Joel Harley

University of Florida | UF · Department of Electrical and Computer Engineering

Guided wave structural health monitoring is widely researched for remotely inspecting large structural areas. To detect, locate, and characterize damage, guided wave methods often compare data to a baseline signal. Yet, environmental variations create large differences between the baseline and the collected measurements. These variations hide damag...

In guided wave structural health monitoring, damage detection is often accomplished by comparing measurements before damage (i.e., baseline data) and after damage (i.e., test data). Yet, in practical scenarios, baseline data is often unavailable. Data from surrogate structures (structures similar to the test structure) could replace baseline data,...

This paper explains the use of supervised and unsupervised dictionary learning approaches on spread spectrum time domain (SSTDR) data to detect and locate disconnections in a PV array consisting of five panels. The aim is to decompose an SSTDR reflection signature into different components where each component has a physical interpretation, such as...

Wavefield imaging is a powerful visualization tool in nondestructive evaluation for studying ultrasonic wave propagation and its interactions with damage. To isolate and study damage scattering, damage-free baseline data is often subtracted from a wavefield. This is often necessary because the damage wavefield can be orders of magnitude weaker than...

Experimental grain growth observations often deviate from grain growth simulations, revealing that the governing rules for grain boundary motion are not fully understood. A novel deep learning model was developed to capture grain growth behavior from training data without making assumptions about the underlying physics. The Physics-Regularized Inte...

Crystallographic texture is an important descriptor of material properties but requires time-intensive electron backscatter diffraction (EBSD) for identifying grain orientations. While some metrics such as grain size or grain aspect ratio can distinguish textured microstructures from untextured microstructures after significant grain growth, such m...

This paper studies the effectiveness of joint compression and denoising strategies with realistic, long-term guided wave structural health monitoring data. We leverage the high correlation between nearby collections of guided waves in time to create sparse and low-rank representations. While compression and denoising schemes are not new, they are a...

Experimental grain growth observations often deviate from grain growth simulations, revealing that the governing rules for grain boundary motion are not fully understood. A novel deep learning model was developed to capture grain growth behavior from training data without making assumptions about the underlying physics. The Physics-Regularized Inte...

Current grain growth models have evolved to account for the relationship between grain boundary energy/mobility anisotropy and the five degrees of grain boundary character. However, the role of grain boundary networks on overall growth kinetics remains poorly understood. To experimentally investigate this problem, a highly textured Al2O3 was fabric...

No PDF available ABSTRACT Modern machine learning has been on the rise in many scientific domains, such as acoustics. Many scientific problems face challenges with limited data, which prevent the use of the many powerful machine learning strategies. In response, the physics of wave-propagation can be exploited to reduce the amount of data necessary...

We have developed a flexible method for calculating the grain boundary (GB) inclinations of voxelated grain structure data using smoothing algorithms. We compared the performance of four algorithms: the linear interpolation, Allen–Cahn, level-set, and vertex algorithms. We assessed their accuracy using 2D and 3D cases with known inclinations. The v...

Guided ultrasonic wave localization systems use spatially distributed sensor arrays and wave propagation models to detect and locate damage across a structure. Environmental and operational conditions, such as temperature or stress variations, introduce uncertainty into guided wave data and reduce the effectiveness of these localization systems. Th...

Guided wave testing is a popular approach for monitoring the structural integrity of infrastructures. We focus on the primary task of damage detection, where signal processing techniques are commonly employed. The detection performance is affected by a mismatch between the wave propagation model and experimental wave data. External variations, such...

This article utilizes variational autoencoder (VAE) and spread spectrum time domain reflectometry (SSTDR) to detect, isolate, and characterize anomalous data (or faults) in a photovoltaic (PV) array. The goal is to learn the distribution of non-faulty input signals, inspect the reconstruction error of test signals, flag anomalies, and then locate o...

Current spread spectrum time-domain reflectometry (SSTDR) fault detection methods in photovoltaics compare measurements with a fault-free baselin. Yet, environmental factors, such as illuminance, temperature, and humidity, affect these signals and can negatively affect our ability to detect and locate faults. This article explains and quantifies th...

