1,721,049 research outputs found
Non-Destructive Techniques for the Condition and Structural Health Monitoring of Wind Turbines: A Literature Review of the Last 20 Years
Full text linkA 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
Instantaneous Spectral Entropy: An Application for the Online Monitoring of Multi-Storey Frame Structures
Full text linkDamage assessment techniques based on entropy measurements have been recently proposed for the structural health monitoring of civil structures and infrastructures. A quasi-real-time approach, based on the use of instantaneous spectral entropy (ISE) over an uninterrupted stream of data, is discussed here. The methodology is proposed for the detection of sudden damage-related structural changes (more specifically, linear stiffness reductions and nonlinear breathing cracks). The method operates by framing the continuous stream of vibration signals and comparing the single frames to a known baseline. The approach is also suitable for nonstationary signals originating from nonlinearly behaving structures. The procedure is validated on an experimental benchmark: a laboratory-scaled model of a three-storey single-span frame metallic structure. Three different definitions of entropy and six candidate time–frequency/time-scale transforms have been tested to find the optimal settings
A Monte Carlo Sampling Strategy for the Automated Operational Modal Analysis of Road Bridges
Full text linkAutomated Operational Modal Analysis (AOMA) is a highly convenient technique
to identify the modal properties of a target system, based only on its measured output and
without human supervision. In particular, AOMA is very useful for permanent and continuous
bridge monitoring, as it would otherwise be impractical to perform input-output dynamic
testing on such large and complex structures or to manually process the acquisitions on a
daily basis.
Nevertheless, its implementation requires a fairly articulated algorithm, made up of several
steps. Some of them have been well-optimised throughout the years thanks to contributions by
many researchers. Other aspects, however, are still open to improvements.
Specifically, the standard AOMA procedure operates on the so-called stabilisation diagram,
i.e. a complete set of identified dynamic properties for different model orders. Traditionally,
the model order n is increased from an initial (and arbitrary) minimum, nmin, up to a
similarly arbitrary maximum nmax, with a constant step and no omissions.
However, feeding the AOMA algorithm with all the models included in the [nmin,nmax]
range is here proved to not be the most efficient course of action. Instead, a Monte Carlo
Sampling strategy is proposed, randomly picking a set of models with order n ∈
[nmin, nmax]. This is verified on an experimental dataset, the Z24 bridge, to provide
comparable results in terms of accuracy and at a lower computational cos
Towards Video-Based System Identification and Finite Element Model Updating of Civil Structures and Infrastructures
Full text linkAn Application of Instantaneous Spectral Entropy for the Condition Monitoring of Wind Turbines
Full text linkFor economic and environmental reasons, the use of renewable energy sources is a key aspect of the ongoing transition to a sustainable industrialised society. Wind energy represents a major player among these natural, carbon-neutral sources. Nevertheless, wind turbines are often subject to mechanical faults, especially due to ageing. To alleviate Operation and Maintenance costs, Vibration-Based Inspection and Condition Monitoring have been proposed in recent times. This research proposes Instantaneous Spectral Entropy and Continuous Wavelet Transform for anomaly detection and fault diagnosis, departing from gearbox vibration time histories. The approach is validated on experimental data recorded from a turbine suffering bearing failure and total gearbox replacement. From a computational point of view, the proposed algorithm was found to be efficient and therefore even potentially applicable for real-time monitoring
Treed gaussian process for manufacturing imperfection identification of pultruded GFRP thin-walled profile
No full textThe process of manufacturing pultruded FRP (Fiber Reinforced Polymers) profiles involves unavoidable imperfections that affect their structural performances. This is is even more relevant for the stability of axially loaded slender elements, due to the importance of imperfections and notches to initiate the buckling phenomenon. Thus, they become a predominant factor for the design of lightweight FRP beam-like structures. A Bayesian approach is proposed to estimate the presence and location of manufacturing imperfections in pultruded GFRPs (Glass Fiber Reinforced Polymers) profiles. Specifically, the Treed Gaussian Process (TGP) procedure is applied. This approach combines regression Gaussian Processes (GP) and Bayesian-based Recursive Partitioning. The experimental and numerical modal shapes of wide flange pultruded profile were investigated. The experimental data were compared with the numerical results of several Finite Element Models (FEM) characterised by different crack sizes
