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Temperature Compensation Strategies for Lamb Wave Inspection using Distributed Sensor Networks
(2022)
The application of temperature compensation strategies is crucial in structural health monitoring approaches based on guided waves. Actually, the varying temperature influences the performance of the inspection system inducing false alarms or missed detection, with a consequent reduction of reliability. This paper quantitatively describes a method to compensate the temperature effect, namely the optimal baseline selection (OBS), extending its application to the case of distributed sensor networks (DSN). The effect of temperature separation between baseline time-traces in OBS are investigated considering multiple couples of sensors employed in the DSN. A combined strategy that uses both OBS and frequent value warning is considered. Theoretical results are compared, using data from two several experiments, which use different frequency analysis with either predominantly A0 mode or S0 mode data or both. The focus is given on the fact that different paths are available in a sensor network and several possible combination of results are available. Nonetheless, introducing a frequent value warning it is possible to increase the efficiency of the OBS approach making use of fewer signal processing algorithms. These confirm that the performance of OBS quantitatively agrees with predictions and also demonstrate that the use of compensation strategies improve detectability of damage.
Performance assessment for GuidedWave (GW)-based Structural Health Monitoring (SHM) systems is of major importance for industrial deployment.
With conventional feature extraction methods like damage indices, pathbased probability of detection (POD) analysis can be realized. To achieve reliability quantification enough data needs to be available, which is rarely the case.
Alternatives like methods for performance assessment on system level are still in development and in a discussion phase. In this contribution, POD results using an Artificial Intelligence (AI)-based data analysis are compared with those delivered by conventional data analysis. Using an open-access dataset from Open Guided Wave platform, the possibility of performance assessment for GW-based SHM systems using AI-based data analysis is shown in detail. An artificial neural network (ANN) classifier is trained to detect artificial damage in a stiffened CFRP plate. As input for the ANN, classical damage indicators are used. The ANN is tested to detect damage at another position, whose inspection data were not previously used in training. The findings show very high detection capabilities without sorting any specific path but only having a global view of current damage metrics. The systematic evaluation of the ANN predictions with respect to specific damage sizes allows to compute a probability of correct identification versus flaw dimension, somehow equivalent to and compared with the results achieved through classic path-based POD analysis. Also, sensitive paths are detected by ANN predictions allowing for evaluation of maximal distances between path and damage position. Finally, it is shown that the prediction performance of the ANN can be improved significantly by combining different damage indicators as inputs.
Reliability assessment of Structural Health Monitoring (SHM) systems
poses new challenges pushing the research community to address many questions which are still open. For guided wave-based SHM it is not possible to evaluate the system performance without taking into account the target structure and applied system parameters. This range of variables would result in countless measurements.
Factors like environmental conditions, structural dependencies and wave characteristics demand novel solutions for performance analysis of SHM systems compared to those relying on classical non-destructive evaluation. Such novel approaches typically require model-assisted investigations which may not only help to explain and understand performance assessment results but also enable complete studies without costly experiments. Within this contribution, a multi input multi output approach using a sparse transducer array permanently installed on a composite structure to excite and sense guided waves is considered. Firstly, the method and the analysis of path-based performance assessment are presented considering an open-access dataset from the Open Guided Wave platform. Then, a performance analysis of a guided wave-based SHM system using Probability of Detection is presented. To explain some unexpected results, the model-assisted investigations are used to understand the physical phenomena of wave propagation in the test specimen including the interaction with damage. Finally, issues and future steps in SHM systems’ performance assessment and their development are discussed.
A semi-automatic thermographic procedure for the assessment of the welded area of resistance projection welded joints has been developed. Currently, to assess the quality of RPW joints destructive tests are used and the more commonly used non-destructive technique is the ultrasonic one. The possibility for a quantitative evaluation of the welded area by thermographic technique has been proved by means of an innovative procedure applied on steel RPW joints with ‘as it’ surface conditions. Measurements obtained by thermography and ultrasound have been compared, to verify the developed procedure.
Understanding on how a machine learning model interprets data is a crucial step to verify its reliability and avoid overfitting. While the focus of the scientific community is nowadays orientated towards deep learning approaches, which are considered as black box approaches, this work presents a toolbox that is based on complementary methods of feature extraction and selection, where the classification decisions of the model are transparent and can be physically interpreted. On the example of guided wave benchmark data from the open guided waves platform, where delamination defects were simulated at multiple positions on a carbon fiber reinforced plastic plate under varying temperature conditions, the authors could identify suitable frequencies for further investigations and experiments. Furthermore, the authors presented a realistic validation scenario which ensures that the machine learning model learns global damage characteristics rather than position specific characteristics.
Understanding on how a machine learning model interprets data is a crucial step to verify its reliability and avoid overfitting. While the focus of the scientific community is nowadays orientated towards deep learning approaches, which are considered as black box approaches, this work presents a toolbox that is based on complementary methods of feature extraction and selection, where the classification decisions of the model are transparent and can be physically interpreted. On the example of guided wave benchmark data from the open guided waves platform, where delamination defects were simulated at multiple positions on a carbon fiber reinforced plastic plate under varying temperature conditions, the authors could identify suitable frequencies for further investigations and experiments. Furthermore, the authors presented a realistic validation scenario which ensures that the machine learning model learns global damage characteristics rather than position specific characteristics.