Objective: Hill-type muscle models are widely employed in simulations of human movement. Yet, the parameters underlying these models are difficult or impossible to measure in vivo. Prior studies demonstrate that Hill-type muscle parameters are encoded within dynamometric data. But, a generalizable approach for estimating these parameters from dyna...

Sequence time-domain reflectometry (STDR) and spread spectrum time-domain reflectometry (SSTDR) detect, locate, and diagnose faults in live (energized) electrical systems. In this paper, we survey the present SSTDR literature for discussions on theory, algorithms used in its analysis, and its more prominent implementations and applications. Our rev...

Environmental effects are a significant challenge in guided wave structural health monitoring systems. These effects distort signals and increase the likelihood of false alarms. Many research papers have studied mitigation strategies for common variations in guided wave datasets reproducible in a lab, such as temperature and stress. There are fewer...

While guided wave structural health monitoring (SHM) is widely researched for ensuring safety, estimating performance deterioration, and detecting damage in structures, it experiences setbacks in accuracy due to varying environmental, sensor, and material factors. To combat these challenges, environmentally variable guided wave data is often stretc...

With the recent success of representation learning methods, which includes deep learning as a special case, there has been considerable interest in developing representation learning techniques that can incorporate known physical constraints into the learned representation. As one example, in many applications that involve a signal propagating thro...

Unexpected results have been seen in multiple papers involving spread spectrum time domain reflectometry (SSTDR) measurements on twin lead cables with one line containing a fault. A small portion of the signal is able to transmit past the fault, reflect off the end of the cable, return back through the fault, and be recorded by the SSTDR. This pape...

This paper explains the use of a convolutional neural network (CNN) to segment solar panels in a satellite image containing solar arrays, and extract associated metadata from the arrays. A novel unsupervised technique is introduced to estimate the azimuth of each individual solar panel from the predicted mask of the convolutional neural network. Th...

In this article, we explore the possibility of using spread spectrum time domain reflectometry (SSTDR) for detecting disconnections in a large-scale photovoltaic (PV) array. We discuss the importance, role, and trade-offs of SSTDR resolution, frequency, and attenuation in detecting disconnects in the system. Our results show that if the proper syst...

Spread spectrum time domain reflectometry (SSTDR) is a non-intrusive method for electrical fault detection and localization that enables continuous monitoring of live electrical systems. Electrical faults create changes in impedance that create subsequent changes in the SSTDR reflection response. These changes in reflection response can be detected...

In this paper, we present a method for estimating complex impedances using reflectometry and a modified steepest descent inversion algorithm. We simulate spread spectrum time domain reflectometry (SSTDR), which can measure complex impedances on energized systems for an experimental setup with resistive and capacitive loads. A parametric function, w...

The ability of spread spectrum time domain reflectometry (SSTDR) to detect and locate faults in photovoltaic (PV) systems is considered in this article. This article provides a simulation that could be used for studying how faults and other parameters affect the reflectometry response and evaluating fault detection algorithms, for providing compari...

The operating efficiency of photovoltaic (PV) plants can be improved if damaged or degraded modules can be detected and identified. Currently, string-level power electronics can detect problems with modules or cabling but not locate them, which would facilitate addressing these issues. Here, we investigate the ability of spread spectrum time domain...

Over the last several decades, structural health monitoring systems have grown into increasingly diverse applications. Structural health monitoring excels with large data sets that can capture the typical variability, novel events, and undesired degradation over time. As a result, the efficient storage and processing of these large, guided wave dat...

Guided ultrasonic wave localization uses spatially distributed multistatic sensor arrays and generalized beamforming strategies to detect and locate damage across a structure. The propagation channel is often very complex. Methods can compare data with models of wave propagation to locate damage. Yet, environmental uncertainty (e.g., temperature or...

Many non-destructive evaluation techniques are based on the study and assessment of guided wavefields. Yet, the extent of the sensing region and the span of time over which wavefield data is acquired can be tremendous, resulting in an enormous amount of spatio-temporal data. As a result, reducing the burden of data acquisition and storage from unde...

Objective: Septic shock is a life-threatening manifestation of infection with a mortality of 20-50%. A catecholamine vasopressor, norepinephrine (NE), is widely used to treat septic shock primarily by increasing blood pressure. For this reason, future blood pressure knowledge is invaluable for properly controlling NE infusion rates in septic patie...

