Joel B. Harley

Sparse recovery of the multimodal and dispersive characteristics of Lamb wavesa)

Guided waves in plates, known as Lamb waves, are characterized by complex, multimodal, and frequency dispersive wave propagation, which distort signals and make their analysis difficult. Estimating these multimodal and dispersive characteristics from experimental data becomes a difficult, underdetermined inverse problem. To accurately and robustly recover these multimodal and dispersive properties, this paper presents a methodology referred to as sparse wavenumber analysis based on sparse recovery methods. By utilizing a general model for Lamb waves, waves propagating in a plate structure, and robust l1 optimization strategies, sparse wavenumber analysis accurately recovers the Lamb waves frequency-wavenumber representation with a limited number of surface mounted transducers. This is demonstrated with both simulated and experimental data in the presence of multipath reflections. With accurate frequency-wavenumber representations, sparse wavenumber synthesis is then used to accurately remove multipath interference in each measurement and predict the responses between arbitrary points on a plate.

Scale transform signal processing for optimal ultrasonic temperature compensation

In structural health monitoring, temperature compensation is an important step to reduce systemic errors and avoid false-positive results. Several methods have been developed to accomplish temperature compensation in guided wave systems, but these techniques are often limited in computational speed. In this paper, we present a new methodology for optimal, stretch-based temperature compensation that operates on signals in the stretch factor and scale-transform domains. Using these tools, we demonstrate three algorithms for temperature compensation that show improved computational speed relative to other optimal methods. We test the performance of these algorithms using experimental guided wave data.

Data-driven matched field processing for Lamb wave structural health monitoring

Matched field processing is a model-based framework for localizing targets in complex propagation environments. In underwater acoustics, it has been extensively studied for improving localization performance in multimodal and multipath media. For guided wave structural health monitoring problems, matched field processing has not been widely applied but is an attractive option for damage localization due to equally complex propagation environments. Although effective, matched field processing is often challenging to implement because it requires accurate models of the propagation environment, and the optimization methods used to generate these models are often unreliable and computationally expensive. To address these obstacles, this paper introduces data-driven matched field processing, a framework to build models of multimodal propagation environments directly from measured data, and then use these models for localization. This paper presents the data-driven framework, analyzes its behavior under unmodeled multipath interference, and demonstrates its localization performance by distinguishing two nearby scatterers from experimental measurements of an aluminum plate. Compared with delay-based models that are commonly used in structural health monitoring, the data-driven matched field processing framework is shown to successfully localize two nearby scatterers with significantly smaller localization errors and finer resolutions.

Robust ultrasonic damage detection under complex environmental conditions using singular value decomposition.

Guided wave ultrasonics is an attractive monitoring technique for damage diagnosis in large-scale plate and pipe structures. Damage can be detected by comparing incoming records with baseline records collected on intact structure. However, during long-term monitoring, environmental and operational conditions often vary significantly and produce large changes in the ultrasonic signals, thereby challenging the baseline comparison based damage detection. Researchers developed temperature compensation methods to eliminate the effects of temperature variation, but they have limitations in practical implementations. In this paper, we develop a robust damage detection method based on singular value decomposition (SVD). We show that the orthogonality of singular vectors ensures that the effect of damage and that of environmental and operational variations are separated into different singular vectors. We report on our field ultrasonic monitoring of a 273.05 mm outer diameter pipe segment, which belongs to a hot water piping system in continuous operation. We demonstrate the efficacy of our method on experimental pitch-catch records collected during seven months. We show that our method accurately detects the presence of a mass scatterer, and is robust to the environmental and operational variations exhibited in the practical system.

Toward Data-Driven Structural Health Monitoring: Application of Machine Learning and Signal Processing to Damage Detection

AbstractA multilayer data-driven framework for robust structural health monitoring based on a comprehensive application of machine learning and signal processing techniques is introduced. This paper focuses on demonstrating the effectiveness of the framework for damage detection in a steel pipe under environmental and operational variations. The pipe was instrumented with piezoelectric wafers that can generate and sense ultrasonic waves. Damage was simulated physically by a mass scatterer grease-coupled to the surface of the pipe. Benign variations included variable internal air pressure and ambient temperature over time. Ultrasonic measurements were taken on three different days with the scatterer placed at different locations on the pipe. The wave patterns are complex and difficult to interpret, and it is even more difficult to differentiate the changes produced by the scatterer from the changes produced by benign variations. The sensed data were characterized by 365 features extracted from a variety of...

