SHM System Research Articles

Lightweight and conformal acousto‐ultrasonic sensing network for multi‐scale structural health monitoring: A review

AbstractStructural health monitoring (SHM) has been increasingly investigated for decades. Different physical principles have been developed for damage identification, such as electronics, mechanics, magnetics, etc., with different coverage (i.e., global, large‐area, and local monitoring) and sensitivity. Mechanical acousto‐ultrasonic‐based methods have formed a big family in SHM technologies. Multiple wave/resonance modes have been utilized for versatile SHM tasks. The permanently integrated sensing networks play a significant role in achieving a cost‐effective and reliable SHM system, with major concerns including weight increase for large‐scale deployment and conformity for complex geometry structures. In this review, typical acousto‐ultrasonic sensors made of different material systems are discussed, along with advantages and limitations. Moreover, advanced network installation methods have been introduced, including surface‐mounting with pre‐integrated networks on substrates and in situ printing, and embedding with composite layup and metal additive manufacturing. Sensor versatility and usage in multi‐scale SHM techniques are then highlighted. Different wave/resonance modes are transmitted and received with corresponding elements and network designs. In conclusion, this systematic review mainly covers a collection of acousto‐ultrasonic sensors, two modalities of network installation, and their employment with various SHM methods, hopefully providing a useful guide to building lightweight and conformal networks with passive or active‐passive sensors, and developing complete and reliable SHM strategies by integrating different damage identification methods on multiple scales.

Multi-scale image compression and reconstruction algorithm for structural health monitoring system

Image data is increasingly common in structural health monitoring systems. However, due to the limitation of hardware resources such as network bandwidth, the data collected by sensors cannot be transmitted efficiently. In order to solve the problem of inefficient data transmission and ensure data recoverability, an end-to-end compression and reconstruction algorithm with an attention-enhanced residual module and a quantization mechanism is proposed. In order to meet the needs of SHM systems for image diversity, multi-scale images of local and global structural damage are used for research. The performance of the proposed algorithm is compared with traditional image compression standards and baseline models. Experimental results show that the peak signal-to-noise ratio of the reconstructed image is greater than 30 dB at different bit rates, effectively reconstructing the original image information; for images with complex textures, the residual block attention module can improve the rate-distortion performance of the proposed algorithm; when the bit rate is less than 0.44 bit per pixel, the peak signal-to-noise ratio and structural similarity of the reconstructed image of the proposed algorithm are better than those of the autoencoder baseline model and Joint Photographic Experts Group compression method. Structural damage detection examples verify the usability of compressed-reconstructed images in engineering applications.

Robustness of Structural Health Monitoring Systems for Offshore Energy Structures based on Damage Characteristics

Offshore energy structures such as fixed/floating wind turbines, tidal turbines and wave energy converters are exposed to loads coming from different sources. Recent advances in SHM offer many unique opportunities to assess the structural integrity of offshore energy infrastructure. The process of SHM generally involves the use of a system of sensors mounted on a structure; the processing of signals received from the sensors to attain damage sensitive features; and the detection of damage in the structure. Over the past decades, most of the research in SHM has been focused on improving and developing new sensor technology, signal processing, and damage detection algorithms. However, there are very few studies assessing the performance of SHM systems with respect to damage detection, localization, and quantification. Moreover, most of the performance analysis models for SHM systems rely only on 'probability of detection (POD)', that is an assessment index used in NDT to evaluate the probability of detecting a damage as a function of its size. For SHM systems, the replicability of POD is influenced by the environment, measurement noise, and the deterioration of sensors and instruments. Therefore, the distribution of POD is often determined by performing an extensive number of test sets with existing damage of varying magnitude. This paper aims to reduce the volume of experimental data required, as well as the uncertainties associated with POD prediction by using a model-assisted probability of detection (MAPOD) approach. We develop a Bayesian network (BN) method to evaluate the performance of SHM systems in terms of damage detection, localization, and assessment. In addition to POD, the probability of accurate localization (POAL) and probability of accurate assessment (POAA) are also incorporated into the analysis. The BN and limit state functions are applied to a fixed offshore energy structure equipped with a vibration based SHM system.

