Guided Wave Structural Health Monitoring Research Articles

Flexible Shear Mode Transducer for Structural Health Monitoring Using Ultrasonic Guided Waves

The application of the fundamental shear-horizontal wave mode for guided wave structural health monitoring is undoubtedly beneficial due to its nondispersive characteristics. Existing guided wave shear mode transducers are rigid and brittle, because of these characteristics, bonding them to irregular surfaces (i.e., curved surfaces) is challenging. There is a huge market interest in the development of a flexible shear mode transducer, which eases the transducer bonding process onto irregular surfaces and improves the surface contact between the transducer and the structure. This study presents a flexible shear mode transducer for structural health monitoring using low-frequency guided waves (20–120 kHz). The proposed transducer is manufactured using piezoceramic, and based on the results of this study, it exhibits the directional excitation of fundamental shear-horizontal mode at 20–120 kHz. Finite element analysis and laboratory experiments were conducted to study the behavior of the proposed transducer. Field trials were conducted on a liquid storage tank with an undulated surface (due to corrosion). The performance of the proposed transducer is also compared to the commercially available macro fiber composite transducers. The proposed transducer was driven by the industrialized ultrasonic guided wave inspection system; Teletest Focus+ in line with the application of tank floor inspection using ultrasonic guided waves.

Baseline-free guided wave damage detection with surrogate data and dictionary learning.

In guided wave structural health monitoring, damage detection is often accomplished by comparing measurements before damage (i.e., baseline data) and after damage (i.e., test data). Yet, in practical scenarios, baseline data is often unavailable. Data from surrogate structures (structures similar to the test structure) could replace baseline data, but due to small differences in material properties, such as thickness, temperature, and other effects, this data is often unreliable. In this paper, a dictionary learning framework overcomes this challenge and detects damage with surrogate information. The framework combines wave propagation characteristics of a test structure with geometric information from surrogate structures to create a synthetic damage-free baseline. The test data is compared with the synthetic baseline to detect damage. The framework is evaluated with four 108 mm ×108 mm plates: two 1.6-mm thick aluminum plates, one 1.6-mm thick steel plate, and one 6.25 mm thick aluminum plate. The framework is applied to each test structure after learning from plates with different material properties and thicknesses. With full wavefield data, this paper successfully isolates reflections from a mass without using explicit baseline data. Furthermore, with sparse guided wave data, this paper shows that a drop in a correlation coefficient can detect a mass without using explicit baseline data.

Dynamic Time Warping Temperature Compensation for Guided Wave Structural Health Monitoring.

Guided wave structural health monitoring is widely researched for remotely inspecting large structural areas. To detect, locate, and characterize damage, guided wave methods often compare data to a baseline signal. Yet, environmental variations create large differences between the baseline and the collected measurements. These variations hide damage signatures and cause false detection. Temperature compensation algorithms, such as baseline signal stretch and the scale transform have been used to optimally realign data to a baseline. While these methods are effective in some conditions, their performance deteriorates in the presence of large temperature variations, long propagation distances, and high frequencies. In this paper, we use dynamic time warping to better align guided wave data and to overcome these errors. When compared with stretch-based methods, we show that the dynamic time warping is more robust to the above-mentioned errors and more accurately detects damage with weak ultrasonic signatures.

An enhanced dynamic Gaussian mixture model–based damage monitoring method of aircraft structures under environmental and operational conditions

Gaussian mixture model–based structural health monitoring methods have been studied in recent years to improve the reliability of damage monitoring under environmental and operational conditions. However, most of these methods only use the ordinary expectation maximization algorithm to construct the Gaussian mixture model but the expectation maximization algorithm can easily lead to a local optimal solution and a singular solution, which also results in unreliable and unstable damage monitoring especially for complex structures. This article proposes an enhanced dynamic Gaussian mixture model–based damage monitoring method. First, an enhanced Gaussian mixture model constructing algorithm based on a Gaussian mixture model merge-split operation and a singularity inhibition mechanism is developed to keep the stability of the Gaussian mixture model and to obtain a unique optimal solution. Then, a probability similarity–based damage detection index is proposed to realize a normalized and general damage detection. The method combined with guided wave structural health monitoring technique is validated by the hole-edge cracks monitoring of an aluminum plate and a real aircraft wing spar. The results indicate that the method is efficient to improve the reliability and the stability of damage detection under fatigue load and varying structural boundary conditions. The method is simple and reliable regarding aviation application. It is a data-driven statistical method which is model-independent and less experience-dependent. It can be used by combining with different kinds of structural health monitoring techniques.

