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All sources NASA NTRS DOE OSTI

DOE OSTI2023

Recursive Use of the Short-Time Fast Fourier Transform for Signature Analysis in Continuous Processes

Although a nuclear reactor is a hostile environment for sensing and electrical communications, the reactor core is amenable to acoustic communication. An acoustic measurement infrastructure (AMI) has been installed in the Advanced Test Reactor (ATR) to record acoustic signals that can capture its different operating regimes. AMI uses coolant pumps as continuous signal sources, coolant and structural components as transmission lines, and accelerometers to capture system motion. A recursive signal processing technique based on the short-time fast Fourier transform (STFFT) for continuous processes provides unique signatures for diagnostic and prognostic analyses from the system motion data. Here this article presents a recursive STFFT methodology that processes acoustic signals from continuous industrial processes. The article first discusses the initial STFFT use with simulated data to elucidate the basic principles necessary to understand and interpret the STFFT results from actual pump vibration data. Each repetitive use of the STFFT on pump vibration data using the results from the prior STFFT processing will generate additional complimentary time-frequency-based signatures. These signatures are generated by the coolant pumps operating under different process conditions. After each use of the STFFT, the resulting signatures provide exemplary examples of the diversity and intuitive nature of recursively using the STFFT. This article focuses on recursively using the STFFT to provide numerous complimentary and diverse signatures that will ultimately be inputs for machine learning algorithms that provide predictive data analytics. The intuitive nature of the information and signatures from recursive STFFT processing will also bring intuitive interpretation capabilities to machine learning and predictive data analytic techniques.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2023

Coolant Pump Predictive Data Analytics from Signatures Generated by the Recursive Short Time Fast Fourier Transform

Although a nuclear reactor is a hostile environment for sensors and signal transmissions, the reactor core is amenable to acoustic communication. An acoustic measurement infrastructure installed at the Advanced Test Reactor (ATR) nozzle trench area records acoustic signals that can capture reactor operating states. The distinct states produce unique signatures that can be identified and tracked using data processing and data analytics. The infrastructure relies on acoustic transmission through ATR in-pile structural components, piping, and coolant that transmit acoustically modified signals generated by the coolant pumps. This paper will discuss results from using the Recursive Short Time Fast Fourier Transform (RSTFFT) technique used to process acoustic signals and provide signatures that are identified and monitored by analytics. The RSTFFT is applied to ATR data to understand the vibration levels and signatures for different operating regimes as displayed by the spectrogram. The combination of coolant pumps for normal and high-power operation generate unique signatures. These acoustic signatures are used to develop machine learning approaches to automatically classify operating regimes. Two machine-learning models, Support Vector Machines and Linear Discriminant Analysis, were developed to classify two event classes. Class 1 is a normal steady-state operation, and Class 2 is any event that is due to start up, shut down, or other actions. Both types of machine learning models had over a 96% prediction accuracy for the two classes. These results lay the foundation for predictive analytic frameworks that can be leveraged by ATR to optimize operations and maintenance.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2026

On-Chip Cavity Electroacoustics Using Lithium Niobate Phononic Crystal Resonators

Mechanical systems are pivotal in quantum technologies because of their long coherent time and versatile coupling to qubit systems. So far, the coherent and dynamic control of gigahertz-frequency mechanical modes mostly relies on optomechanical coupling and piezoelectric coupling to superconducting qubits. Here, we demonstrate on-chip cavity electroacoustic dynamics using our microwave-frequency electrically modulated phononic crystal resonators on lithium niobate. Leveraging the high dispersion of phononic crystal, our acoustic modes space unevenly in the frequency spectrum, emulating atomic energy levels. Atomiclike transitions between different acoustic modes are selectively achieved by applying electrical fields to modulate acoustic modes via the nonlinear piezoelectricity of lithium niobate. Among two modes, we demonstrate Autler-Townes splitting, ac Stark shift, and Rabi oscillation with a maximum cooperativity of 4.18. Extending to three modes, we achieve nonreciprocal frequency conversions with an isolation up to 20 dB. Nonreciprocity can be tuned by the time delay between the two modulating pulses. Finally, our cavity electroacoustic platform could find broad applications in sensing, microwave signal processing, acoustic computing, and quantum acoustics.