A Composite Fuselage under Mechanical Load: a case study for Guided Wave-based SHM

The introduction of composite materials in aeronautics has brought numerous advantages, along with unique damage and failure modes. The structure health is currently ensured by a damage-tolerant design and non-destructive inspections. Among other techniques, Guided Wave-based Structural Health Monitoring (GW-SHM) has gained interest as a cost and time effective alternative to traditional non-destructive techniques. One of the main challenges for GW-SHM is the influence of environmental and operational conditions on the damage identification capability. Aircraft structures undergo a broad range of mechanical load conditions, affecting the GW-SHM system. The study of load influence is meaningful in view of applications where the SHM system is expected to work under varying load conditions. Such applications may be in-flight monitoring, but also on-ground only usage and structural test monitoring. A full-scale CFRP door surrounding structure was instrumented with a GW-SHM system and tested under mechanical load. A hydraulic test rig was used to apply three representative load cases in quasi-static conditions on the structure. A network of robust piezocomposite transducers to monitor the structure has been designed and manufactured. The network is organized in arrays, which include the transducers, cabling and a connecting base plate for optimized sensor installation. A multiplexing module is directly connected to the base plate enabling a fast and reliable sensor connection, a drastic cable weight reduction, and a modular design. The combined influence of load and temperature on the propagation of Guided Waves has been assessed. Finally, a data-driven approach to damage identification has allowed the detection and localization of barely visible impact damage introduced during the test campaign.

Reliability Assessment of Damage Localization Techniques for Guided Wave-based Structural Health Monitoring Systems in Composite Stiffened Structures

In Guided Wave-based Structural Health Monitoring (GWSHM) systems, the reliability assessment is of utmost importance. The primary focus is the smallest damage that can be detected with 90 percent probability and 95 percent confidence. However, the probability of correct damage localization is equally crucial as the probability of detection. In this view, defining the smallest damage that can be localized accurately in a statistically meaningful way is quite challenging. Several localization techniques have been developed in the literature to localize damage resorting to a GW sensor network, like SAFT and RAPID. This article investigates the reliability of such SHM systems with these localization techniques using an experimental data set from the Open Guided Waves (OGW) platform and compares it with predictions obtained via a simulation model using the EFIT method. A carbon fibre-reinforced plate-like structure reinforced by an omega stringer and a GWSHM system employing twelve piezoceramic transducers bonded over the surface to excite and receive guided waves are used. Pseudo damage of thirteen different sizes are attached to three different locations using a damping tacky tape. Moreover, the EFIT modelling tool is used to simulate the experiment with an extensive number of damage scenarios at the defined three damage locations with a high degree of consistency with the experimental data, facilitating a more robust analysis. The Outcome concerning the influence and correlation between damage size on localization accuracy, considering various localization techniques, were explored and discussed. In addition, the reliability assessment allowed to establish the performance of the localization approaches in a rigorous way.

Quantifying the Reliability Performance of a Guided Wave-Based SHM System Using Piezoelectric Wafer Actuator Sensors

The adoption of structural health monitoring systems can bring important benefits to the aerospace industry. However, the current implementation of SHM technologies in today's aerospace industry remains limited. One of the primary obstacles hindering the adoption of SHM technologies is the challenge associated with assessing the reliability of SHM systems. In aerospace applications, SHM hardware is exposed to harsh environments, including high or low temperatures, vibrations, moisture, and radiation. Consequently, the long-term performance of SHM systems is adversely affected. This study delves into various issues related to the evaluation of SHM system reliability and performance quantification, placing special emphasis on guided wave-based SHM using Piezoelectric Wafer Actuator Sensors. We present experimental and numerical modeling examples designed to illustrate the detrimental effects of aging at the unitary sensor and sensor network levels. Conclusions are drawn regarding the current challenges in quantifying system reliability, emphasizing relevant performance metrics such as the probability of detection, localization, and false call rates.

Evaluation of a permanent SHM system performance in detecting damage scenarios considering runnability thresholds

Due to an increasing number of bridges approaching the end of their design lives, there is a growing interest by the infrastructure managers on monitoring systems able to provide information on the service capability of structures. Focusing, in particular, on railway bridges, serviceability is related to both structural serviceability and runnability, where the first is related to the structural capability of the bridge, while the second is related to the constraints on rail geometry. In this work the authors present an investigation of the capability of a permanent monitoring system to detect the presence of damages that would lead to an exceedance of the structural threshold values imposed by runnability norms. The target structure is a double-span Warren truss railway bridge, located in Italy, designed in 1946 and currently instrumented with a permanent monitoring system. The latter performs dynamic, quasi-static and static measurements, with sensors ranging from velocimeters to end displacement transducers and clinometers. Thanks to a FE model of the actual structure, a set of damage scenarios can be simulated, defining the damage entities that lead to the exceedance of thresholds imposed by norms in terms of runability assessment. As a result, the installed sensors mesh was found to distinguish such critical structural conditions as well as lower entity damage scenarios. The paper discusses the capability of different sensor configurations and monitoring strategies (“dynamic”, “static”, etc) to detect such damage scenarios, in terms both of “early warning” capability and of minimum time required to detect the anomaly. The same approach can be extended to the analysis with respect to structural serviceability limits.

