Wave Structural Health Monitoring System Research Articles

A model assisted probability of detection for guided wave SHM systems: comparative study on recent statistical developments

Despite the potential benefits, industrial implementation of guided wave monitoring systems remains limited today. One of the reason behind this is the lack of consolidated reliability assessment techniques. Recent works clearly illustrated that the classical statistical methodology adopted from nondestructive evaluation to estimate POD is not directly applicable to structural health monitoring (SHM): the hypothesis of independence does not hold for the data obtained from permanently attached sensors and growing defects. The experimental determination of POD for SHM systems requires extensive and costly studies, in addition to the numerous representative structures. Involving numerical studies can complement experimental trials, inform POD evaluation and reduce this cost drastically. We propose in this work a comparative study to demonstrate the application of Model Assisted Probability Of Detection approach (MAPOD) for elastic guided wave monitoring systems. We apply classical and recently developed statistical methods proposed in the literature, using simulated data obtained thanks to the CIVA SHM module. The MAPOD estimation is validated in comparison to experimental studies.

Defect detection and localisation using guided wave images from array data processed by nonlinear autoregressive exogenous model and Gamma statistical operator

Guided wave structural health monitoring (GWSHM) systems, using the delay-and-sum imaging algorithm, are an efficient solution to detect and localise defects in industrial structures. However, the image artifacts caused by either imperfect detection or sensor lay-out limitations make it difficult to identify and locate defects accurately. In order to enhance the performance of defect detection and localisation in GWSHM systems, this paper proposes a three-step procedure for post-processing guided wave signals prior to image formation. In the first step, the signals are processed using the nonlinear autoregressive exogenous model to suppress noise from benign features. The second step calculates the probability of defect presence based on the rescaled Gamma cumulative distribution function. This probabilistic threshold is then determined from the quantile mapping. Finally, a guide wave image is formed using the delay-and-sum imaging algorithm. The experimental validation was performed to inspect a 6 mm-diameter through-thickness circular hole on an aluminium plate and the defects were further scaled as simulated datasets to test its detectability under various amplitudes. In the second procedure step, the detection and localisation performance of the proposed procedure was compared with that of using the signal difference coefficient and the Rayleigh maximum likelihood estimator. It is shown that the proposed procedure can enhance the contrast between damaged and undamaged regions, providing more reliable and accurate guided wave images.

Unsupervised deep learning framework for temperature-compensated damage assessment using ultrasonic guided waves on edge device

Fueled by the rapid development of machine learning (ML) and greater access to cloud computing and graphics processing units, various deep learning based models have been proposed for improving performance of ultrasonic guided wave structural health monitoring (GW-SHM) systems, especially to counter complexity and heterogeneity in data due to varying environmental factors (e.g., temperature) and types of damages. Such models typically comprise of millions of trainable parameters, and therefore add to cost of deployment due to requirements of cloud connectivity and processing, thus limiting the scale of deployment of GW-SHM. In this work, we propose an alternative solution that leverages TinyML framework for development of light-weight ML models that could be directly deployed on embedded edge devices. The utility of our solution is illustrated by presenting an unsupervised learning framework for damage detection in honeycomb composite sandwich structure with disbond and delamination type of damages, validated using data generated by finite element simulations and experiments performed at various temperatures in the range 0–90 °C. We demonstrate a fully-integrated solution using a Xilinx Artix-7 FPGA for data acquisition and control, and edge-inference of damage. Despite the limited number of features, the lightweight model shows reasonably high accuracy, thereby enabling detection of small size defects with improved sensitivity on an edge device for online GW-SHM.