Plane Wave SH0 Piezoceramic Transduction Optimized Using Geometrical Parameters

Structural health monitoring is a prominent alternative to the scheduled maintenance of safety-critical components. The nondispersive nature as well as the through-thickness mode shape of the fundamental shear horizontal guided wave mode (SH) make it a particularly attractive candidate for ultrasonic guided wave structural health monitoring. However, plane wave excitation of SH at a high level of purity remains challenging because of the existence of the fundamental Lamb modes (A and S) below the cutoff frequency thickness product of high-order modes. This paper presents a piezoelectric transducer concept optimized for plane SH wave transduction based on the transducer geometry. The transducer parameter exploration was initially performed using a simple analytical model. A 3D multiphysics finite element model was then used to refine the transducer design. Finally, an experimental validation was conducted with a 3D laser Doppler vibrometer system. The analytical model, the finite element model, and the experimental measurement showed excellent agreement. The modal selectivity of SH within a 20 beam opening angle at the design frequency of 425 kHz in a 1.59 mm aluminum plate was 23 dB, and the angle of the 6 dB wavefront was 86.

Ensembles of novelty detection classifiers for structural health monitoring using guided waves

Guided wave structural health monitoring uses sparse sensor networks embedded in sophisticated structures for defect detection and characterization. The biggest challenge of those sensor networks is developing robust techniques for reliable damage detection under changing environmental and operating conditions (EOC). To address this challenge, we develop a novelty classifier for damage detection based on one class support vector machines. We identify appropriate features for damage detection and introduce a feature aggregation method which quadratically increases the number of available training observations. We adopt a two-level voting scheme by using an ensemble of classifiers and predictions. Each classifier is trained on a different segment of the guided wave signal, and each classifier makes an ensemble of predictions based on a single observation. Using this approach, the classifier can be trained using a small number of baseline signals. We study the performance using Monte-Carlo simulations of an analytical model and data from impact damage experiments on a glass fiber composite plate. We also demonstrate the classifier performance using two types of baseline signals: fixed and rolling baseline training set. The former requires prior knowledge of baseline signals from all EOC, while the latter does not and leverages the fact that EOC vary slowly over time and can be modeled as a Gaussian process.

Managing Complexity, Uncertainty, and Variability in Guided Wave Structural Health Monitoring

Guided waves are an attractive tool for monitoring large structures. They are able to interrogate sizable areas at once, are sensitive to structural damage, and carry information about the damage l...

Impact Damage Localisation with Piezoelectric Sensors under Operational and Environmental Conditions.

Guided-wave structural health monitoring (SHM) systems with piezoelectric sensors are investigated for localisation of barely visible impact damage in CFRP plates under vibration and different thermal conditions. A single baseline set is used in a delay-and-sum algorithm with temperature correction for damage localisation in a large temperature range. Damage localisation is also demonstrated under transient thermal conditions, with signals recorded while the temperature is rapidly decreased. Damage severity due to successive impact events is studied under constant temperature. Damage is also localised when the plate is subjected to random vibration.

Simultaneous excitation system for efficient guided wave structural health monitoring

Many structural health monitoring systems utilize guided wave transducer arrays for defect detection and localization. Signals are usually acquired using the “pitch-catch” method whereby each transducer is excited in turn and the response is received by the remaining transducers. When extensive signal averaging is performed, the data acquisition process can be quite time-consuming, especially for metallic components that require a low repetition rate to allow signals to die out. Such a long data acquisition time is particularly problematic if environmental and operational conditions are changing while data are being acquired. To reduce the total data acquisition time, proposed here is a methodology whereby multiple transmitters are simultaneously triggered, and each transmitter is driven with a unique excitation. The simultaneously transmitted waves are captured by one or more receivers, and their responses are processed by dispersion-compensated filtering to extract the response from each individual transmitter. The excitation sequences are constructed by concatenating a series of chirps whose start and stop frequencies are randomly selected from a specified range. The process is optimized using a Monte-Carlo approach to select sequences with impulse-like autocorrelations and relatively flat cross-correlations. The efficacy of the proposed methodology is evaluated by several metrics and is experimentally demonstrated with sparse array imaging of simulated damage.

