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The performance of the Acoustic Emission (AE) technique is significantly dependent on the sensors attached to the structural surface. Although conventional commercially AE sensors, like R15a and WSa sensors, have been extensively employed in monitoring many different structures, they are unavailable in restricted-assess areas. In contrast, thin PZT sensors are small, inexpensive and lightweight. These thin PZT sensors have a great potential for passive sensing to detect AE signals. However, their utility in AE monitoring is limited due to their low signal-to-noise ratio and information incompleteness because of their simple construction. This work discusses the issues and possible solutions with regards to the specific selection and application of thin PZT sensors for passive sensing. The compatibility of different thin PZT sensors and conventional bulky sensors is investigated using pencil break lead (PBL) tests. The comparison between the recorded signals is carried out in both the time domain and frequency domain for these sensors. To improve the reliability and performance of the thin PZT sensors, a methodology employing multiple thin PZT sensors of different sizes is proposed based on machine learning techniques and sensor data fusion.

A self-supervised classification algorithm is proposed for detecting and isolating sensor faults of health monitoring devices. This is achieved by automatically extracting information from failure investigations. This approach uses (i) failure reports for extracting comprehensive failure labels; (ii) recorded data of a faulty monitoring device and the information of the failure type for selecting fault-sensitive features. The features-label pairs are then used to train a classification algorithm, so that when a new set of measurements becomes available, the algorithm is capable of identifying with a high accuracy one of the possible failure types included in the training data set. The proposed approach is successfully applied to the failure investigations conducted on a low-cost wearable device, displaying similar challenges encountered in SHM.

The growing demand towards life cycle sustainability has created a tremendous interest in non-destructive evaluation (NDE) to minimize manufacturing defects and waste, and to improve maintenance and extend service life. Applications of Magnetic Sensors (MSs) in NDE of civil engineering structures have become of great interest in recent years due to their non-contact data collection, and their high sensitivity under the influence of external stimuli such as strain, temperature, and humidity, to detect damage and deficiencies. There have been several advancements in MSs over the years for strain evaluation, corrosion monitoring, etc. based on the magnetic property changes. However, these MSs are at their nascent stages of development, and thus, there are several challenges that exist. This paper summarizes the recent advancements in MSs and their applications in civil engineering. Principle functions of different MSs are discussed, and their comparative characteristics are presented. The research challenges are highlighted and the roadmap towards high technology readiness level is discussed.

The recording and processing of acoustic emissions can be used to identify and localise damage mechanisms occurring in engineering structures. In plate-like structures, acoustic emissions propagate through the structure as guided waves. With a measurement location away from the source location, dispersion effects in the guided wave distort the acoustic emission signal. The distortion of the original signal hampers identification of damage mechanisms. This research describes and assesses a method to reconstruct the original acoustic emission signal using dispersion compensation. Simulations and experiments are performed involving thick glass-fibre reinforced plastic laminates. The signal reconstruction on the simulated data gives a reasonable representation of the simulated signal at the location of interest. In the experimental case, similarity slightly degrades. Deviation in arrival time between original measurement and reconstruction is attributed to a possible discrepancy in material properties in reality versus the properties used in the reconstruction.

To move towards a condition-based maintenance practice for aircraft structures, design of reliable health management methodologies is required. Development of diagnostic methodologies is commonly realised on simplified sample structures with assumptions that methodologies can be adapted for application to realistic aircraft structures under in-service conditions. Yet such actual applications are not conducted. In this work, we study the development of diagnostic methodologies to training structures and their application to dissimilar testing structures. A heterogeneous population is considered, consisting of single-stiffener composite panels for methodology development and training and a multi-stiffener composite panel for application and testing. Characteristics as its composite material, lay-up, and temperature condition are constant while topologies and applied loads differ between the dissimilar structures. Damage in the structural panels is monitored on multiple diagnostic levels using a variety of structural health monitoring (SHM) techniques, including acoustic emission and distributed strain sensing. Specifically, we develop diagnostic methods for localising and monitoring disbond growth after impact using strain data collected during fatigue testing of multiple single-stiffener panels and apply these for disbond monitoring in an upscaled version of a multi-stiffener panel. In this manner, this study aids in the maturement and application of SHM methodologies to realistic aircraft structures.