Experimental modal analysis of structural systems by using the fast relaxed vector fitting method
No full textSystem identification (SI) techniques can be used to identify the dynamic parameters of mechanical systems and civil infrastructures. The aim is to rapidly and consistently model the object of interest, in a quantitative and principled manner. This is also useful in establishing the capacity of a structure to serve its purpose, thus as a tool for structural health monitoring (SHM). In this context, input–output SI techniques allow precise and robust identification regardless of the actual input. However, one of the most popular and widely used approaches, the Rational Fraction Polynomial (RFP) method, has several drawbacks. The fitting problem is nonlinear and generally non-convex, with many local minima; even if linearised via weighting, it can become severely ill-conditioned. Here, a novel proposal for the broadband macro-modelling of structures in the frequency domain with several output and/or input channels is presented. A variant of the vector fitting approach, the Fast Relaxed Vector Fitting (FRVF), applied so far in the literature only for the identification of electrical circuits, is translated and adapted to serve as a technique for structural SI and compared with other traditional techniques. A study about the robustness of the FRVF method with respect to noise is carried out on a numerical system. Finally, the method is applied to two experimental case studies: a scaled model of a high-aspect-ratio (HAR) wing and the well known benchmark problem of the three-storey frame of Los Alamos laboratories. Promising results were achieved in terms of accuracy and computational performance
Recent advances in embedded technologies and self-sensing concrete for structural health monitoring
Full text linkFully embedded and spatially diffuse sensors are central to the advancement of civil and construction engineering. Indeed, they serve as an enabling technology necessary for addressing the current challenges associated with through-life management and structural health monitoring of existing structures and infrastructures. The need to identify structural issues early on has driven the integration of such embedded sensing capabilities into construction materials, turning passive structures into proactive, self-aware “entities,” commonly referred to as Smart Structures. The economic rationale behind this endeavor is underscored by the vital significance of continuous monitoring, which enables prompt anomaly assessment and thus mitigates the risks of potential structural failures. This is particularly relevant for road and rail infrastructures, as they represent a substantial and enduring investment for any nation. Given that a large majority of these large infrastructures are composed of concrete and reinforced concrete, both academics and construction companies are continuously researching micro- and nano-engineered self-sensing solutions specifically tailored for this building material. This comprehensive review paper reports the latest advances in the field of self-sensing concrete as of 2024, with an emphasis on intrinsic self-sensing concrete, that is, electrically conductive functional fillers. A critical analysis and a discussion of the findings are provided. Based on the perceived existing gaps and demands from the industry, the field's future perspectives are also briefly outlined
Recursive partitioning and Gaussian Process Regression for the detection and localization of damages in pultruded Glass Fiber Reinforced Polymer material
Full text linkIn 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
A machine learning approach for automatic operational modal analysis
No full textOne of the major applications of Structural Dynamics in Civil, Mechanical, or Aerospace Engineering regards the dynamic characterisation of man-made structures and components. Yet, traditional Experimental Modal Analysis (EMA) needs dedicated setups which may not be always available where and when needed. For these and other reasons, output-only Operational Modal Analysis (OMA) is regarded as a more practical and convenient alternative. Many OMA algorithms have been reported in the scientific literature during the last twenty and more years. In this study, an Automatic OMA method is presented. The proposed algorithm is completely independent of the user experience, fully objective, and based on statistical principles and a Machine Learning (ML) clustering approach. The AOMA code is firstly applied to a numerical case study, to test all the parameters which control the process. An Airbus H135 helicopter blade is then analysed to verify the performance of the algorithm experimentally
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