Chang Liu

Bio: Chang Liu is an academic researcher from Carnegie Mellon University. The author has contributed to research in topics: Structural health monitoring & Guided wave testing. The author has an hindex of 7, co-authored 21 publications receiving 203 citations. Previous affiliations of Chang Liu include Imperial College London.

Efficient generation of receiver operating characteristics for the evaluation of damage detection in practical structural health monitoring applications.

Abstract: Permanently installed guided wave monitoring systems are attractive for monitoring large structures. By frequently interrogating the test structure over a long period of time, such systems have the potential to detect defects much earlier than with conventional one-off inspection, and reduce the time and labour cost involved. However, for the systems to be accepted under real operational conditions, their damage detection performance needs to be evaluated in these practical settings. The receiver operating characteristic (ROC) is an established performance metric for one-off inspections, but the generation of the ROC requires many test structures with realistic damage growth at different locations and different environmental conditions, and this is often impractical. In this paper, we propose an evaluation framework using experimental data collected over multiple environmental cycles on an undamaged structure with synthetic damage signatures added by superposition. Recent advances in computation power enable examples covering a wide range of practical scenarios to be generated, and for multiple cases of each scenario to be tested so that the statistics of the performance can be evaluated. The proposed methodology has been demonstrated using data collected from a laboratory pipe specimen over many temperature cycles, superposed with damage signatures predicted for a flat-bottom hole growing at different rates at various locations. Three damage detection schemes, conventional baseline subtraction, singular value decomposition (SVD) and independent component analysis (ICA), have been evaluated. It has been shown that in all cases, the component methods perform significantly better than the residual method, with ICA generally the better of the two. The results have been validated using experimental data monitoring a pipe in which a flat-bottom hole was drilled and enlarged over successive temperature cycles. The methodology can be used to evaluate the performance of an installed monitoring system and to show whether it is capable of detecting particular damage growth at any given location. It will enable monitoring results to be evaluated rigorously and will be valuable in the development of safety cases.

Ultrasonic monitoring of a pipe under operating conditions

Abstract: 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.

Robust change detection in highly dynamic guided wave signals with singular value decomposition

Abstract: Ultrasonic guided waves are sensitive to small scatterers and can, in principle, be used to detect damage in pipe structures. However, pipes are often subjected to varying environmental and operational conditions (EOC), which can produce false positives or mask the change of interest. We apply singular value decomposition as a robust change detection method in ultrasonic signals. We test the methods on experimental data collected in a realistic highly dynamic environment, and show successful detection of a mass scatterer as a physical simulation of damage. We also compare our method to two other change detection methods that are robust to EOC.

Singular value decomposition for novelty detection in ultrasonic pipe monitoring

Abstract: Guided wave ultrasonics is an attractive technique for structural health monitoring, especially on pressurized pipes. However, civil infrastructure components, including pipes, are often subject to large environmental and operational variations that prevent traditional baseline subtraction-based approaches from detecting damage. We collect ultrasonic data on a large-scale pipe segment in its normal operating conditions and observe large environmental variations. We developed a damage detection method based on singular value decomposition (SVD) that is robust to those benign variations. We further develop an online novelty detection framework based on our SVD method to detect the presence of a mass scatterer on the pipe at the same time that we collect the data. We examine the framework with both synthetic simulations and field experimental data. The results show that the framework can effectively detect the presence of a scatterer and is robust to large environmental and operational variations.

Guided wave based structural health monitoring: A review

Abstract: The paper provides a state of the art review of guided wave based structural health monitoring (SHM). First, the fundamental concepts of guided wave propagation and its implementation for SHM is explained. Following sections present the different modeling schemes adopted, developments in the area of transducers for generation, and sensing of wave, signal processing and imaging technique, statistical and machine learning schemes for feature extraction. Next, a section is presented on the recent advancements in nonlinear guided wave for SHM. This is followed by section on Rayleigh and SH waves. Next is a section on real-life implementation of guided wave for industrial problems. The paper, though briefly talks about the early development for completeness,. is primarily focussed on the recent progress made in the last decade. The paper ends by discussing and highlighting the future directions and open areas of research in guided wave based SHM.

Statistical Methods For Environmental Pollution Monitoring

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Structural health monitoring: Closing the gap between research and industrial deployment:

Abstract: There has been a large volume of research on structural health monitoring since the 1970s but this research effort has yielded relatively few routine industrial applications. Structural health monitoring can include applications on very different structures with very different requirements; this article splits the subject into four broad categories: rotating machine condition monitoring, global monitoring of large structures (structural identification), large area monitoring where the area covered is part of a larger structure, and local monitoring. The capabilities and potential applications of techniques in each category are discussed. Condition monitoring of rotating machine components is very different to the other categories since it is not strictly concerned with structural health. However, it is often linked with structural health monitoring and is a relatively mature field with many routine applications, so useful lessons can be read across to mainstream structural health monitoring where there ar...

Data-driven support vector machine with optimization techniques for structural health monitoring and damage detection

Abstract: Rapid detecting damages/defeats in the large-scale civil engineering structures, assessing their conditions and timely decision making are crucial to ensure their health and ultimately enhance the level of public safety. Advanced sensor network techniques recently allow collecting large amounts of data for structural health monitoring and damage detection, while how to effectively interpret these complex sensor data to technical information posts many challenges. This paper presents three optimization-algorithm based support vector machines for damage detection. The optimization algorithms, including grid-search, partial swarm optimization and genetic algorithm, are used to optimize the penalty parameters and Gaussian kernel function parameters. Two types of feature extraction methods in terms of time-series data are selected to capture effective damage characteristics. A benchmark experimental data with the 17 different scenarios in the literature were used for verifying the proposed data-driven methods. Numerical results revealed that all three optimized machine learning methods exhibited significantly improvement in sensitivity, accuracy and effectiveness over conventional methods. The genetic algorithm based SVM had a better prediction than other methods. Two different feature methods used in this study also demonstrated the appropriate features are crucial to improve the sensitivity in detecting damage and assessing structural health conditions. The findings of this study are expected to help engineers to process big data and effectively detect the damage/defects, and thus enable them to make timely decision for supporting civil infrastructure management practices.