Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “time and frequency domain features”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Data-Driven Cyber-Attack Detection for PV Farms via Time-Frequency Domain Features

The internetworking of grid-connected power electronics converters (PECs) in photovoltaic (PV) farms has inevitably expanded the cyber-attack surfaces. Here this paper presents a comprehensive study on cyber-attack detection and diagnosis for PEC-enabled PV farms via single waveform sensor to distinguish between normal conditions, open-circuit faults, short-circuit faults, and cyber-attacks. To our knowledge, this has not been attempted before. Firstly, we propose frequency-domain magnitude-based residuals to identify short-circuit faults and a time-domain mean current vector-based feature to distinguish open-circuit faults from other threats. These features can fully reflect the specific physical characteristics of PV farms during threat duration. Secondly, unlike micro phasor measurement units (µPMU) and raw electric waveform-based methods, the proposed innovative features can address novel cyber-attacks that are excluded from the training process. Thirdly, an online hardware-in-the-loop (HIL) testbed using the OPAL-RT real-time digital simulator has verified the effectiveness. The monitoring system runs in real-time while using HIL as an operational solar farm and a National Instruments (NI) data acquisition card as the electric waveform sensor at the point of coupling.

42 ENGINEERING↗
DOE OSTI2026

Feature-Based PMU Event Classification under Variable PMU Participation and Overlapping Events

This paper is the basis for a presentation help at the 2026 Georgia Tech Fault & Disturbance Analysis Conference, which can be found at OSTI # 3168287 Paper Abstract—Phasor Measurement Units (PMUs) stream time synchronized, high-resolution measurements from the grid, enabling data-driven techniques for event detection and classification. Accurate event classification improves grid reliability and stability. Events can be detected by varying numbers of PMUs and exhibit different durations depending on the event type. This variability challenges standard classifiers that require uniform input sizes. Moreover, multiple events may coincide, which increases classification complexity. Standard classifiers assign each instance to the class with the highest predicted probability, whereas overlapping events may exhibit comparable probabilities across multiple classes. In this study, to handle data size variability, we extract a wide range of time–frequency domain features from all available PMUs for each event into a fixed-length vector, facilitating the application of standard machine learning classifiers, including Random Forest, XGBoost, LightGBM, Support Vector Machine, and Multilayer Perceptron. To account for overlapping events, a probabilistic post-processing step is applied. For a given data instance, if multiple predicted class probabilities exceed 30% and the differences between them are less than 10%, the event is assigned to multiple classes. Experiments using real-world PMU data demonstrate that the Random Forest and XGBoost models achieve the highest accuracy, while the proposed post-processing method yields perfect classification performance on external unseen test sets.

Nematirad, Reza [Danovo Energy Solutions]↗
DOE OSTI2026

Feature-Based PMU Event Classification under Variable PMU Participation and Overlapping Events

Danovo Energy Solution's presented its paper named: Feature-Based PMU Event Classification under Variable PMU Participation and Overlapping Events at the 2026 Georgia Tech Fault & Disturbance Analysis Conference. The full paper can be found at OSTI ID# 3169150 Paper Abstract—Phasor Measurement Units (PMUs) stream time synchronized, high-resolution measurements from the grid, enabling data-driven techniques for event detection and classification. Accurate event classification improves grid reliability and stability. Events can be detected by varying numbers of PMUs and exhibit different durations depending on the event type. This variability challenges standard classifiers that require uniform input sizes. Moreover, multiple events may coincide, which increases classification complexity. Standard classifiers assign each instance to the class with the highest predicted probability, whereas overlapping events may exhibit comparable probabilities across multiple classes. In this study, to handle data size variability, we extract a wide range of time–frequency domain features from all available PMUs for each event into a fixed-length vector, facilitating the application of standard machine learning classifiers, including Random Forest, XGBoost, LightGBM, Support Vector Machine, and Multilayer Perceptron. To account for overlapping events, a probabilistic post-processing step is applied. For a given data instance, if multiple predicted class probabilities exceed 30% and the differences between them are less than 10%, the event is assigned to multiple classes. Experiments using real-world PMU data demonstrate that the Random Forest and XGBoost models achieve the highest accuracy, while the proposed post-processing method yields perfect classification performance on external unseen test sets.