Dynamic time warping: Compensating for temperature variations

Guided wave structural health monitoring has the potential to monitor large structural areas. Yet, temperature variations are known to misalign measurement data and cause false alarms. Current popular temperature compensation methods include the optimal signal stretch method and interpolation method. While these methods perform well for small temperature changes, they fail in large temperature variations. In this paper, dynamic time warping is used to realign signal responses that have been distorted by temperature. Baseline subtraction between the realigned signal and the baseline can then be used to detect and identify damage responses. We demonstrate dynamic time warping with guided wave measurements taken from an aluminum plate that is heated in cycles from roughly 75°F to 125°F. Dynamic time warping achieves a correlation between the chosen baseline and the pre-damage measurements greater than 90%. Applying no temperature compensation or more traditional methods, such as optimal signal stretch, demonstrate correlations of 0% and 30%, respectively. This shows that dynamic time warping removes temperature effects more accurately than other current approaches.

Progress on the Research of Ultrasonic Guided Wave Structural Health Monitoring in the Changing Ambient

Progress on the Research of Ultrasonic Guided Wave Structural Health Monitoring in the Changing Ambient

A real-time approach for damage identification using hyperchaotic probe and stochastic estimation

A real-time damage identification approach using an extended Kalman–Bucy filter is proposed for identification and real-time monitoring of damage-induced changes which is applicable both as a vibration-based technique and as a guided-wave technique for structural health monitoring. The proposed approach makes use of the intrinsic hypersensitivity of hyperchaotic systems to subtle changes in system parameters by applying the augmented state method along with an optimal filtering problem to provide simultaneous estimation of the states and parameters of a (possibly) nonlinear structural system driven by a tuned hyperchaotic excitation in order to monitor damage-induced changes. The proposed approach can also be employed for guided-wave structural health monitoring by discretizing the governing partial differential equation to obtain a process model in the context of optimal filtering problem, and thus to estimate the transmitted hyperchaotic wave and model parameters. Numerical simulation shows that the proposed approach is capable of real-time identification of reduction in elastic modulus of an isotropic beam.

Finite element simulation of guided waves in pipelines for long range monitoring against third party attacks

Guided waves (GW) are finding more applications for structural health monitoring (SHM) of pipelines and other long, slender structures, particularly in the areas of corrosion and crack detection. Third party impact, both accidental and intentional, is also a major cause of pipeline failure. The use of low frequency (below 10 kHz) GW to detect damage caused by a third-party is investigated. Field test data on a 1 km long pipeline are compared with finite element (FE) predictions to illustrate the potential of low frequency GW to travel long distances along a pipeline. An FE study indicates the type and frequency of GW that can propagate long distances (low attenuation) without significant change in shape (low dispersion). The FE analysis is conducted on a typical 10 in (255 mm) diameter steel pipe with 7.8 mm wall thickness. The effects of pipe diameter and thickness on the GW propagation characteristics are illustrated. It is shown that certain frequencies for certain pipe geometries produce a very dispersive signal and should be avoided for GW SHM and the reasons for this are discussed.

Omnidirectional piezo-optical ring sensor for enhanced guided wave structural health monitoring

This paper presents a novel method for the detection of ultrasonic waves from acoustic emission events using piezoelectric wafer ac3tive sensors (PWAS) and optical fiber Bragg grating (FBG) sensing combined with mechanical resonance amplification principles. The method is best suited for detecting the out-of-plane motion of the AE wave with preference for a certain frequency that can be adjusted by design. Several issues are discussed: (a) study the mode shapes of the sensors under different resonance frequencies in order to understand the behavior of the ring in a frequency band of interest; (b) comparison of analytical results and mode shapes with FEM predictions; (c) choice of the final piezo-optical ring sensor shape; (d) testing of the piezo-optical ring sensor prototype; (e) discussion of the ring-sensor test results in comparison with conventional results from PWAS and FBG sensors mounted directly on the test structure. The paper ends with summary, conclusions, and suggestions for further work.

