Baseline subtraction is commonly used in guided wave structural health monitoring to identify the signal changes produced by defects. However, before subtracting the current signal from the baseline, it is essential to compensate for changes in environmental conditions such as temperature between the two readings. This is often done via the baseline stretch method that seeks to compensate for wave velocity changes with temperature. However, the phase of the signal generated by the transduction system is also commonly temperature sensitive and this effect is neglected in the usual compensation procedure. This article presents a new compensation procedure that deals with both velocity and phase changes. The results with this new method have been compared with those obtained using the standard baseline stretch technique on both a set of experimental signals and a series of synthetic signals with different coherent noise levels, feature reflections, and defect sizes, the range of noise levels and phase changes being chosen based on initial experiments and prior field experience. It has been shown that the new method both reduces the residual signal from a set baseline and enables better defect detection performance than the conventional baseline signal stretch method under all conditions examined, the improvement increasing with the size of the temperature and phase differences encountered. For example, in the experimental data, the new method roughly halved the residual between baseline and current signals when the two signals were acquired at temperatures 15°C apart.

In guided wave structural health monitoring, defects are typically detected by identifying high residuals obtained through the baseline subtraction method, where an earlier measurement is subtracted from the "current" signal. Unfortunately, varying environmental and operational conditions (EOCs), such as temperature, also produce signal changes and hence, potentially, high residuals. While the majority of the temperature compensation methods that have been developed target the changed wave speed induced by varying temperature, a number of other effects are not addressed, such as the changes in attenuation, the relative amplitudes of different modes excited by the transducer, and the transducer frequency response. A temperature compensation procedure is developed, whose goal is to correct any spatially dependent signal change that is a systematic function of temperature. At each structural position, a calibration function that models the signal variation with temperature is computed and is used to correct the measurements, so that in the absence of a defect the residual is reduced to close to zero. This new method was applied to a set of guided wave signals collected in a blind trial of a guided wave pipe monitoring system using the T(0, 1) mode, yielding residuals de-coupled from temperature and reduced by at least 50% as compared with those obtained using the standard approach at positions away from structural features, and by more than 90% at features such as the pipe end. The method, therefore, promises a substantial improvement in the detectability of small defects, particularly at the existing pipe features.

This article describes a new system for high-speed and noncontact rail integrity evaluation being developed at the University of California at San Diego. A prototype using an ultrasonic air-coupled guided wave signal generation and aircoupled signal detection has been tested at the University of California at San Diego Rail Defect Farm. In addition to a real-time statistical analysis algorithm, the prototype uses a specialized filtering approach due to the inherently poor signal-to-noise ratio of the air-coupled ultrasonic measurements in rail steel. The laboratory results indicate that the prototype is able to detect internal rail defects with a high reliability. Extensions of the system are planned to add rail surface characterization to the internal rail defect detection. In addition to the description of the prototype and test results, numerical analyses of ultrasonic guided wave propagation in rails have been performed using a Local Interaction Simulation Approach algorithm and some of these results are shown. The numerical analysis has helped designing various aspects of the prototype for maximizing its sensitivity to defects.

The general topic of this paper is the passive reconstruction of an acoustic transfer function from an unknown, generally nonstationary excitation. As recently shown in a study of building response to ground shaking, the paper demonstrates that, for a linear system subjected to an unknown excitation, the deconvolution operation between two receptions leads to the Green's function between the two reception points that is independent of the excitation. This is in contrast to the commonly used cross-correlation operation for passive reconstruction of the Green's function, where the result is always filtered by the source energy spectrum (unless it is opportunely normalized in a manner that makes it equivalent to a deconvolution). This concept is then applied to high-speed ultrasonic inspection of rails by passively reconstructing the rail's transfer function from the excitations naturally caused by the rolling wheels of a moving train. A first-generation prototype based on this idea was engineered using noncontact air-coupled sensors, mounted underneath a test railcar, and field tested at speeds up to 80 mph at the Transportation Technology Center (TTC), Pueblo, CO. This is the first demonstration of passive inspection of rails from train wheel excitations and, to the authors' knowledge, the first attempt ever made to ultrasonically inspect the rail at speeds above ∼30 mph (that is the maximum speed of common rail ultrasonic inspection vehicles). Once fully developed, this novel concept could enable regular trains to perform the inspections without any traffic disruption and with great redundancy.

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The transition from one-off ultrasound–based non-destructive testing systems to permanently installed monitoring techniques has the potential to significantly improve the defect detection sensitivity, since frequent measurements can be obtained and tracked with time. However, the measurements must be compensated for changing environmental and operational conditions, such as temperature, and careful analysis of measurements by highly skilled operators quickly becomes unfeasible as a large number of sensors acquiring signals frequently is installed on a plant. Recently, the authors have developed a location-specific temperature compensation method that uses a set of baseline measurements to remove temperature effects from the signals, thus producing “residual” signals on an unchanged structure that are essentially normally distributed with zero-mean and with standard deviation related to instrumentation noise. This enables the application of change detection techniques such as the generalized likelihood ratio method that can process sequences of residual signals searching for changes caused by damage. The defect detection performance offered by the generalized likelihood ratio when applied to guided wave signals adjusted either via the newly developed location-specific temperature compensation method or the widely used optimal baseline selection technique is investigated on a set of simulated measurements based on a set of experimental signals acquired by a permanently installed pipe monitoring system designed to monitor tens of meters of pipe from one location using the torsional, T(0,1), guided wave mode. The results presented here indicate that damage on the order of 0.1% cross section loss can reliably be detected with virtually zero false calls if the assumptions of the study are met, notably the absence of sensor drift with time. This represents a factor of 20–50 improvement over that typically achieved in one-off inspection and makes such monitoring systems very attractive. The method will also be applicable to bulk wave ultrasound signals.

Data-driven damage localization is an important step of vibration-based structural health monitoring. Statistical pattern recognition based on the prominent steps of feature extraction and statistical decision-making provides an effective and efficient framework for structural health monitoring. However, these steps may become time-consuming or complex when there are large volumes of vibration measurements acquired by dense sensor networks. To deal with this issue, this study proposes fast unsupervised learning methods for feature extraction through autoregressive modeling and damage localization through a new distance measure called Kullback–Leibler divergence with empirical probability measure. The feature extraction approach consists of an iterative algorithm for order selection and parameter estimation aiming to extract residuals in the training phase and another iterative process aiming to extract residuals only in the monitoring phase. The key feature of the proposed approach is the use of correlated residual samples of the autoregressive model as a new time series at each iteration, rather than handling the measured vibration response of the structure. This is shown to highly reduce the computational burden of order selection and feature extraction; moreover, it effectively provides low-order autoregressive models with uncorrelated residuals. The Kullback–Leibler divergence with empirical probability measure method exploits a segmentation technique to subdivide random data into independent sets and provides a distance metric based on the theory of empirical probability measure with no need to explicitly compute the actual probability distributions at the training and monitoring stages. Numerical and experimental benchmarks are then used to assess accuracy and performance of the proposed methods and compare them with some state-of-the-art approaches. Results show that the proposed approaches are successful in feature extraction and damage localization, with a reduced computational burden.

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