Nematirad, Reza [Danova Energy Solutions]↗
DOE OSTI2022

Sensor selection and tool wear prediction with data‐driven models for precision machining

Abstract Estimation of tool wear in precision machining is vital in the traditional subtractive machining industry to reduce processing cost, improve manufacturing efficiency and product quality. In this vein, fusion of time and frequency‐domain features of commonly sensed signals can provide an early indication of tool wear and improve its prediction accuracy for prognostics and health management. This paper presents a data‐driven methodology and a complete tool chain for the inference of precision machining tool wear from fused machine measurements, such as cutting force, power, audio and vibration signals, and quantify the usefulness of each measurement. Indicators of tool wear are extracted from time‐domain signal statistics, frequency‐domain analysis, and time‐frequency domain analysis. Correlation coefficients between the extracted features (indicators) and the tool wear are used to select the most informative features. Principal Component Analysis and Partial Least‐Squares are used to reduce the dimensionality of the feature space. Regression models, including linear regression, support vector regression, Decision tree regression, neural network regression and Gaussian process regression, are used to predict the tool wear using data from a Haas milling machine performing spiral boss face milling. The performance of the regression models based on subsets of sensors validates the preliminary estimates about the saliency of the sensors. The experimental results show that the proposed methods can predict the machine tool wear precisely, with readily available sensor measurements. Neural network and Gaussian process regression were able to achieve good estimates of tool wear at different machine operating conditions. The most informative signal in predicting tool wear was shown to be the vibration signal. Time‐frequency domain features were the most informative features among the combination of features of three domains. In addition, using partial least squares components extracted from the original features of signals led to higher prediction accuracy.

Han, Seulki↗
NASA NTRS1987

Dynamic spectrum of a radio flare on UV Ceti

The dMe flare star UV Ceti (L726-8B) was observed at four frequencies simultaneously in the 1385-1652 MHz band using the Very Large Array. A flare lasting 10 minutes was observed with 6.67 s time resolution 'Dynamic spectrum'-type images in the Stokes parameters I and V show considerable complexity in the frequency-time domain; some features show a positive frequency drift with time, while others are more complex, involving both positive and negative frequency drifts. The positive drift features would be consistent with disturbances traveling downward in the star's corona.

Jackson, Peter D.↗
DOE OSTI2025

Enhancing Unknown Waveform Detection by Learning Intra and Inter-domain Dependencies with Advanced Attention Fusion Mechanisms

Detection of unknown waveforms in mission-critical communications is a crucial area of interest for the Department of Energy (DoE). Traditional methods and recent deep learning-based approaches often assume that the training set includes all possible classes, which is impractical for detecting new waveforms. This limitation gives rise to the problem of open-set recognition (OSR), which involves correctly identifying known classes while detecting and rejecting unknown or unseen classes. To address this limitation, we propose a novel dual-domain complex-valued neural architecture that jointly processes time-domain and frequency-domain signal representations using transformer mechanisms. A transformer model is a deep learning architecture that uses self-attention mechanisms to process and learn relationships in sequential data. Our model employs a cosine similarity loss to extract domain-specific features and incorporates a transformer architecture in the latent space to weigh the importance of different features from the time and frequency domains. The transformer layer includes stacked self-attention and cross-attention modules to learn intra-domain and inter-domain dependencies, creating a more holistic signal representation. An attention-based fusion module intelligently combines the time and frequency-domain features using multi-head attention, enabling the network to learn the optimal feature for each domain in each input signal. Quantitative results demonstrate the impact of these architectural choices on overall performance, showing significant improvement after incorporating self and cross-attention modules and using complex attention fusion over simple weighted fusion. Our ongoing work will focus on addressing the limitations of threshold-based OSR methods by developing a novel generative framework that integrates a conditional diffusion probabilistic model (DPM). DPM is a generative framework that learns to synthesize complex data by reversing a gradual noising process using a neural network trained to denoise step-by-step. Our goal is to leverage the inherent strengths of DPMs for identifying unknown signals more robustly. One primary advantage of using a DPM is its ability to provide a more reliable anomaly score based on the model's reconstruction error, rather than relying solely on classifier confidence. Additionally, the iterative denoising process of DPMs makes this approach naturally resilient to low Signal-to-Noise Ratio (SNR) conditions, where traditional methods often fail. By implementing this generative framework, we aim to enhance the model's capability to accurately detect unknown waveforms and maintain performance in challenging environments.