Estimation of the effective range of PZT sensors for damage detection based on full wavefield measurements

Optimal location of sensors for enhanced damage detection capability is one of the key factors which needs to be considered when designing SHM system based on PZT sensors. However the sensitivity of a sensor to damage is not only dependent on its relative location with respect to elastic waves source and damage, but can also depend on structure geometry as well as particular signal features which are used for structure assessment. In the presentation, results of structure evaluation based on guided waves excited by PZT transducers and full wavefield measurements obtained by laser scanning vibrometer will be presented. The measurements were acquired for pristine state of test structure and with introduced damage, therefore it is possible to calculate spatial distribution of various signal characteristics and estimate effective range of PZT sensors for damage detection based on a given damage index. Various parameters affecting the damage detection range will be compared, in particular: type and the extent of damage, type of signal characteristic used, parameters of the excitation signal (e.g. frequency, duration), as well time interval used for damage index calculation. The obtained results may be useful for refining algorithms for sensor placement optimization as well as damage localization techniques, e.g. based on RAPID imaging algorithm.

Wave Propagation for Structural Health Monitoring (WaveProSHM): open software with GUI

Guided wave–based SHM methods have been explored extensively in recent decades. The wave propagation modelling including piezoelectric transducers and various damage types helps understand intricacies of guided wave behavior and for designing a robust SHM system. However, modelling aspects are still challenging. Moreover, commercially available software for the modelling of guided waves often lacks appropriate high-order elements and code optimization for fast GPU computations. This research aimed to develop and disseminate to the SHM community the open software based on highly efficient vectorised code of the time domain spectral element method which can be run on GPU. The developed code is written in MATLAB with Graphical User Interface (GUI) and communicates with GMSH – open source finite element mesh generator. It can model signals of propagating guided waves in structures which are excited and registered by an array of piezoelectric transducers. The capabilities of the software are shown in an example of a large-scale problem of interacting guided waves with delamination.

Consequences of thermal aging on the detection of impact damage in composite structures with PZT/FBG guided wave systems

In the context of recent reusable launch vehicles (RLV) developments, SHM could help fast and economic revalidation of RLVs between launches, provided that the transducers keep their functionality and reliability after repeating launches being exposed to harsh flight conditions. This paper studies the detection of barely visible impact damage in composite plates with guided waves (GW) based SHM systems. More precisely, it focuses on how this detection can be affected by the degradation of the sensors potentially induced by the exposition to flight conditions. The aging conditions in this work mainly consist of thermal cycling with high maximum temperature (150°C), expected for RLV structures and close to the operational limit of the thermoset-matrix composite. The response of the sensors to such environmental conditions depends on the sensor itself and on its bonding to the structure. Thus, in this paper two different types of sensors are tested (commonly used PZT transducers and FBG sensors) along with two different bonding methods (classic bonding with a secondary adhesive and cobonding on composite surface during the curing process). The sensors are setup in pairs (PZT-PZT or PZT-FBG) with GW signal paths going through the impact location. After a baseline acquisition, incremental impact tests (with increasing energy) are performed and GW signals are acquired after each impact to assess the damage signature on the waveform. Then after submitting the plate to thermal cycling, GW signals are recorded again to assess its effect on the damage detection performance and on the risk of false alarms (mistaking sensor degradation for structural damage). Sensor degradations are monitored through non-destructive techniques and impedance self-diagnostic measurements for PZT. Additionally to this aging test, the responses of degraded sensors previously studied by the authors are compared with the impact signature to assess how those degraded sensors could harm the impact detection.

