1,721,067 research outputs found
Assessment of the ultimate actual strength of rock-climbing protection devices: Extraction tests in the field and the human capability to predict the ultimate strength
No full textBackground. Rock climbing protection devices are crucial for climbing practice safety and for mountaineering in general. The use of these devices, together with appropriate tech-niques, reduces injuries in the critical event of a climber’s fall. Although European stan-dards and rules support the manufacturer in the design, production and laboratory test-ing, a thorough investigation of their behaviour in a real environment and during an actual placement has not yet been performed. Methods. The aim of this work is to present an insight into the strength of such devices through the application of a monitored, quasi-static, increasing force in a field environ-ment. Results from several types of devices (pitons, nuts and cams) are presented and crit-ically evaluated with respect to the values of the loads acting on the anchors due to the fall of the climber. Results. As far as the piton actual strength is concerned, the present activities show that the characteristics requested by EN specifications and rules are functional for product qualification purposes, but of very little use when defining the load holding capabilities once the devices are in place. However, even if the actual strength does not match the requirement of the standard, the comparison with the actual load applied is fairly encour-aging. With regards to nuts and cams, it is worth underlining the importance of a correct placement: when placed correctly, the actual strength achieved by the device in the field complies and is higher than the classification of the EN standard. Moreover, an investigation of human capability to predict the ultimate strength of rock-climbing protection devices placed in the field has been carried out, with the aim of verifying the reliability of the climber’s judgement, and, possibly, improve the safety of the in-field decision-making process. Conclusions. The lesson learned from the experiments is that modern equipment shows one step better behaviour and, similarly to pitons, the device-rock coupling dictates the pairs actual strength, assuming of course a sound placement. To the author’s best knowl-edge, the present work represents the first attempt to investigate the human capabilities to assess the reliability of a protection placement in-field
Particle Filters and Auto-Encoders Combination for Damage Diagnosis on Hysteretic Non-Linear Structures Subject to Changing Environmental Conditions
No full textDamages may naturally arise in structures within their life span due to the insurgence of phenomena related to normal operation. Their occurrence might also be favored by external boundary conditions the systems experience during their lifetime, such as time-varying environmental and operating conditions. Standard maintenance activities, such as scheduled non-destructive testing (NDT) and corrective maintenance, are typically carried out to improve the health and longevity of such systems, typically entailing long downtimes with significant economic impacts. In recent decades, condition-based maintenance strategies (CBM) or even predictive ones (PM) have increasingly gained popularity since, in principle, they allow to optimally intervene on the structure only when really required by its current conditions. These maintenance schemes require that a deep knowledge of the system current state of health and, possibly, of the main degradation mechanisms be available, which may rely on advanced structural health monitoring (SHM) systems being installed on the structures for performing real-time diagnosis and prognosis. Many approaches to SHM have been formulated, with several applications to mechanical, aeronautical, space, and civil structures. Particle Filters (PFs) have been proposed as a model-based, time-domain tool for estimating hidden, not observable system states, including those normally affected by damage, in particular, when the structure behavior is non-linear and affected by non-Gaussian disturbances and noises. Yet, in case of varying operating and environmental conditions, the SHM task often still turns out to be quite challenging, since the diagnostic features associated with damage can be significantly distorted. To overcome this issue, auto-encoders have successfully been employed to extract damage-related features in presence of such varying external conditions. Thus, this work aims at combining these two methods for developing an original approach to damage detection and localization in structures, robust with respect to changing environmental and operating conditions, capable of leveraging the specific benefits provided by the two aforementioned methodologies. The proposed algorithm is demonstrated with reference to the problem of damage diagnosis on a vibrating n-degrees of freedom system, featuring a non-linear stiffness component characterized by a Bouc-Wen hysteretic behavior and subject to varying temperature conditions
Convolutional Neural Networks for Ultrasonic Guided Wave-Based Structural Damage Detection and Localisation
No full textAmong the many methods proposed in the literature to perform structural health monitoring (SHM) of thin-walled structures, two of them appear to be particularly promising and complementary. On the one hand, integrating Machine Learning techniques into this field seems a remarkable solution, since these methods have been shown to be effective in recognising usually hard-to-detect recurring patterns in the measured signals related to the presence of damages in structures, thus improving the diagnostic performances of SHM frameworks. In particular, in the past years, Deep Learning algorithms have gained much importance in this field due to their capability of processing high-dimensional inputs (such as images), thus making it possible to automatically identify onsetting structural damages. On the other hand, ultrasonic guided wave-based approaches are commonly adopted to assess the structural integrity of plate-like structures and pipelines. These approaches, coupled with tomographic algorithms, typically allow performing damage detection and localisation with satisfactory results. However, such reconstruction algorithms are significantly sensors layout-dependent and, as such, they come with some still unsolved issues, leading, for example, to artifacts creation and unsatisfactory tomographic damage localisation performances in case of unevenly distributed network of sensors or when few sensors are installed on the structure. In this work, convolutional neural networks (CNNs) and ultrasonic guided waves are combined into a unique framework, which leverages on the advantages of the two methods to perform damage detection and localisation in plate-like structures. Guided waves are excited and sensed by a network of sensors permanently installed on the structure. The information acquired is then converted into grayscale image as is, without performing any prior feature extraction procedure, which is further analysed by a set of CNNs. First, a classifier is employed to perform damage detection. In case damage is identified, the grayscale image is then analysed by two regression CNNs to localise the damage. The framework is tested using experimentally validated numerical simulations of guided waves propagating in a metallic plate available in the literature
