Location Specific Temperature Compensation of Guided Wave Signals in Structural Health Monitoring

Abstract

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.

Change detection using the generalized likelihood ratio method to improve the sensitivity of guided wave structural health monitoring systems

“…The results were partially validated using the experimental signals by showing that the residuals produced by the LSTC method are normally distributed, which is a key assumption of the framework, and that the GLR test scores obtained on the undamaged structure are on the order of those predicted by the numerical study. Although the analyzed experimental data set did not contain any actual defect, it should be stressed that the proposed method is sensitive to departures from the residuals obtained on the undamaged structure, as would be produced by LSTC when damage occurs; 6 this sensitivity was tested by superposing a simulated defect reflection of amplitude 1.5 times the standard deviation of the measured incoherent noise (which would be produced by damage on the order of 0.1% pipe cross section loss). Again, the GLR test scores obtained from such hybrid experimental and simulated measurements confirmed the predictions of the numerical study.…”

“…The pipe was subjected to heating and cooling cycles between 30°C and 90°C, and a total of 250 measurements were acquired during the cooling phases, as seen in Figure 14(b) that shows the pipe temperature measured by a sensor installed near the sensor ring. Figure 15 shows the measurement that was used as baseline for the BSS temperature compensation method, 8 which was needed to compensate for the varying T(0,1) wave speed at the different measurement temperatures, and which was performed prior to the application of the LSTC compensation method 6 for the reasons given in section ''Creation of the test data set.'' In Figure 15, the time-domain ultrasonic signal is converted to distance using a T(0,1) velocity of 3240 m/s, which was estimated from the arrival time of the reflection from the end of the pipe and the known pipe length.…”

“…In ultrasonicbased SHM, where either bulk waves or guided waves are used, ''residuals'' are typically obtained via amplitude subtraction between a ''current'' measurement (taken when the structure is at unknown health state) and a ''baseline'' signal (taken at an earlier time, when the structure was deemed defect-free), although the signals need first to be compensated for the effects of environmental and operational conditions, where the predominant factor is typically temperature. [2][3][4][5][6] Two widely used methods for temperature compensation are baseline signal stretch (BSS) and optimal baseline selection (OBS). While the former relies on storing only one baseline signal that is used as a reference to either compress or stretch in time any current measurement to minimize the residuals, 7,8 in OBS, a set of baseline signals is stored so that the one deemed most similar to the specific current measurement is used for amplitude subtraction, often after also applying BSS on the selected baseline.…”

Evaluating the Probability of Detection Capability of Permanently Installed Sensors Using a Structural Integrity Informed Approach

“…For the purpose of simple illustration, only the effect of random noise is considered in the present study. While repeated measurements have the effect of averaging out and suppressing the influence of random incoherent noise, the detection capabilities may not improve ad infinitum due to environmental influence [44] and parametric uncertainty [4]. In real-life applications, this would have to be further investigated for each individual case in order to accurately quantify the detection capabilities.…”

Ultrasonic Guided-Waves Sensors and Integrated Structural Health Monitoring Systems for Impact Detection and Localization: A Review

“…This approach is also rather simple to implement in sensors with on board embedded processing, but it suffers from the sensitivity to environmental and operational conditions, mainly temperature variations. Recently, Mariani et al [ 45 , 46 ] have proposed a method for the compensation of this detrimental phenomenon. For the electro-mechanical-impedance (EMI) method, the temperature compensation was achieved with some benefits by using artificial neural network (ANN) as reported by Sepehry et al [ 47 ].…”