Real-Time SHM of composite wing in a wind tunnel test using PCA-Based statistics

Structural health monitoring could prove particularly valuable in the case of composite aerostructures, where traditional inspection methods for critical components face challenges due to limited accessibility. An effective online SHM system can be achieved by employing fiber optic sensors, facilitating the integration of a 'fiber optic nervous system' into the composite structure, enabling the detection, measurement, and communication of a number of critical structural parameters. While the success of such system relies on selecting the right parameters to be monitored and the right sensing technology, it can also greatly benefit from on-board, real-time processing of the rather large amount of streamed data that ends with some data-driven real-time and continuous assessment as to the current state of the structure: Normal/Abnormal (based, of course, upon previously selected criteria). In this work we demonstrate fiber-optic-based structural health monitoring of a healthy/damaged composite airplane wing in a wind tunnel test. Real-time health assessment is based on Principal Component Analysis (PCA). We utilize two PCA-based measures, the Hotelling's T-squared distribution and the Q-statistics, as fault indicators. These statistics have been demonstrated to exhibit high sensitivity to structural anomalies. A PCA model accompanied by these statistics form a decision support tool that offers dependable real-time information regarding the health of the airborne structure, including the reporting of deviations from a prescribed flight envelope. We demonstrate the performance of the method on real-time structural data gathered in a wind tunnel test, performed on a composite wing. Here, a PCA model was built based on the wing in its healthy state under a nominal loading regime. During the test, the system, processing the data collected from 10 FBGs, using a standard PC, successfully identified deviations, such as overload and initial damage.

Seven Years SHM of Offshore Wind Turbine Foundations: Review, Experiences and Outlook

Wölfel Engineering is a leading expert on the design and provision of SHM systems for offshore wind turbines, as well as the assessment of their structural integrity based on the implementation of sophisticated data evaluation algorithms. This contribution aims to provide an insight into the SHM results of offshore wind turbine foundations in numerous wind farms over the last seven years, focusing on presenting an overview of the evolution of SHM concepts, monitoring goals and features monitored for structural assessment – alternatively referred to as key performance indicators (KPIs). Over the years, design approaches have become more accurate, alongside with the continuous increase of the size of the structures. Examples from offshore wind farms are used in order to show the effect of these evolutions to KPIs, such as the natural frequencies and the fatigue life consumption. The challenges met in industrial SHM applications for offshore foundations are discussed. A novel approach to SHM, the agile SHM concept, is Wölfel’s answer to the future requirements on monitoring accuracy and reliability. The proposed concept enables the efficient exchange of information between the results of the SHM system and the design process, so that the design model is at all times an accurate representation of the real structures.

The initial structural health monitoring system of the Nibelungen Bridge Worms

As an essential part of the validation project for the German Research Foundation-funded Priority Program SPP 100+, an initial structural health monitoring system has been implemented on the Nibelungen Bridge in Worms. The aim of this system is to monitor the general behavior of the bridge for further development of condition indicators and to provide real data to all project partners to validate their methods. In this scope, 20 sensors, including radiation, humidity, temperature, inclination, linear variable differential transformer (LVDT), and acceleration sensors, have already been installed on the bridge. The quality of the recorded data, including measurement technical traceability, completeness, and reliability, shall be guaranteed. Subsequently, both the raw data and the first statistically evaluated values, such as the maximum, average, and minimum values, are saved. These raw and first-hand data are made available to the involved partners in a possible real-time manner. In addition, a web-based visualization platform has also been established. Therefore, users can access the deviation of the measured values from different sensors in the most direct way. The measured results of this initiated SHM system are compared and validated with numerical simulation results. Further supplementation of additional sensors, extension, and improvement of this initial system are aimed to be achieved in the future.

Structural Health Monitoring for Dębica Railway Steel Arch Bridge in Poland

The study presents the vibration-based SHM system for the Dębica railway bridge located in Poland. The railway bridge owner was concerned about the excessive and self-excited vibrations of the hangers, the vibration measurement of 8 hangers per span in a total of two spans being monitored. The dynamic responses in both the transverse and longitudinal directions for each hanger under different load events over a nine-month period were recorded and introduced in this paper. The tension force and stress on each hanger are estimated through the natural frequency of the experimental vibration analysis. The proposed approaches could be used to develop a smart alarm system integrated into a vibration-based data-driven SHM system for heavy railway bridges.

An approach to define the minimum detectable damage and the alarm thresholds in vibration-based SHM systems

An approach to define the minimum detectable damage and the alarm thresholds in vibration-based SHM systems

Rail Structural Health Monitoring using ultrasonic guided waves: system design and deployment