Dispersion curve recovery with orthogonal matching pursuit

Dispersion curves characterize many propagation mediums. When known, many methods use these curves to analyze waves. Yet, in many scenarios, their exact values are unknown due to material and environmental uncertainty. This paper presents a fast implementation of sparse wavenumber analysis, a method for recovering dispersion curves from data. This approach, based on orthogonal matching pursuit, is compared with a prior implementation, based on basis pursuit denoising. In the results, orthogonal matching pursuit provides two to three orders of magnitude improvement in speed and a small average reduction in prediction capability. The analysis is demonstrated across multiple scenarios and parameters.

Detection of structural defects in pipes using time reversal of guided waves

Structural health monitoring of buried pipelines is of vital importance as infrastructures age. Ultrasonic guided waves are a popular method for inspecting buried pipes, due to their potential for long propagation. Unfortunately, the large number of wave modes present, and the effects of dispersion, in a pipeline make analysis of the received signals difficult. We plan to use Time Reversal Acoustics to compensate for these complex signals, and improve performance for the detection of faults in a pipeline. We will present theoretical performance results for conventional and Time Reversal detectors, verified with simulations conducted in PZFlex. Time Reversal shows a potential for a reduction in the power requirements of a fault detection system.

Application of Mellin transform features for robust ultrasonic guided wave structural health monitoring

Guided wave based structural health monitoring systems are sensitive to environmental and operational conditions. This leads to false-positive results for most conventional detection methods. In this paper, we investigate the capabilities of the Mellin transform for detecting damage under variable environmental conditions. The Mellin transform is chosen due to its invariance to scaling operations and robustness to wave velocity. From experimental results, we demonstrate that the Mellin transform features can detect a mass on a steel pipe under variable internal pressure with an overall average accuracy of 94.00% while equivalent Fourier transform features detect the mass with only a 67.00% accuracy.

Broadband localization in a dispersive medium through sparse wavenumber analysis

Matched field processing is a powerful tool for accurately localizing targets in dispersive media. However, matched field processing requires a precise model of the medium under test. In underwater acoustics, where matched field processing has been extensively studied, authors often resort to extremely detailed numerical models of the propagation medium, which are computationally expensive and impractical for many applications. As an alternative, this paper uses convex sparse recovery techniques to construct, directly from measured data, an accurate model of a plate medium based on its dispersion characteristics. From this data-driven model, the Greens function between two points can be readily predicted. We demonstrate the effectiveness of this model by localizing a source in a dispersive plate medium. The results visually illustrate our approach to significantly improve localization accuracy and reduce artifacts when compared to a conventional narrowband technique.

Ultrasonic monitoring of a pipe under operating conditions

The paper presents experimental results of applying an ultrasonic monitoring system to a real-world operating hot-water supply system. The purpose of these experiments is to investigate the feasibility of continuous ultrasonic damage detection on pipes with permanently mounted piezoelectric transducers under environmental and operational variations. Ultrasonic guided wave is shown to be an efficient damage detector in laboratory experiments. However, environmental and operational variations produce dramatic changes in those signals, and therefore a useful signal processing approach must distinguish change caused by a scatterer from change caused by ongoing variations. We study pressurized pipe segments (10-in diameter) in a working hot-water supply system that experiences ongoing variations in pressure, temperature, and flow rate; the system is located in an environment that is mechanically and electrically noisy. We conduct pitch-catch tests, with a duration of 10 ms, between transducers located roughly 12 diameters apart. We applied different signal processing techniques to the collected data in order to investigate the ongoing environmental and operational variations and the stationarity of the signal. We present our analysis of these signals and preliminary detection results.