An imaging method for impact localization using metal-core piezoelectric fiber rosettes

As a novel piezoelectric device, metal-core piezoelectric fiber has great potential to be a structurally integrated transducer for guided-wave structural health monitoring. Compared with common piezoelectric ceramics with in-plane isotropic characteristic, metal-core piezoelectric fiber exhibits unique directivity in Lamb wave sensing which can locate the direction of the impact without any information about time of flight or speed of propagation. This article introduces a metal-core piezoelectric fiber rosette that contains three metal-core piezoelectric fibers with an angle of 120° between adjacent metal-core piezoelectric fibers. To simplify the process of placing three metal-core piezoelectric fibers onto the structure and improve the performance of metal-core piezoelectric fiber rosettes, a method for packaging metal-core piezoelectric fiber rosette is presented. By coupling the piezoelectric effect to the wave strain field, the directivity of metal-core piezoelectric fiber is derived. In order to determine the direction of an incoming wave, previous work measures the amplitudes of metal-core piezoelectric fibers and calculates the wave direction by the rosette principles. However, the calculated wave direction is very sensitive to the measurement error, which comes from unavoidable sensor noise and complex dispersive effect in actual application. To improve the accuracy, a new method that combines an imaging method with the rosette approach is proposed. Its accuracy is validated by experiments, and the phenomena in experiments are also discussed.

Data-driven matched field processing for Lamb wave structural health monitoring

Matched field processing is a model-based framework for localizing targets in complex propagation environments. In underwater acoustics, it has been extensively studied for improving localization performance in multimodal and multipath media. For guided wave structural health monitoring problems, matched field processing has not been widely applied but is an attractive option for damage localization due to equally complex propagation environments. Although effective, matched field processing is often challenging to implement because it requires accurate models of the propagation environment, and the optimization methods used to generate these models are often unreliable and computationally expensive. To address these obstacles, this paper introduces data-driven matched field processing, a framework to build models of multimodal propagation environments directly from measured data, and then use these models for localization. This paper presents the data-driven framework, analyzes its behavior under unmodeled multipath interference, and demonstrates its localization performance by distinguishing two nearby scatterers from experimental measurements of an aluminum plate. Compared with delay-based models that are commonly used in structural health monitoring, the data-driven matched field processing framework is shown to successfully localize two nearby scatterers with significantly smaller localization errors and finer resolutions.

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 multiresolution approach to cointegration for enhanced SHM of structures under varying conditions – An exploratory study

Cointegration has now been established as a powerful means of projecting out long-term trends from time-series data in the context of econometrics. Recent work has further established that cointegration can be applied profitably in the context of structural health monitoring (SHM), where it is desirable to project out the effects of environmental and operational variations from data in order that they do not generate false positives in diagnostic tests. These variations typically impose a time-varying nature on SHM data on a much longer time scale than those at which the dynamics of the structures of interest usually manifest themselves. This situation means that cointegration can be used to remove long-term time variations and thus projects out the operational and environmental effects. The concept of cointegration is partly built on a clear understanding of the ideas of stationarity and nonstationarity for time-series. The current paper will discuss the distinction between different types of nonstationarity in the context of SHM data and will extend the discussion by the introduction of multiresolution (discrete wavelet) analysis as a means of characterising the time scales on which nonstationarity manifests itself. One of the major results of the current study is to demonstrate how a multiresolution approach to cointegration can lead to enhanced sensitivity for damage detection. The discussion will be based on synthetic data and also on experimental data for the guided-wave SHM of a composite plate.

Guided wave structural health monitoring using inductively coupled embedded sensors

Conventional SHM systems typically rely on permanently attached sensor networks glued on to a structure. These add complexity and weight through either a wiring requirement or the use of wireless sensing nodes. This paper reports on an alternative approach whereby the wire between transducer and ultrasonic equipment is replaced by an inductive coupling. This allows a passive small sensor unit to be embedded into a composite component. Here a model is presented to describe the performance and optimization of such a coupling. The resulting sensors are embedded in a CFRP component and shown to exhibit excellent performance. Signal processing to account for the effects of misalignment is described. Finally, the ability of such a system to detect typical impact damage is demonstrated.

Continuous baseline growth and monitoring for guided wave SHM

It has been shown by many authors that temperature effect compensation is a necessity for the realization of a reliable structural health monitoring (SHM) system. However, there exist practical issues related to the acquisition of baseline signals, required for temperature compensation methods to work effectively, such as degenerate baseline signals recorded at the same temperature and a long acquisition period before monitoring is started. An alternative approach for the acquisition of baseline signals is presented in this paper. The algorithm makes the acquisition an evolutionary process, integrated in the damage detection procedures. The algorithm is shown to be successful in generating a concise set of baselines that guarantees efficient performance of temperature compensation methods, leading to computation time and computer memory savings. Its capabilities in detecting simulated abrupt and slowly evolving damages have also been tested.