Rabi model↗

DOE OSTI2022

Contactless excitation of acoustic resonance in insulating wafers

Contactless excitation and detection of high harmonic acoustic overtones in a thin insulator single crystal are described using radio frequency spectroscopy techniques. Single crystal [001] silicon wafer samples were investigated, one side covered with a Nb thin film, the common starting point for the fabrication of quantum devices. Additionally, the coupling between electromagnetic signals and mechanical oscillation is achieved from the Lorentz force generated by an external magnetic field. This method is suitable for any sample with a metallic surface or covered with a thin metal film. High resolution measurements of the temperature dependence of the sound velocity and elastic constants of silicon are reported and compared with known results.

47 OTHER INSTRUMENTATION↗

NASA NTRS2010

Speech Acquisition and Automatic Speech Recognition for Integrated Spacesuit Audio Systems

A voice-command human-machine interface system has been developed for spacesuit extravehicular activity (EVA) missions. A multichannel acoustic signal processing method has been created for distant speech acquisition in noisy and reverberant environments. This technology reduces noise by exploiting differences in the statistical nature of signal (i.e., speech) and noise that exists in the spatial and temporal domains. As a result, the automatic speech recognition (ASR) accuracy can be improved to the level at which crewmembers would find the speech interface useful. The developed speech human/machine interface will enable both crewmember usability and operational efficiency. It can enjoy a fast rate of data/text entry, small overall size, and can be lightweight. In addition, this design will free the hands and eyes of a suited crewmember. The system components and steps include beam forming/multi-channel noise reduction, single-channel noise reduction, speech feature extraction, feature transformation and normalization, feature compression, model adaption, ASR HMM (Hidden Markov Model) training, and ASR decoding. A state-of-the-art phoneme recognizer can obtain an accuracy rate of 65 percent when the training and testing data are free of noise. When it is used in spacesuits, the rate drops to about 33 percent. With the developed microphone array speech-processing technologies, the performance is improved and the phoneme recognition accuracy rate rises to 44 percent. The recognizer can be further improved by combining the microphone array and HMM model adaptation techniques and using speech samples collected from inside spacesuits. In addition, arithmetic complexity models for the major HMMbased ASR components were developed. They can help real-time ASR system designers select proper tasks when in the face of constraints in computational resources.

Huang, Yiteng↗

NASA NTRS1992

Signal processing methodologies for an acoustic fetal heart rate monitor

Research and development is presented of real time signal processing methodologies for the detection of fetal heart tones within a noise-contaminated signal from a passive acoustic sensor. A linear predictor algorithm is utilized for detection of the heart tone event and additional processing derives heart rate. The linear predictor is adaptively 'trained' in a least mean square error sense on generic fetal heart tones recorded from patients. A real time monitor system is described which outputs to a strip chart recorder for plotting the time history of the fetal heart rate. The system is validated in the context of the fetal nonstress test. Comparisons are made with ultrasonic nonstress tests on a series of patients. Comparative data provides favorable indications of the feasibility of the acoustic monitor for clinical use.

Pretlow, Robert A., III↗

NASA NTRS1973

Acoustic-emission signal-processing analog unit for locating flaws in large tanks

Technique monitors structural flaws in 105-in. diameter tanks. Tank surface is divided into many areas and each area is sectioned into 20 equilateral triangles that form icosahedron. Twelve transducers are equally positioned on tank surface at vertex of each triangle. Transducers monitor area for flaws by detecting any increase in acoustical activity.