In this paper, we present a method for estimating capacitances with SSTDR and a dictionary matching algorithm. We simulate a dictionary of simulated SSTDR reflections for a range of capacitances, based on parameters of the transmitted SSTDR signal, the SSTDR signal generator, and the transmission line. The measured SSTDR reflection data is compared...

Accurate decision-making requires understanding the factors that influence those decisions. In guided wave structural health monitoring, the first aim is to detect the presence of damage. This decision is based on the assumption that it is possible to discriminate between undamaged and damaged states. Sensing systems collect data to construct damag...

Accurate decision-making requires understanding the factors that influence those decisions. In guided wave structural health monitoring, the first aim is to detect the presence of damage. This decision is based on the assumption that it is possible to discriminate between undamaged and damaged states. Sensing systems collect data to construct damag...

For more details see: https://bit.ly/PVSSTDR -- Spread spectrum time domain reflectometry (SSTDR) is a broadband electrical reflectometry technique that has been used to detect and locate faults on live electrical systems, including photovoltaic systems. In this article, we evaluate the detectability and localizability from both existing literature...

Time domain reflectometry is frequently used to localize faults in electrical systems. Most existing literature on reflectometry in transmission lines considers symmetric faults that are either shorts between the two conductors or open circuits where both conductors are disconnected at the same location. This paper investigates spread spectrum time...

Spread spectrum time domain reflectometry (SSTDR) has been traditionally used to detect hard faults (open and short circuit faults) in transmission lines. Prior work has focused on loads at the end of the line with little research on impedances from circuit elements located in the middle of the line (i.e., not at the load) or on only one wire of th...

This paper studies the detection of hidden polymer matrix composite delaminations with a pitch–catch ultrasonic testing system and an agglomerative clustering algorithm. Existing ultrasonic testing methods characterize damage through normal-incidence pulse-echo measurements. Yet, these pulse-echo methods are ineffective at detecting delaminations u...

This article describes the novel use of spread-spectrum time-domain reflectometry (SSTDR) for detecting and locating disconnection faults in photovoltaic (PV) power plants. We measure strings of cells and full-sized modules to understand how disconnections affect the reflectometry signature. PV modules correspond to reactive loads and disconnection...

Ultrasonic guided waves are commonly used to localize structural damage in infrastructures such as buildings, airplanes, bridges. Damage localization can be viewed as an inverse problem. Physical model based techniques are popular for guided wave based damage localization. The performance of these techniques depend on the degree of faithfulness wit...

Go to -- https://doi.org/10.24433/CO.1017684.v1 -- to interact with this code. This script demonstrates temporal sparse wavenumber analysis to reconstruct / complete guided wave / Lamb wave data from temporally incomplete measurements. This is demonstrated with simulated Lamb wave data.

Go to -- https://doi.org/10.24433/CO.0906211.v1 -- to interact with our code. In this script, we introduce data-driven matched field processing, a framework to build models of multimodal propagation environments from measured data and then uses these models to locate damage. The example code demonstrates data-driven matched field processing's local...

DOWNLOAD LINK: https://utah.instructure.com/courses/558911/files/87377947/download?wrap=1 Spread spectrum time domain reflectometry (SSTDR) can be used to measure complex impedances in live electrical systems by evaluating the shape and magnitude of the reflected signature. A range of capacitors or inductors can be identified, but above and below t...

Objective: Norepinephrine (NE), an endogenous catecholamine, is a mainstay treatment for septic shock, which is a life-threatening manifestation of severe infection. NE counteracts the loss in blood pressure associated with septic shock. However, an NE infusion that is too low fails to counteract the blood pressure drop, and an NE infusion that is...

Guided wave methodologies are among the established approaches for structural health monitoring. For guided wave data, being able to accurately estimate wave properties in the absence of ample measurements can greatly facilitate the often time-consuming and potentially expensive data acquisition procedure. Nevertheless, inherent complexities of the...

Reflectometry, commonly used for locating faults on electrical wires, produces sampled time domain signatures with peaks that are often missed due to this sampling. Resultant errors in these sampled peaks translate to errors in calculating the impedance and location of the fault. Typical signal processing methods to improve the accuracy of these sa...