99 - GENERAL AND MISCELLANEOUS↗
DOE OSTI2025

Contrasting Time-Frequency Representations for Unknown Waveform Detection

In real-world applications like spectrum management and interference detection, dealing with unseen electromagnetic waveforms is critical. Although some methods attempt to simulate open set data using generator models, they face challenges in generating synthetic samples for open set while simultaneously selecting an optimal discriminator for accurate classification. This results in difficulties capturing distinctive features across classes, especially in dynamic scenarios where new classes emerge. To detect unseen waveforms, we propose combining time and frequency domain features with cosine similarity loss to enhance feature distinctiveness and enabling more accurate predictions. This approach efficiently captures more comprehensive information than single-domain representations or approaches without cosine loss. Additionally, our model avoids generic feature vectors by extracting class-specific features during training, resulting in improved class representation. The experiment results show that this combined feature approach with cosine loss outperforms single-domain models and improves accuracy by 10\% over models without cosine loss.

99 - GENERAL AND MISCELLANEOUS↗
DOE OSTI2025

Contrasting Time-Frequency Representations for Unknown Waveform Detection

Identifying unseen electromagnetic waveforms is critical for many applications, like interference management, electronic warfare and spectrum management. Traditionally this is done using statistical methods for anomaly detection, which has evolved to deep learning models for identifying the unseen data, formally termed as open set recognition. Some prior methods use a generative model to emulate open set data, which face challenges in generating synthetic samples for open set while simultaneously selecting an optimal discriminator for accurate classification. To alleviate this issue, we propose a discriminative model that effectively combines time and frequency domain features of communication signals for accurate predictions. We further introduce a cosine similarity loss that makes the domain specific features unique to enhance the prediction rate. Additionally, our model avoids generic feature vectors by extracting class-specific features during training, resulting in improved class representation. The experiment results show that this combined feature approach with cosine loss outperforms single-domain models and improves accuracy by 10% over models without cosine loss.

99 - GENERAL AND MISCELLANEOUS↗
NASA NTRS2010

Combining Model-Based and Feature-Driven Diagnosis Approaches - A Case Study on Electromechanical Actuators

Model-based diagnosis typically uses analytical redundancy to compare predictions from a model against observations from the system being diagnosed. However this approach does not work very well when it is not feasible to create analytic relations describing all the observed data, e.g., for vibration data which is usually sampled at very high rates and requires very detailed finite element models to describe its behavior. In such cases, features (in time and frequency domains) that contain diagnostic information are extracted from the data. Since this is a computationally intensive process, it is not efficient to extract all the features all the time. In this paper we present an approach that combines the analytic model-based and feature-driven diagnosis approaches. The analytic approach is used to reduce the set of possible faults and then features are chosen to best distinguish among the remaining faults. We describe an implementation of this approach on the Flyable Electro-mechanical Actuator (FLEA) test bed.

Narasimhan, Sriram↗
DOE OSTI2020

The influence of packing structure and interparticle forces on ultrasound transmission in granular media

Ultrasound propagation through externally stressed, disordered granular materials was experimentally and numerically investigated. Experiments employed piezoelectric transducers to excite and detect longitudinal ultrasound waves of various frequencies traveling through randomly packed sapphire spheres subjected to uniaxial compression. The experiments featured in situ X-ray tomography and diffraction measurements of contact fabric, particle kinematics, average per-particle stress tensors, and interparticle forces. The experimentally measured packing configuration and inferred interparticle forces at different sample stresses were used to construct spring networks characterized by Hessian and damping matrices. The ultrasound responses of these network were simulated to investigate the origins of wave velocity, acoustic paths, dispersion, and attenuation. Results revealed that both packing structure and interparticle force heterogeneity played an important role in controlling wave velocity and dispersion, while packing structure alone quantitatively explained most of the observed wave attenuation. This research provides insight into time- and frequency-domain features of wave propagation in randomly packed granular materials, shedding light on the fundamental mechanisms controlling wave velocities, dispersion, and attenuation in such systems.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Unsteady aerodynamic loads on pitching aerofoils represented by Gaussian body force distributions