Moskal, F. J.↗

NASA NTRS2014

Acoustic Data Processing and Transient Signal Analysis for the Hybrid Wing Body 14- by 22-Foot Subsonic Wind Tunnel Test

An advanced vehicle concept, the HWB N2A-EXTE aircraft design, was tested in NASA Langley's 14- by 22-Foot Subsonic Wind Tunnel to study its acoustic characteristics for var- ious propulsion system installation and airframe con gurations. A signi cant upgrade to existing data processing systems was implemented, with a focus on portability and a re- duction in turnaround time. These requirements were met by updating codes originally written for a cluster environment and transferring them to a local workstation while en- abling GPU computing. Post-test, additional processing of the time series was required to remove transient hydrodynamic gusts from some of the microphone time series. A novel automated procedure was developed to analyze and reject contaminated blocks of data, under the assumption that the desired acoustic signal of interest was a band-limited sta- tionary random process, and of lower variance than the hydrodynamic contamination. The procedure is shown to successfully identify and remove contaminated blocks of data and retain the desired acoustic signal. Additional corrections to the data, mainly background subtraction, shear layer refraction calculations, atmospheric attenuation and microphone directivity corrections, were all necessary for initial analysis and noise assessments. These were implemented for the post-processing of spectral data, and are shown to behave as expected.

Bahr, Christopher J.↗

NASA NTRS2003

Applications of Hilbert Spectral Analysis for Speech and Sound Signals

A new method for analyzing nonlinear and nonstationary data has been developed, and the natural applications are to speech and sound signals. The key part of the method is the Empirical Mode Decomposition method with which any complicated data set can be decomposed into a finite and often small number of Intrinsic Mode Functions (IMF). An IMF is defined as any function having the same numbers of zero-crossing and extrema, and also having symmetric envelopes defined by the local maxima and minima respectively. The IMF also admits well-behaved Hilbert transform. This decomposition method is adaptive, and, therefore, highly efficient. Since the decomposition is based on the local characteristic time scale of the data, it is applicable to nonlinear and nonstationary processes. With the Hilbert transform, the Intrinsic Mode Functions yield instantaneous frequencies as functions of time, which give sharp identifications of imbedded structures. This method invention can be used to process all acoustic signals. Specifically, it can process the speech signals for Speech synthesis, Speaker identification and verification, Speech recognition, and Sound signal enhancement and filtering. Additionally, as the acoustical signals from machinery are essentially the way the machines are talking to us. Therefore, the acoustical signals, from the machines, either from sound through air or vibration on the machines, can tell us the operating conditions of the machines. Thus, we can use the acoustic signal to diagnosis the problems of machines.

Huang, Norden E.↗

DOE OSTI2022

Deep learning categorization of infrasound array data

Here we develop a deep learning-based infrasonic detection and categorization methodology that uses convolutional neural networks with self-attention layers to identify stationary and non-stationary signals in infrasound array processing results. Using features extracted from the coherence and direction-of-arrival information from beamforming at different infrasound arrays, our model more reliably detects signals compared with raw waveform data. Using three infrasound stations maintained as part of the International Monitoring System, we construct an analyst-reviewed data set for model training and evaluation. We construct models using a 4-category framework, a generalized noise vs non-noise detection scheme, and a signal-of-interest (SOI) categorization framework that merges short duration stationary and non-stationary categories into a single SOI category. We evaluate these models using a combination of k-fold cross-validation, comparison with an existing “state-of-the-art” detector, and a transportability analysis. Although results are mixed in distinguishing stationary and non-stationary short duration signals, f-scores for the noise vs non-noise and SOI analyses are consistently above 0.96, implying that deep learning-based infrasonic categorization is a highly accurate means of identifying signals-of-interest in infrasonic data records.