Railways are a central industrial infrastructure enabling people and goods transportation all across the globe and their use is due to increase in the forthcoming years to reduce the overall carbon footprint of our modern societies. Hence the need to ensure their reliable use, as traffic interruptions or accidents can have dire social, economic and environmental impacts. In particular, rail breaks can produce such incidents and need to be monitored and prevented if possible. Ultrasonic guided waves are a promising physical phenomenon to monitor rails as they can enable the monitoring of long portions with a limited number of sensors. This talk will present a SHM system relying on such waves for rail monitoring which has been deployed for industrial tests on a subway line. The electronics, the sensors and their coupling to the rail were optimized to ensure the measurement of guided waves after a long-range propagation throughout the life of the system. Indeed, specific couplings are needed to enable long-time coupling, and so ultrasound sensitivity of the system, while the rails are bearing high loads or dilating due to temperature changes. An experimental campaign was carried out to evaluate the sensitivity of the system to rail breaks, and to demonstrate its detection under representative, uncontrolled environmental conditions. Particular considerations linked to pulse-echo inspections, inducing a sensitivity to cut-off frequencies and zero-group velocity modes, will be presented.

Physics-based reduced order modeling for uncertainty quantification of guided wave propagation using Bayesian optimization

Guided wave propagation (GWP) is commonly employed for the design of SHM systems. However, GWP is sensitive to variations in the material properties, often leading to false alarms. To address this issue, uncertainty quantification (UQ) is employed to improve the reliability of the predictions. Computational structural mechanics is a useful tool for the simulation of GWP. Even so, the application of UQ methods requires numerous solutions, while large-scale, transient GWP simulations increase the computational cost. In this paper, we propose a machine learning (ML)-based reduced order model (ROM), annotated as BO-ML-ROM, to decrease the computational time of the GWP simulation. The ROM is integrated with a Bayesian optimization (BO) framework to adaptively sample the data for the ROM training. The results showed that BO outperforms one-shot sampling methods, both in terms of accuracy and speed. The developed ROM, which is orders of magnitude faster than the original solver, is then applied for forward uncertainty quantification of the GWP in an aluminum plate and a composite panel under varying material properties. The UQ analysis reveals the higher impact of the uncertainties in the wave reflections. Furthermore, to compute the influence of the material properties on the predictions, a sensitivity analysis (SA) is performed using the variance-based Sobol’ indices and the Shapley additive explanations, where it is shown that the uncertainties in the elastic properties have a varying influence across time. The predicted results reveal the efficiency of BO-ML-ROM for the simulation of the GWP and demonstrate its utility for UQ and SA.

Industrial application of a low-cost structural health monitoring system in large-scale airframe tests

A small, novel, integrated SHM system has been deployed during full-scale testing of a wing fatigue test for several weeks and a fuselage pressurisation test for several months. Complementary NDE measurement techniques were combined, with inputs from visible and infrared optical sensors, as well as resistance strain gauges. Sensor units were deployed at regions of interest and integrated board computers permitted near real-time data processing. The outputs were full-field measurement datasets from digital image correlation and thermoelastic stress analysis systems. Changes in these datasets in the regions of interest were successfully quantified using orthogonal decomposition and were indicative of changes in the condition of the structure. The results from these case studies demonstrate that this system can be successfully deployed in spatially restricted areas within airframe structures to monitor crack growth. The low cost and small footprint of the system presents the opportunity for installation of arrays of similar sensors for both test and in-service data collection. Near real-time data processing would allow timely reporting to service engineers, informing maintenance or operational decisions.

A domain adaptation approach to damage classification with an application to bridge monitoring

Data-driven machine-learning algorithms generally suffer from a lack of labelled health-state data, mainly those referring to damage conditions. To address such an issue, population-based structural health monitoring seeks to enrich the original dataset by transferring knowledge from a population of monitored structures. Within this context, this paper presents a transfer learning approach, based on domain adaptation, to leverage information from completely-labelled bridge structure data to accurately predict new instances of an unknown target domain. Since intrinsic structural differences may cause distribution shifts, domain adaptation attempts to minimise the distance between the domains and to learn a mapping within a shared feature space. Specifically, the methodology involves the long-term acquisition of natural frequencies from several structural scenarios. Such damage-sensitive features are then aligned via domain adaptation so that a machine-learning algorithm can effectively utilise the labelled source domain data and generalise well to the unlabelled target-domain data. The described procedure is applied to two case studies, including the Z24 and the S101 benchmark bridges and their finite element models, respectively. The results demonstrate the successful exchange of health-state labels to identify the damage class within a population of bridges equipped with SHM systems, showing potential to reduce computational efforts and to deal with scarce or poor data sets in application to bridge network monitoring.

A Reliable Virtual Sensing Architecture with Zero Additional Deployment Costs for SHM Systems

A Reliable Virtual Sensing Architecture with Zero Additional Deployment Costs for SHM Systems