Recent high-profile successes in machine learning have found solutions to problems that were long-thought to be decades away and has generated renewed interest in artificial intelligence (AT) and machine learning (ML) research. For example, the recent success and rapid commercialization of deep learning has catapulted technical achievements in many...

Guided wave structural health monitoring is used to inspect large structures with ultrasonic waves. To detect damage, statistics are typically computed from raw guided wave responses. Many current detection methods use single-signal statistics and batch statistics [1]. Single-signal statistics are derived from a single measurement while batch stati...

This paper describes new algorithms for spread spectrum time domain reflectometry (SSTDR) for detecting and locating faults in photovoltaic (PV) panels. Specifically, we present a new method for identifying the impedance of multiple loads (such as PV panels) on a single transmission line. This method is based on adapting a well-known algorithm, kno...

In non-destructive evaluation/testing (NDE/NDT) and structural health monitoring (SHM) applications, guided waves are commonly employed and widely studied. Wave behavior characterization and analysis can be vital in determining the state of the structure under inspection. Effective analysis of guided waves, however, is encumbered by their intricate...

In recent years, the use of scanning laser Doppler vibrometery and full wavefield acquisition has grown to aid the char- acterization of ultrasonic waves and the detection of structural defects. Yet, these methods require a considerable amount of time to acquire full wavefield data. Therefore, there is a significant need to reduce acquisition time....

Go to --- https://doi.org/10.24433/CO.2630229.v1 --- to interact with our code. In this script, we show an example of applying sparse wavenumber analysis (SWA) to simulation data corrupted by multipath interference. This code learns the dispersion curves (i.e., the frequency-wavenumber representation) from wave data collected from spatially sparse...

Degradation in performance can occur in PV arrays due to faults in wiring or modules themselves. It is crucial to identify and locate the faults specially disconnections to reduce power loss in PV arrays. The main goal of our project is to monitor operating strings of PV modules using Spread Spectrum Time Domain Reflectometry (SSTDR) technology. We...

This paper studies a computationally efficient algebraic graph theory engine for simulating time-domain one-dimensional waves in a multi-segment transmission line, such as for reflectometry applications. Efficient simulation of time-domain signals in multi-segment transmission lines is challenging because the number of propagation paths (and theref...

We present a framework for analyzing electromagnetic signal propagation through piecewise-defined transmission lines with arbitrary, series-connected impedances. While the formulation is general and scalable, we apply it here to propagation through a photovoltaic module with cables on either side acting, with a home run cable, as a section of an in...

One of the primary challenges with additively manufactured metal components is the potential for the material heterogeneity, such as the variations in density, porosity, and elasticity. These heterogeneities are difficult to predict and can cause the deviation in material strength and structural reliability from the specified design. This paper pre...

The inability to statistically evaluate the damage size based on amplitude mapping has long plagued the ultrasound propagation imaging (UPI) system and related non-destructive evaluation (NDE) community. This paper proposes a damage visualization method called the Statistically Thresholded Anomaly Mapping (STAM) method to solve this problem. It cou...

Spread spectrum time domain reflectometry (SSTDR) has previously been used for detection and location of intermittent faults on live electrical wiring. These intermittent faults can be open circuits, short circuits, or resistive changes, all of which preserve the original shape of the SSTDR correlated waveform. But things are very different when SS...

The multi-modal and dispersive behavior of guided waves is often characterized by their dispersion curves, which describe their frequency-wavenumber behavior. In prior work, compressive sensing based techniques, such as sparse wavenumber analysis (SWA), have been capable of recovering dispersion curves from limited data samples. A major limitation...

Guided wave localization methods based on delay-and-sum imaging, matched field processing, and other techniques have been designed and researched to create images that locate and describe structural damage. The maximum value of these images typically represent an estimated damage location. Yet, it is often unclear if this maximum value, or any othe...

Structural health monitoring using ultrasonic guided waves relies on accurate interpretation of guided wave propagation to distinguish damage state indicators. However, traditional physics based models do not provide an accurate representation, and classic data driven techniques, such as a support vector machine, are too simplistic to capture the c...