47 OTHER INSTRUMENTATION↗

DOE OSTI2022

Acoustic Travel-Time Tomography for Wind Energy

New remote sensing technologies for direct observation of the atmospheric flow in the proximity of operating wind turbines are necessary for the advancement of wind turbine design, wind plant control, and the validation of high-fidelity numerical models; fundamental goals of the DOE Wind Energy Technology Office's (WETO's) A2e program. Modern remote sensing technologies for commercial and research applications rely on the backscatter of light or sound waves, which limits spatial and temporal resolution and cannot produce reliable flow estimates near solid bodies or reflective surfaces. Acoustic tomography (AT) is an innovative sensing technology based on fundamental thermodynamic properties of the atmosphere and an inverse algorithm that can reconstruct both velocity and temperature fields within a network of acoustic transducers. AT relies on the direct travel path of acoustic signals between speakers and microphones in the network and can achieve much higher resolutions than conventional backscatter technologies. For wind energy R&D applications, AT represents an extremely economical option over other remote sensing technologies, integrating mature and commonly available acoustic and signal processing hardware. This report is intended as an update on the development of the AT array on the NREL Flatirons Campus (FC) and includes a brief review of the instrumentation, the calibration of equipment, signal processing, and resulting flow field estimates. Designs for future AT systems and applications are detailed in this report, including summaries of new capabilities, engineering and theoretical challenges, and anticipated costs help to outline future directions for AT technology R\&D. The potential for application of AT to utility-scale turbines is also discussed. Investing in the development of the proposed instrument will establish AT as a transformational RS technique that represents a significant advance in measurement instrumentation, enabling new fundamental insights into the structure of the atmospheric boundary layer and supporting applications for wind energy, pollution transport, and resource characterization. AT is also an extremely economical option over other RS instruments, integrating mature and commonly available acoustic and signal processing hardware.

17 WIND ENERGY↗

NASA NTRS1987

Punch stretching process monitoring using acoustic emission signal analysis. II - Application of frequency domain deconvolution

The coloring effect on the acoustic emission signal due to the frequency response of the data acquisition/processing instrumentation may bias the interpretation of AE signal characteristics. In this paper, a frequency domain deconvolution technique, which involves the identification of the instrumentation transfer functions and multiplication of the AE signal spectrum by the inverse of these system functions, has been carried out. In this way, the change in AE signal characteristics can be better interpreted as the result of the change in only the states of the process. Punch stretching process was used as an example to demonstrate the application of the technique. Results showed that, through the deconvolution, the frequency characteristics of AE signals generated during the stretching became more distinctive and can be more effectively used as tools for process monitoring.

Liang, Steven Y.↗

NASA NTRS2015

NASA's Aeroacoustic Tools and Methods for Analysis of Aircraft Noise

Aircraft community noise is a significant concern due to continued growth in air traffic, increasingly stringent environmental goals, and operational limitations imposed by airport authorities. The ability to quantify aircraft noise at the source and ultimately at observers is required to develop low noise aircraft designs and flight procedures. Predicting noise at the source, accounting for scattering and propagation through the atmosphere to the observer, and assessing the perception and impact on a community requires physics-based aeroacoustics tools. Along with the analyses for aero-performance, weights and fuel burn, these tools can provide the acoustic component for aircraft MDAO (Multidisciplinary Design Analysis and Optimization). Over the last decade significant progress has been made in advancing the aeroacoustic tools such that acoustic analyses can now be performed during the design process. One major and enabling advance has been the development of the system noise framework known as Aircraft NOise Prediction Program2 (ANOPP2). ANOPP2 is NASA's aeroacoustic toolset and is designed to facilitate the combination of acoustic approaches of varying fidelity for the analysis of noise from conventional and unconventional aircraft. The toolset includes a framework that integrates noise prediction and propagation methods into a unified system for use within general aircraft analysis software. This includes acoustic analyses, signal processing and interfaces that allow for the assessment of perception of noise on a community. ANOPP2's capability to incorporate medium fidelity shielding predictions and wind tunnel experiments into a design environment is presented. An assessment of noise from a conventional and Hybrid Wing Body (HWB) aircraft using medium fidelity scattering methods combined with noise measurements from a model-scale HWB recently placed in NASA's 14x22 wind tunnel are presented. The results are in the form of community noise metrics and auralizations.