The actuator line model (ALM) is an approach commonly used to represent lifting and dragging devices like wings and blades in large-eddy simulations (LES). The crux of the ALM is the projection of the actuator point forces onto the LES grid by means of a Gaussian regularisation kernel. The minimum width of the kernel is constrained by the grid size; however, for most practical applications like LES of wind turbines, this value is an order of magnitude larger than the optimal value that maximises accuracy. This discrepancy motivated the development of corrections for the actuator line, which, however, neglect the effect of unsteady spanwise shed vorticity. In this work we develop a model for the impact of spanwise shed vorticity on the unsteady loading of an aerofoil modelled as a Gaussian body force distribution, where the model is applicable within the regime of unsteady attached flow. The model solution is derived both in the time and frequency domain and features an explicit dependence on the Gaussian kernel width. We verify the model with ALM-LES for both pitch steps and periodic pitching. The model solution is compared with Theodorsen theory and validated with both computational fluid dynamics using body fitted grids and experiment. It is concluded that the optimal kernel width for unsteady aerodynamics is approximately 40 % of the chord. The ALM is able to predict the magnitude of the unsteady loading up to a reduced frequency of 𝑘 ≈ 0.2.

17 WIND ENERGY↗
NASA NTRS1991

Rotor noise

The physical characteristics and sources of rotorcraft noise as they exist today are presented. Emphasis is on helicopter-like vehicles, that is, on rotorcraft in nonaxial flight. The mechanisms of rotor noise are reviewed in a simple physical manner for the most dominant sources of rotorcraft noise. With simple models, the characteristic time- and frequency-domain features of these noise sources are presented for idealized cases. Full-scale data on several rotorcraft are then reviewed to allow for the easy identification of the type and extent of the radiating noise. Methods and limitations of using scaled models to test for several noise sources are subsequently presented. Theoretical prediction methods are then discussed and compared with experimental data taken under very controlled conditions. Finally, some promising noise reduction technology is reviewed.

Schmitz, F. H.↗
DOE OSTI2025

Detecting Unclassified Electromagnetic Signals for Secure Wireless Communication Using Open Set Recognition

We developed multiple machine learning methods for the detection and classification of new wireless communication waveforms, which is critical for targeted attacks in wireless networks and electronic warfare. Our machine learning models are capable of dynamically detecting security threats in near real time through our advanced open set recognition (OSR) approach. This model has demonstrated significant improvements in the detection of unknown waveforms, thereby enhancing the security and reliability of mission critical communications. Our approach to detecting uncertain security threats is novel; we advanced OSR techniques by incorporating domain knowledge of wireless signals. Specifically, we combined time and frequency domain model features to enhance the model’s performance. Utilizing an OSR approach eliminates the need for training data to be distributed similarly to the deployment environment and removes the requirement for the training set to contains all possible threat classes. This is crucial because it is often infeasible to determine and characterize all potential security threats in advance. Our model were trained on simulated data, generated in partnership with the University at Albany, State of New York. The data set contained a diverse array of wireless signals, including those with additive white Gaussian noise and multipath signals, with and without line of sight. This comprehensive training set allowed us to optimize our models to detect unknown waveforms under various challenging scenarios, such as low signal-to-noise ratios. By training on various waveforms, varying signal-to-noise ratio, and different sample sizes under normal conditions, our models were fine tuned to perform effectively in challenging environments.

99 - GENERAL AND MISCELLANEOUS↗
NASA NTRS1990

Identification of structural-borne noise components by the use of time-frequency filtering

In this paper it is shown that a time-frequency analysis of a structural impulse response may be used to identify the wave types carrying significant energy through a structure. The identification of various wave types is possible since each is characterized by its own dispersion relation, with the result that each wave type may be associated with characteristic features in the time-frequency domain representation of an impulse response. In the work described here, the Wigner Distribution has been used to obtain appropriate time-frequency representations of impulse responses. Practical aspects of the calculation of the Wigner Distribution are discussed briefly and examples of its application to the analysis of structural impulse responses are given. Additionally, filtering techniques are described that can be used both to detect the presence of and to quantify the energy carried by both nondispersive and dispersive waves.