Experiments and modelling of ultrasonic waves in composite plates under varying temperature

Guided wave (GW) structural health monitoring (SHM) systems offer an attractive solution as an in-situ quasi real-time assessment of structural damage, but their sensitivity and efficiency may be impaired under varied environmental and operational conditions. Thus, virtual tests, such as that based on the Finite Element (FE) method, represent a valid approach for simulating and investigating SHM systems, enabling a substantial reduction in experimental campaigns. In this work, GW propagation characteristics in a carbon fibre-reinforced composite plate are studied under a varying temperature condition, representative of the aeronautics application. At first, GW SHM system was physically tested at room temperature (20°C), and the results were used to calibrate and assess the proposed FE modelling approaches, characterised by different element types and mesh sizes. A temperature independent averaged time compensation factor is proposed to mitigate the numerical data dependency on excitation frequency and propagation angle. Two temperature variations (from 20°C to -50°C, and 20°C to 65°C) were experimentally and numerically considered to investigate the effect of varying temperature on the GW. For all test cases, the compensated numerical data was compared to the experimental results, and discussed in terms of dispersion curves, focusing on the zero-order symmetric, S0, and antisymmetric, A0, modes. Results show that both 2D and 3D FE approaches can accurately predict the changes in GW due to varying temperature, with the group velocity of the A0 mode being less sensitive to temperature variations.

Assessment of Damage in Composite Pressure Vessels Using Guided Waves.

This paper deals with guided wave-based structural health monitoring of composite overwrapped pressure vessels adopted for space application. Indeed, they are well suited for this scope thanks to their improved performance compared with metallic tanks. However, they are characterized by a complex damage mechanics and suffer from impact induced damage, e.g., due to space debris. After reviewing the limited progress in this specific application, the paper thoroughly covers all the steps needed to design and verify guided wave structural health monitoring system, including methodology, digital modelling, reliability, and noise estimation for a correct decision-making process in a virtual environment. In particular, propagation characteristics of the fundamental anti-symmetric mode are derived experimentally on a real specimen to validate a variety of finite element models useful to investigate wave interaction with damage. Different signal processing techniques are demonstrated sensitive to defect and linearly dependent upon damage severity, showing promising reliability. Those features can be implemented in a probability-based diagnostic imaging in order to detect and localized impact induce damage. A multi-parameter approach is achieved by metrics fusion demonstrating increased capability in damage detection with promising implication in enhancing probability of detection.

Enhancing damage localisation in critical areas of storage tanks in floating production storage and offloadings with a guided wave structural health monitoring system

A guided wave monitoring system for storage tanks of floating production storage and offloading units was tested on a panel which is representative of a critical region of such structures. Initially, baseline signals of the undamaged structure were acquired within a representative temperature range. These signals were then processed with the optimal baseline subtraction, optimal stretch and phase-shift compensation algorithms. Two distinct damage indexes were generated from the residual signals in order to verify the detection capability of the system. A delay-and-sum image algorithm was then applied to the residual signals in order to evaluate the defect localisation capabilities of the system. These images were than post-processed with three different methods, one based on a probabilistic approach, another on singular value decomposition and also with a combination of these two methods. The signal-to-noise ratio (SNR) gain obtained by applying the post-processing strategies was calculated and compared to the original images. Results demonstrate that the system is capable of detecting localised wall loss and indicating its correct position for damage larger than 20% wall thickness loss. Results also show that the post-processing methods helped to improve the SNR, leading to gains of more than 15dB in damage to artefact amplitude ratios.

An up-scaling temperature compensation framework for guided wave–based structural health monitoring in large composite structures

Variations in environmental conditions can significantly impair the accuracy and reliability of guided wave structural health monitoring systems. Acquisition of baseline signals over a wide temperature range for the purposes of damage detection and localization is impractical for large composite structures. A novel framework for compensating the effect of temperature at a post-processing stage is presented in this paper to allow updating the compensation factors using observations obtained at different scales. The proposed methodology utilizes observations collected at the lower scales, where a large amount of data under controlled environment is available. Subsequently, the estimated compensation factors are propagated to the higher scales as priors within a Bayesian framework. This way, the measurements required from the high levels are reduced while making it possible to also update the estimated factors during the operation of the structure. The performance of the methodology is evaluated at different scales and compared with the direct use of compensation factors obtained from coupon studies only. It is demonstrated that the proposed methodology improves the fidelity of the compensation algorithm leading to a reduction in the uncertainty of the temperature-compensated signals. Based on the findings of the present study, the reduction in the uncertainty of the compensation improves the performance of both damage detection as well as damage localization in a large composite panel.