In an earlier research, experimental evidence was given on the ability to use Piezo Wafer Active Sensors and acousto-ultrasonics to monitor the accumulation of fatigue damage in a thick composite structure. As a next step, numerical models are investigated as they aid in the further understanding of the governing phenomena and a quantification of the accumulated damage. However, they suffer from high computational demands, due to a high mesh density, the stochastic nature of crack initiation and the combination of initiation and propagation of cracks. The Polynomial Chaos Expansion (PCE) method is employed to efficiently make meta models and, with these models, account for the stochastic behaviour of crack initiation and formation of delaminations. The meta models thus allow predicting the overall effect of damage accumulations within certain bounds of uncertainty. This aids in the quantification of damage accumulation, hence allowing for a damage severity estimation based on the experimental results. The input for the PCE method is a 2D Finite Element (FE) model. Cracks and delaminations are generated using Random Variables (RV) describing the geometrical position and length and orientation. Moreover, the number of cracks and delaminations is randomized as well. The necessary remeshing is done automatically, allowing for a completely automated simulation for a large number of FE simulations to feed the PCE model. Several Quantities of Interests (QoI) are defined and tested against their sensitivity to the increasing amount of damage accumulation. A global sensitivity analysis is used to identify the importance of each of the Random Variables. Random variables with a low sensitivity can be eliminated from the analysis, improving the efficiency.

Fatigue assessment in offshore wind turbine support structures requires the monitoring of strains below the mudline, where the highest bending moments occur. However, direct measurement of these strains is generally impractical. This paper presents the validation of a virtual sensing technique based on the Gaussian process latent force model for dynamic strain monitoring. The dataset, taken from an operating near-shore turbine in the Westermeerwind Park in the Netherlands, provides a unique opportunity for validation of strain estimates at locations below the mudline using strain gauges embedded within the monopile foundation.

This study presents an approach for the detection of evolving degradation in large-scale low-speed roller bearings by clustering of Acoustic Emission (AE) events, and its application to experimental degradation data. To acquire the latter, a purpose-built linear bearing, representative of a segment of a turret bearing, has been instrumented with multiple piezoelectric AE transducers in the frequency range between 40–580 kHz. Clustering based on cross-correlation has identified a number of significant clusters that are linked to the observed damage. The results suggest that condition monitoring based on AE waveform similarity clustering is suitable for detection and identification of degradation in a large-scale roller bearing.

A case study is presented in which the first steps are made towards the development of a structural health monitoring (SHM) data fusion framework. For this purpose, a composite single-stiffener panel is subjected to compression-compression fatigue loading (R = 10). The carbon-epoxy panel contains an artificial disbond of 30 mm, which was created using a Teflon insert during manufacturing and placed between the skin and the stiffener foot. Under the applied fatigue load, the disbond is expected to grow and its propagation is monitored using two SHM techniques, namely acoustic emission (AE) and Rayleigh-scattering based distributed fiber optic strain sensing. Four AE sensors are placed on the skin, thereby allowing for disbond growth detection and localization. On each stiffener foot, fiber optic sensors are surface-bonded to monitor the growth of the disbond under the applied fatigue loading. The distributed strain measurements are used to localize and monitor the disbond growth. The strength of each technique is utilized by fusing the data from the AE sensors and the fiber optic sensors. In this manner, a data-driven approach is presented in which a data fusion of the different techniques allows for monitoring the damage in the stiffened panel on multiple SHM levels, including disbond growth detection and localization.

A radar equipment was used to measure the deflection response of bridge stay-cables induced by ambient and traffic excitation. After a concise description of the radar equipment and a summary of advantages and potential issues of the microwave technology, the paper focuses on the experimental tests performed on all stay-cables of the curved cable-stayed bridge erected in the commercial harbor of Porto Marghera, Venice, Italy. The bridge consists of an inclined concrete tower, single-plane cables and a composite deck; the curved deck has a centerline length of 231 m, with two different side spans and 9 cables supporting each side span. Three series of ambient vibration tests were performed (on July 2010, April 2011 and October 2019) on the two arrays of cables of the bridge by using conventional accelerometers and microwave interferometer. The availability of simultaneously collected radar and accelerometer data (which are usually regarded as reference data in dynamic tests) allowed to investigate the accuracy of the radar technique (in terms of natural frequencies and tensile force estimated from natural frequencies) and the errors/uncertainties in radar results. Furthermore, the tests allowed to verify the repeatability of radar survey, with SHM purposes.

Civil infrastructures such as bridges, buildings, and pipelines ensure society's economic and industrial prosperity. Specifically, pipe networks assure the transportation of primary commodities such as water, oil, and natural gas. The quantitative and early detection of defects in pipes is critical in order to avoid severe consequences. As a result of high-profile accidents and economic downturn, research and development in the area of pipeline inspection has focused mainly on gas and oil pipelines. Due to the low cost of water, the development of nondestructive inspection (NDI) and structural health monitoring (SHM) technologies for fresh water mains and sewers has received the least attention. Moreover, the technical challenges associated with the practical deployment of monitoring system demand synergistic interaction across several disciplines, which may limit the transition from laboratory to real structures. This paper presents an overview of the most used NDI/SHM technologies for freshwater pipes and sewers. The challenges that said infrastructures pose with respect to oil and natural gas pipeline networks will be discussed. Finally, the methodologies that can be translated into SHM approaches are highlighted.