Wahl, T. J.↗
NASA NTRS1993

The application of the Wigner Distribution to the identification of structure-borne noise components

In this paper it is shown that time-frequency analysis of a transient structural response may be used to identify the wave types carrying significant energy through a structure. The identification of various wave types is possible since each is characterized by its own dispersion relation, with the result that each wave type may be associated with characteristic features in the time-frequency domain representation of a structural response. In the work described here, the Wigner Distribution has been used to obtain time-frequency representations of structural transient responses. Practical aspects of the calculation of the Wigner Distribution are discussed briefly, and examples of its application to the analysis of transient structural responses are given. Additionally, time-frequency filtering techniques are described that can be used both to detect the presence of and to quantify the energy carried by both non-dispersive and dispersive waves.

Wahl, T. J.↗
NASA NTRS2025

On the Characterization and Mitigation of Noise in Space-borne Microwave Sounding Instruments

Space-borne microwave sounding instruments have become vital data sources for weather prediction and climate change studies. Among the various radiometer configurations, the total power microwave radiometer is particularly appealing for current and future operational satellites due to its superior sensitivity and simple design. However, its performance is vulnerable to degradation caused by receiver gain fluctuations, electronic 1/f noise, and other time varying receiver characteristics. For Numerical Weather Prediction (NWP) users, 1/f noise introduces inter-channel correlations, complicating the assimilation of affected observations and reducing their accuracy. Addressing this noise issue in ground data processing system is essential to enhance the utility of microwave sounding data. This paper focuses on the characterization and mitigation of noise in current and future microwave sounding instruments, with particular emphasis on the impact of 1/f noise. Various methods are applied to quantitatively characterize noise features in both frequency and time domains. Additionally, the influence of calibration parameters on 1/f noise are analyzed. Based on these findings, we propose a mitigation algorithm for reducing noise during the on-orbit calibration of microwave sounding instruments, aiming to improve the quality of retrieved data for operational use.

calibration↗
DOE OSTI2023

Low power and privacy preserving sensor platform for occupancy detection

A low-cost, low-power, stand-alone sensor platform having a visible-range camera sensor, a thermopile array, a microphone, a motion sensor, and a microprocessor that is configured to perform occupancy detection and counting while preserving the privacy of occupants. The platform is programmed to extract shape/texture from images in spatial domain; motion from video in time domain; and audio features in frequency domain. Embedded binarized neural networks are used for efficient object of interest detection. The platform is also programmed with advanced fusion algorithms for multiple sensor modalities addressing dependent sensor observations. The platform may be deployed for (i) residential use in detecting occupants for autonomously controlling building systems, such as HVAC and lighting systems, to provide energy savings, (ii) security and surveillance, such as to detect loitering and surveil places of interest, (iii) analyzing customer behavior and flows, (iv) identifying high performing stores by retailers.

Velipasalar, Senem↗
DOE OSTI2022

Cyber-Attack Identification of Synchrophasor Data Via VMD and Multifusion SVM

A large amount of synchrophasor data in the wide area measurement system (WAMS) needs to be collected and transmitted to the phasor data concentrator, thereby increasing the possibility of being attacked by hackers. The attacked data are therefore hidden into the normal synchrophasor data so that the synchrophasor data based application will be affected. To remedy this problem, an identification framework is proposed to detect the data cyber-attack in WAMS utilizing variational mode decomposition (VMD) and multifusion support vector machine (MSVM). First, VMD is used to transform the attacked data into multiple modal components. Thereafter, a novel MSVM is employed to classify the deterministic features using the proposed linear combined multikernel (LCM). Further, this LCM can fuse multiple types of features, including the time, frequency, and statistical domains of the synchrophasor data. Utilizing the actual data from FNET/GridEye, different experiments are conducted under multiple attack strengths and types. The results demonstrate that the identification framework has higher precision and robustness compared with other conventional classifiers.

97 MATHEMATICS AND COMPUTING↗