Long-term guided wave structural health monitoring in an uncontrolled environment through long short-term principal component analysis

Environmental effects are a significant challenge in guided wave structural health monitoring systems. These effects distort signals and increase the likelihood of false alarms. Many research papers have studied mitigation strategies for common variations in guided wave datasets reproducible in a lab, such as temperature and stress. There are fewer studies and strategies for detecting damage under more unpredictable outdoor conditions. This article proposes a long short-term principal component analysis reconstruction method to detect synthetic damage under highly variational environments, like precipitation, freeze, and other conditions. The method does not require any temperature or other compensation methods and is tested by approximately seven million guided wave measurements collected over 2 years. Results show that our method achieves an area under curve score of near 0.95 when detecting synthetic damage under highly variable environmental conditions.

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

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.

ICD: A methodology for real time onset detection of overlapped acoustic emission waves

Accurate time of arrival (TOA) detection is essential for successful wave and vibration-based structural health monitoring (SHM) system including Acoustic Emission (AE). AE is the foremost among passive wave-based monitoring systems. During high burst rate results, AE waves could overlap. Overlapping of AE waves leads to overlap in both time and frequency. As a result, the TOA information is lost in the coda of the previous wave and cannot be (reliably) detected by conventional techniques. To this end, ICD is introduced. ICD involves 3 cascaded systems a) (Overlapping) Identification b) (Overlap) Cleaning c) (Arrival) Detection. A novel 3D Fingerprint is meticulously designed to autogenously and injectively identify the overlapping waves. Positive identification activates the cleaning system which eradicates the influence of the Intersecting Wave using newly proposed adaptive spectral subtraction (ASpS). Then, TOA for the Intersected Wave was identified. The approaches were verified in controlled as well as source localization tests. The results from controlled study validate robustness in low IRR values for a wide range of waveform parameters. Finally, source localization results from laboratory testing confidently display the scientific applicability of the proposed system. This system could enhance detectability, reliability, and accuracy of a conventional system.

Assessment of the Length and Depth of Delamination-Type Defects Using Ultrasonic Guided Waves

Fiber-reinforced composite laminates are being increasingly used in various engineering components in the sectors of aerospace and green energy. Due to impacts throughout the service life of the structure, matrix breakage and delaminations significantly altering the structural integrity of the laminate can occur. Hence, robust guided wave structural health monitoring systems are required to ensure continuous safety of engineering structures. In this paper, the ultrasonic method based on the analysis of A0 mode reflecting within the defected area has been proposed to extract the length and the depth of the delamination-type defect. The technique proposed in this study extracts the depth of the damage by analyzing the magnitude variations of direct A0 mode which are caused by the difference of wave velocities in the upper and lower sub-laminates. This results in an altering and frequency-dependent forward-scattered amplitude of direct A0 mode. Furthermore, the proposed approach uses previously obtained information about the depth of the defect, which allows for the determination of the phase velocities of A0 and S0 modes in the upper and lower sub-laminates. As a result, the accuracy of the damage length estimation is increased. The performance of the proposed method was proven with 2D and 3D numerical simulations and experiments on samples with artificial defects. The method validation results showed that the proposed method with some limitations is capable of extracting the length of delamination with an approximate error below 6%.