Rizzi, Stephen A.↗

NASA NTRS1975

Electrophysiological measurement of human auditory function

Knowledge of the human auditory evoked response is reviewed, including methods of determining this response, the way particular changes in the stimulus are coupled to specific changes in the response, and how the state of mind of the listener will influence the response. Important practical applications of this basic knowledge are discussed. Measurement of the brainstem evoked response, for instance, can state unequivocally how well the peripheral auditory apparatus functions. It might then be developed into a useful hearing test, especially for infants and preverbal or nonverbal children. Clinical applications of measuring the brain waves evoked 100 msec and later after the auditory stimulus are undetermined. These waves are clearly related to brain events associated with cognitive processing of acoustic signals, since their properties depend upon where the listener directs his attention and whether how long he expects the signal.

Galambos, R.↗

NASA NTRS1975

Electrophysiological measurement of human auditory function

Contingent negative variations in the presence and amplitudes of brain potentials evoked by sound are considered. Evidence is produced that the evoked brain stem response to auditory stimuli is clearly related to brain events associated with cognitive processing of acoustic signals since their properties depend upon where the listener directs his attention, whether the signal is an expected event or a surprise, and when sound that is listened-for is heard at last.

Galambos, R.↗

NASA NTRS2014

NDE Software Developed at NASA Glenn Research Center

NASA Glenn Research Center has developed several important Nondestructive Evaluation (NDE) related software packages for different projects in the last 10 years. Three of the software packages have been created with commercial-grade user interfaces and are available to United States entities for download on the NASA Technology Transfer and Partnership Office server (https://sr.grc.nasa.gov/). This article provides brief overviews of the software packages.

image processing↗

NASA NTRSDate not supplied

EVALUATION OF ACOUSTIC SNAPSHOT ARRAYS FOR ROTORCRAFT SOURCE NOISE CHARACTERIZATION

Historical development of acoustic hemispheres has required steady flight of a rotorcraft vehicle across a large linear array of microphones. The US Army, NASA, and Navy recently conducted a rotorcraft acoustics flight test in which multiple “snapshot” microphone arrays were used alongside a traditional linear microphone array. The snapshot arrays allow for a near instantaneous capture of rotorcraft acoustic emissions, without the need for steady flight. Development of the snapshot array is contained herein, and an evaluation of effectiveness of the array during adverse weather conditions. The snapshot arrays captured significant variation in acoustic emissions throughout a single run and between multiple runs of similar conditions. Hemispheres were created and modelled in land-use planning software and an investigation of A-weighted Sound Exposure Level (SEL [dBA]) was conducted. Sideline predictions of SEL compared well (within 0.1 dBA) between traditional and snapshot arrays, while centre line locations were less favourable with a difference of 1.6 dBA. Future refinement is required for the snapshot array technique, including advanced design of microphone placement and employing a semiempirical method to interpolate between measurement points, instead of the linear frequency weighting conventionally employed.

Rotorcraft↗

NASA NTRS2015

Removing Background Noise with Phased Array Signal Processing

Preliminary results are presented from a test conducted to determine how well microphone phased array processing software could pull an acoustic signal out of background noise. The array consisted of 24 microphones in an aerodynamic fairing designed to be mounted in-flow. The processing was conducted using Functional Beam forming software developed by Optinav combined with cross spectral matrix subtraction. The test was conducted in the free-jet of the Nozzle Acoustic Test Rig at NASA GRC. The background noise was produced by the interaction of the free-jet flow with the solid surfaces in the flow. The acoustic signals were produced by acoustic drivers. The results show that the phased array processing was able to pull the acoustic signal out of the background noise provided the signal was no more than 20 dB below the background noise level measured using a conventional single microphone equipped with an aerodynamic forebody.

Background Noise↗