An experimental study of the feasibility of phase‐based video magnification for damage detection and localisation in operational deflection shapes

Optical measurements from high‐speed, high‐definition video recordings can be used to define the full‐field dynamics of a structure. By comparing the dynamic responses resulting from both damaged and undamaged elements, structural health monitoring can be carried out, similarly as with mounted transducers. Unlike the physical sensors, which provide point‐wise measurements and a limited number of output channels, high‐quality video recording allows very spatially dense information. Moreover, video acquisition is a noncontact technique. This guarantees that any anomaly in the dynamic behaviour can be more easily correlated to damage and not to added mass or stiffness due to the installed sensors. However, in real‐life scenarios, the vibrations due to environmental input are often so small that they are indistinguishable from measurement noise if conventional image‐based techniques are applied. In order to improve the signal‐to‐noise ratio in low‐amplitude measurements, phase‐based motion magnification has been recently proposed. This study intends to show that model‐based structural health monitoring can be performed on modal data and time histories processed with phase‐based motion magnification, whereas unamplified vibrations would be too small for being successfully exploited. All the experiments were performed on a multidamaged box beam with different damage sizes and angles

Non-Destructive Techniques for the Condition and Structural Health Monitoring of Wind Turbines: A Literature Review of the Last 20 Years

A complete surveillance strategy for wind turbines requires both the condition monitoring (CM) of their mechanical components and the structural health monitoring (SHM) of their load-bearing structural elements (foundations, tower, and blades). Therefore, it spans both the civil and mechanical engineering fields. Several traditional and advanced non-destructive techniques (NDTs) have been proposed for both areas of application throughout the last years. These include visual inspection (VI), acoustic emissions (AEs), ultrasonic testing (UT), infrared thermography (IRT), radiographic testing (RT), electromagnetic testing (ET), oil monitoring, and many other methods. These NDTs can be performed by human personnel, robots, or unmanned aerial vehicles (UAVs); they can also be applied both for isolated wind turbines or systematically for whole onshore or offshore wind farms. These non-destructive approaches have been extensively reviewed here; more than 300 scientific articles, technical reports, and other documents are included in this review, encompassing all the main aspects of these survey strategies. Particular attention was dedicated to the latest developments in the last two decades (2000–2021). Highly influential research works, which received major attention from the scientific community, are highlighted and commented upon. Furthermore, for each strategy, a selection of relevant applications is reported by way of example, including newer and less developed strategies as well

A Computer Vision-Based Approach for Non-contact Modal Analysis and Finite Element Model Updating

Computer vision-based techniques for modal analysis and system identification are rapidly becoming of great interest for both academic research and engineering practice in structural engineering. For instance, this is particularly relevant in fields such as bridge or tall building monitoring, where the large size of the structure would require an expensive sensor network, and for the characterisation of very slender, highly-flexible structural components, where physically-attached sensors cannot be deployed without altering the mass and stiffness of the system under investigation. This study concerns the latter case. Here, an algorithm for the full-field, non-contact extraction and processing of useful information from vibrational data is applied. Firstly, video acquisition is used to capture rapidly very spatially- and temporally-dense information regarding the vibrational behaviour of a high-aspect-ratio (HAR) prototype wing, with high image quality and high frame rate. Video processing is then applied to extract displacement time histories from the collected data; in turn, these are used to perform Modal Analysis (MA) and Finite Element Model Updating (FEMU). Results are benchmarked against the ones obtained from a single-point laser Doppler vibrometer (LDV). The study is performed on the beam-like spar of the wing prototype with and without the sensors attached to appreciate the disruptive effects of sensor loading. Promising results were achieved

The extreme function theory for damage detection: An application to civil and aerospace structures

Any damaged condition is a rare occurrence for mechanical systems, as it is very unlikely to be observed. Thus, it represents an extreme deviation from the median of its probability distribu-tion. It is, therefore, necessary to apply proper statistical solutions, i.e., Rare Event Modelling (REM). The classic tool for this aim is the Extreme Value Theory (EVT), which deals with uni-or multivariate scalar values. The Extreme Function Theory (EFT), on the other hand, is defined by enlarging the fundamental EVT concepts to whole functions. When combined with Gaussian Process Regres-sion (GPR), the EFT is perfectly suited for mode shape-based outlier detection. In fact, it is possible to investigate the structure’s normal modes as a whole rather than focusing on their constituent data points, with quantifiable advantages. This provides a useful tool for Structural Health Monitoring, especially to reduce false alarms. This recently proposed methodology is here tested and validated both numerically and experimentally for different examples coming from Civil and Aerospace Engineering applications. One-dimensional beamlike elements with several boundary conditions are considered, as well as a two-dimensional plate-like spar and a frame structure

A novel approach to damage localisation based on bispectral analysis and neural network

The normalised version of bispectrum, the so-called bicoherence, has often proved a reliable method of damage detection on engineering applications. Indeed, higher-order spectral analysis (HOSA) has the advantage of being able to detect non-linearity in the structural dynamic response while being insensitive to ambient vibrations. Skewness in the response may be easily spotted and related to damage conditions, as the majority of common faults and cracks shows bilinear effects. The present study tries to extend the application of HOSA to damage localisation, resorting to a neural network based classification algorithm. In order to validate the approach, a non-linear finite element model of a 4-meters-long cantilever beam has been built. This model could be seen as a first generic concept of more complex structural systems, such as aircraft wings, wind turbine blades, etc. The main aim of the study is to train a Neural Network (NN) able to classify different damage locations, when fed with bispectra. These are computed using the dynamic response of the FE nonlinear model to random noise excitation

Design issues of a standard cell BiCMOS carrier transceiver on low voltage power lines

In this paper are described some of the issues of the mixed signal standard cell VLSI design with emphasis on the practical experience resulted from designing a carrier transceiver in SGS-THOMSON 2 μm BiCMOS technology. Presented are the circuit block structure, some advantages and disadvantages of the standard cell design approach, the testing strategy we implemented, and finally some practical conclusions resulting from our experience

Recursive partitioning and Gaussian Process Regression for the detection and localization of damages in pultruded Glass Fiber Reinforced Polymer material

In this paper, a methodology for the detection and localization of damages in composite pultruded members is proposed. This is particularly relevant to thin-walled pultruded members, which are typically characterized by orthotropic behavior, anisotropic along the fibers and isotropic in the cross section. Hence, a method to detect and localize damage, and the influence these might have on the performance of thin-walled Glass Fiber Reinforced Polymer (GFRP) members, is proposed and applied to both numerical and experimental data. Specifically, the numerical and experimental modal shapes of a narrow flange pultruded profile are analyzed. The reliability of the proposed semiparametric statistical method, which is based on Gaussian Processes Regression and Bayesian-based Recursive Partitioning, is analyzed on a narrow flange profile, artificially affected by sawed notches with incremental depth. The numerical investigation is carried out via finite element models (FEMs) of the cracked beam, where the dynamic parameters and the modal shapes are computed. In total, three different crack sizes are investigated, to compare the results with the experimental ones. Finally, the proposed approach is further extended and validated on numerically simulated frame structures