Validation of a procedure for the evaluation of the performance of an installed structural health monitoring system

Validation of the performance of guided wave structural health monitoring systems is vital if they are to be widely deployed; testing the damage detection ability of a system by introducing different types of damage at varying locations is very costly and cannot be performed on a system in operation. Estimating the damage detection ability of a system solely by numerical simulations is not possible as complex environmental effects cannot be accounted for. In this study, a methodology was tested and verified that uses finite element simulations to superimpose defect signals onto measurements collected from a defect-free structure. These signals are acquired from the structure of interest under varying environmental and operational conditions for an initial monitoring period. Measurements collected in a previous blind trial of an L-shaped pipe section, onto which a number of corrosion-like defects were introduced, were utilised during this investigation. The growth of three of these defects was replicated using finite element analysis and the simulated reflections were superimposed onto signals collected on the defect-free test pipe. The signal changes and limits of reliable detection predicted from the synthetic defect reflections superimposed on the measurements from the undamaged complex structure agreed well with the changes due to real damage measured on the same structure. This methodology is of great value for any structural health monitoring system as it allows for the minimum detectable defect size to be estimated for specific geometries and damage locations in a quick and efficient manner without the need for multiple test structures while accounting for environmental variations.

Long-term stability of guided wave structural health monitoring using distributed adhesively bonded piezoelectric transducers

Industrial interest in structural health monitoring of safety-critical assets is increasing. Guided wave structural health monitoring shows potential for practical use as it allows detection of small defects (1.5% reflection changes) in the presence of environmental changes. Guided wave structural health monitoring systems use bonded piezoelectric sensors to monitor wave propagation patterns and subtraction between signals and baselines from the “healthy” structure for robust discrimination of damage. Long-term stability of structural health monitoring using these techniques has been investigated in this article as this is essential for industrial deployment of a system. Plates that were exposed to thermal cycling in an environmental chamber were monitored using bonded S0 mode sensors operating in pitch catch. Analysis of structural health monitoring data revealed drifts in baseline subtraction results corresponding to progressive reduction in defect sensitivity levels. These drifts were correlated with increases in coherent variability and suspected to be due to variations in the adhesive properties of the bonds between the sensors and the structure. Finite element analysis and further experimental results confirmed that changes in adhesive properties were a likely cause of the degradation in monitoring capability with time.

A Wavelet-Based Damage Detection Approach for Acousto-Ultrasonic <i>In Situ</i> Monitoring Systems

In this research, an advanced signal processing technique using wavelet analysis has been developed for a guided wave structural health monitoring system. The approach was applied for the detection of delamination in carbon fibre reinforced composites. A monolithic piezoceramic actuator was attached to a laminate plate for wave generation while laser vibrometry was used to facilitate the measurements of the wave response in a sensor network. This database of wave response was then processed using the continuous wavelet transform to obtain the positional frequency content. Transforms between damaged and undamaged states were compared to ascertain the presence of defects by evaluating the total energy of the time-frequency density function. Results show high damage detection indices depending on the location of the sensor and normalisation factor applied while there are positive indications that this methodology can be extended for damage characterisation.

Guided Wave Beamsteering using MFC Phased Arrays for Structural Health Monitoring: Analysis and Experiment

Phased array beamsteering is an effective tool for damage detection and assessment method in a guided wave structural health monitoring system. Monolithic piezoceramic (PZT) actuators have been widely utilized for beamsteering by assuming omnidirectional point sources for each actuator. However, this assumption can lead to erroneous results for a phased array of actuators with anisotropic actuation, such as macro-fiber composites (MFC). The MFC actuators are investigated in this research for beamsteering considering the main lobe width, main lobe magnitude, and side lobe levels and compared to equivalently sized PZT actuators. Analytical models of the MFC and PZT actuators attached to an isotropic plate structure are presented and the genetic algorithm is used to determine the optimal timing sequences of the phased array elements for beamforming enhancement. The analysis results show that the MFC phased arrays have reduced the main lobe width and side lobe levels compared to the PZT phased arrays for a range of beamsteering angles. The area ratios of the MFC arrays are also found to be greater than the PZT arrays for these beamsteering angles. Experiments using the PZT and MFC phased arrays on an aluminum plate are performed and compared to the analysis results. The experiment results agree with the analysis results and demonstrate improved beamsteering of the MFC phased arrays for specific applications.