Structural health monitoring (SHM) is a term that groups together techniques adopted to evaluate in a continued fashion the structural integrity and degradation of technical appliances. SHM is particularly attractive for components that are difficult to access or expensive to take off-line. Among many other techniques, SHM can be performed using ultrasonic guided waves (UGW) which have an advantage of traveling over Long distances. Various guided wave modes exist along with many methods for their generation and sensing, e.g by means of interdigital transducers (IDT). This contribution is dedicated to the design and characterization of a flexible piezopolymerbased IDT which allows for the selective excitation of UGW, resulting in more straightforward data analysis. The designed IDT was characterized using a 3D Laser Doppler Vibrometer (3D LDV) in the air to identify and analyze the IDT’s Vibration modes. Then the transducer was mounted on an aluminum plate, and the generated wavefield was measured with the 3D LDV.
According to this investigation, we demonstrate that it is possible to selectively excite desired guided wave mode, namely the A0 mode, suppressing the excitation of the S0 mode. Moreover, the measured wavefield allows for analysis of the directivity of the designed IDT. All in all the results show good correlation between theoretical predictions and measured values, thus allowing to use the current design in terms of selective excitation as it is.
Hollow axle inspection can be performed without demounting the axles and without dismantling the wheels and the brake discs by using the drilling for the scan. To increase inspection reliability and inspection speed, the application of phased array systems instead of conventional probes is a good choice. For solid shaft inspection phased array setups became standard in the recent years. Nevertheless, for hollow axle inspection typically a number of conventional probes rotating through the axles drilling are applied.
The new approach uses an electronically steered rotating sound field from a phased array for the circumferential scan. This is realized by a cone shaped phased array which operates in immersion technique. That allows a significant increase in inspection speed and a reduction of the mechanical effort of the inspection system. The inspection can be carried out by a linear movement of the probe setup along the axles drilling. Applying additional focal laws allows exact inclination and focusing of the sound beam in the plane vertical to the specimen axis to concentrate the sound in the zones close to the external surface. An additional focus in the plane of incidence increases overall resolution and sensitivity.
The cone type phased array probe has been optimized to detect transversal flaws in and close to the outer surface of the hollow axle with orientation in the radial-radial plane. The prototype probe system, sound field simulations and measurement results are presented.
Magneto resistance sensors like GMR (giant magneto resistance) or TMR (tunnel magneto resistance) are widely used in industrial applications, examples are position measurement and read heads of hard disk drives. However, in case of non-destructive testing (NDT) applications these sensors, although their properties are outstanding like high spatial resolution, high field sensitivity, low cost and low energy consumption, never reached a technical transfer to an application beyond sci-entific scope.
This paper deals with benefits of GMR/TMR sensors in terms of high spatial resolution testing for different NDT applica-tions. The first example demonstrates the preeminent advantages of MR-elements compared with conventional coils used in eddy current testing (ET). The probe comprises one-wire excitation with an array of MR elements. This led to a better spatial resolution in terms of neighboring defects.
The second section concentrates on MFL-testing (magnetic flux leakage) with active field excitation during and before test-ing. The latter illustrated the capability of highly resolved crack detection of a crossed notch. This example is best suited to show the ability of tiny magnetic field sensors for magnetic material characterization of a sample surface. Another example is based on characterization of samples after tensile test. Here, no external field is applied. The magnetization is only changed due to external load and magnetostriction leading to a field signature which GMR sensors can resolve. This gives access to internal changes of the magnetization state of the sample under test.
Austenitic stainless steel welds as well as dissimilar metal welds with nickel alloy filler material, used in safety relevant parts of nuclear power plants, still challenge the ultrasonic inspection. The weld material forms large oriented grains which lead on the one hand to high sound scattering and on the other hand – to inhomogeneity and to the acoustic anisotropy of the weld structure. The ultrasonic wave fronts propagate not linearly, as in ferritic weld joints, but along the curves, which depend on the specific grain structure of the weld. Due the influence of these phenomena, it is difficult to analyze the inspection results and to classify the ultrasonic indications, which could be both from the weld geometry and from the material defects. A correct flaw sizing is not possible.
In an ongoing research project, different techniques to improve the reliability of ultrasonic testing at these kinds of welds are investigated. In a first step (in the previous research project) two ultrasonic inspection techniques were developed and validated on plane test specimens with artificial and realistic flaws. In the ongoing project, these techniques are applied to circumferential pipe welds with longitudinal and transverse flaws.
The technique developed at the Federal Institute for Materials Research and Testing (BAM) in Germany uses a combination of ray tracing and synthetic aperture focusing technique (SAFT). To investigate the unknown grain structure, the velocity distribution of weld-transmitting ultrasound waves is measured and used to model the weld by ray tracing.
The second technique, developed at the Fraunhofer Institute for Nondestructive Testing (IZFP) in Germany, uses Sampling Phased Array (Full Matrix Capture) combined with the reverse phase matching (RPM) and the gradient elastic constant descent algorithm (GECDM). This inspection method is able to estimate the elastic constants of the columnar grains in the weld and offers an improvement of the reliability of ultrasonic testing through the correction of the sound field distortion. The unknown inhomogeneity and anisotropy are investigated using a reference indication and the special optimization algorithm.
Both reconstruction techniques give quantitative inspection results and allow the defect sizing. They have been compared to conventional ultrasonic testing with techniques, which are state of the art for components in nuclear power plants. The improvement will be quantified by the comparison of the probability of detection (POD) of each technique.