Efficient temperature compensation strategies for guided wave structural health monitoring

The application of temperature compensation strategies is important when using a guided wave structural health monitoring system. It has been shown by different authors that the influence of changing environmental and operational conditions, especially temperature, limits performance. This paper quantitatively describes two different methods to compensate for the temperature effect, namely optimal baseline selection (OBS) and baseline signal stretch (BSS). The effect of temperature separation between baseline time-traces in OBS and the parameters used in the BSS method are investigated. A combined strategy that uses both OBS and BSS is considered. Theoretical results are compared, using data from two independent long-term experiments, which use predominantly A 0 mode and S 0 mode data respectively. These confirm that the performance of OBS and BSS quantitatively agrees with predictions and also demonstrate that the combination of OBS and BSS is a robust practical solution to temperature compensation.

Temperature effects in ultrasonic Lamb wave structural health monitoring systems

There is a need to better understand the effect of temperature changes on the response of ultrasonic guided-wave pitch-catch systems used for structural health monitoring. A model is proposed to account for all relevant temperature-dependent parameters of a pitch-catch system on an isotropic plate, including the actuator-plate and plate-sensor interactions through shear-lag behavior, the piezoelectric and dielectric permittivity properties of the transducers, and the Lamb wave dispersion properties of the substrate plate. The model is used to predict the S(0) and A(0) response spectra in aluminum plates for the temperature range of -40-+60 degrees C, which accounts for normal aircraft operations. The transducers examined are monolithic PZT-5A [PZT denotes Pb(Zr-Ti)O3] patches and flexible macrofiber composite type P1 patches. The study shows substantial changes in Lamb wave amplitude response caused solely by temperature excursions. It is also shown that, for the transducers considered, the response amplitude changes follow two opposite trends below and above ambient temperature (20 degrees C), respectively. These results can provide a basis for the compensation of temperature effects in guided-wave damage detection systems.

An Investigation Into the Temperature Stability of a Guided Wave Structural Health Monitoring System Using Permanently Attached Sensors

When testing complex structures using a deployable guided wave system, the measured signals are often far too complex to directly interpret without a priori knowledge of the geometry of the structure. However, in a structural health monitoring (SHM) scenario, the sensors are permanently attached to the structure and so changes in the system can be monitored by comparison to an earlier baseline measurement, taken when the structure was undamaged. This paper investigates such a system for the SHM of plate-like structures using guided waves. The basis of the system is a distributed array of permanently attached piezoelectric guided wave transducers where pairs of transducers are used in pitch-catch configuration allowing the detection and localization of damage in the plate. It has been observed that the use of a single baseline measurement suffers in the medium to long-term due to the variation of environmental conditions. A key parameter in the instability is the change in the temperature of the test structure. A method of using a database of baselines, termed optimal baseline subtraction (OBS), is described and applied to an experimental SHM system. The objective of this paper is to investigate the suitability and robustness of OBS under varying rates of temperature change and its use in the localization of damage. Results have shown that through the OBS an improvement of up to 20 dB in signal-to-coherent noise ratio may be achieved compared with single baseline subtraction

The temperature stability of guided wave structural health monitoring systems

It is desirable for any structural health monitoring (SHM) system to have maximumsensitivity with minimum sensor density. The structural health monitoring systemdescribed here is based on the excitation and reception of guided waves usingpiezoelectric elements as sensors. One of the main challenges faced is that in all butthe most simple structures the wave interactions become too complex for thetime domain signals to be interpreted directly. One approach to overcoming thiscomplexity is to subtract a baseline reference signal from the measured systemwhen it is known to be defect free. This strategy enables changes in the structureto be identified. Two key issues must be addressed to allow this paradigm tobecome a reality. First, the system must be sufficiently sensitive to small reflectionsfrom defects such as cracking. Second, it must be able to distinguish betweenbenign changes and those due to structural defects. In this paper the baselinesubtraction approach is used to detect defects in a simple rectangular plate. Thesystem is shown to work well in the short term, and good sensitivity to defects isdemonstrated. The performance degrades over the medium to long term. The principalreason for this degradation is shown to be the effect of change in temperature ofthe system. These effects are quantified and strategies for overcoming them arediscussed.