Multiple local particle filter for high-dimensional system identification
Full text linkNonlinearity and high dimensionality emerge as two primary challenges in the realm of system identification within the context of structural health monitoring (SHM) applications. Particle filter (PF) has been demonstrated efficient for nonlinear identification, but it suffers from the curse of dimensionality and behaves poorly in high-dimensional problems. The idea of state and measurement partitioning has been used in many PF algorithms to simplify high-dimensional identification problems into the identification of several lower-dimensional subgroups, but with very few applications to SHM problems. In this context, by combining multiple particle filters (MPF) with the decay of correlations property, this paper develops a novel multiple local particle filter (MLPF) for high-dimensional identification problems. A whole state vector is partitioned into several state subgroups, each consisting of fewer state components and then estimated by one PF through a novel likelihood including the local state and measurement vectors. The feasibility and efficiency of the proposed method are tested through a benchmark toy example, a case study of a twenty-story Bouc-Wen frame structure under ground motion, and an experimental study of fatigue delamination growth in composites
Towards a deep learning-based unified approach for structural damage detection, localisation and quantification
Full text linkUltrasonic guided waves have been extensively employed for characterising structural damage thanks to their sensitivity to defects. Although they are easy to excite and acquire, heavy processing is often required to extract single-valued indicators of damage presence, or damage indices, from the acquired signals. Traditionally, damage indices have been elaborated through tomographic algorithms to generate damage probability maps, even though limitations affect the performance of such approach. Recently, the potentialities of machine learning have been leveraged to improve the accuracy of frameworks processing guided waves for damage diagnosis. However, most methods still require extracting damage indices from the acquired signals, which may bring to loss of diagnostic information and reduced accuracy. Furthermore, damage position and extent are usually roughly estimated through classification, while regression should be employed instead. In this context, this work aims (i) to test the capabilities of different supervised machine learning algorithms to localise and quantify damage through regression and (ii) to carry out a critical discussion about possible limitations of using damage indices instead of unprocessed signals. Results are compared to identify which algorithm performs better and if machine learning can improve the accuracy of damage diagnosis compared to traditional imaging methods. An experimentally validated numerical case study was used to test the capabilities of the proposed machine learning-based framework and to bring evidence of the accuracy of the algorithms involved to characterise damage with properties not seen during training
Particle filter-based hybrid damage prognosis considering bias
Full text linkHybrid prognosis combining both the physical knowledge and data-driven techniques has shown great potential for damage prognosis in structural health monitoring (SHM). Current practices consider the physics-based process and data-driven measurement equations to describe the damage evolution and the mapping between the damage state and the SHM signal (or the feature extracted from SHM signal), respectively. However, the bias between the measurements predicted by data-driven equation and the actual SHM measurements, arising from uncertainties like damage geometries and sensor placement or noise, can lead to inaccurate prognosis results. To account for this problem, this paper adopts a methodology typically applied for sensor fault diagnosis, and develops a new hybrid state space model with a bias parameter included into the state vector and the measurement equation. Particle filter (PF) serves as the estimation technique to identify the state and parameters relating to the damage as well as the bias parameter, and RUL can be predicted by the PF estimates and physics-based process equation. The numerical study about the fatigue crack growth shows the new model together with PF can provide satisfactory estimation and prediction results in case of bias in the measurement model
Vulnerability Analysis of Power Transmission Grids Subject to Cascading Failures
Full text linkCascading failures are a major threat to interconnected systems, such as electrical power transmission networks. Typically, approaches proposed for devising optimized control strategies are demonstrated with reference to a few test systems of reference (IEEE systems). However, this limits the robustness of the proposed strategies with respect to different power grid structures. Recently, this issue has been addressed by considering synthetic networks randomly generated for mimicking power transmission grids’ characteristics. These networks can be used for investigating the vulnerability of power networks to cascading failures. In this work, we propose to apply a recent algorithm for sampling random power grid topologies with realistic electrical parameters and further extend it to the random allocation of generation and load. Integration with a realistic cascade simulation tool, then, allows us to perform thorough statistical analyses of power grids with respect to their cascading failure behavior, thus offering a powerful tool for identifying the strengths and weaknesses of different grid classes. New metrics for ranking the control and mitigation effort requirements of individual cascade scenarios and/or of grid configurations are defined and computed. Finally, genetic algorithms are used to identify strategies to improve the robustness of existing power networks
Explainable framework for lamb wave-based damage diagnosis
No full textStructural health monitoring has been widely employed in several engineering fields as support tool for condition-based maintenance policies aimed at increasing structural safety levels. Among the many applications proposed in the literature, such an approach has been proved to allow accurately characterizing damage in thin-walled structures, which are widespread both in aeronautical and in mechanical applications. In this field, a consolidated solution is represented by tomographic algorithms used to process actively monitored ultrasonic guided waves to generate a probability map of possible damage affecting the structure. More recently, machine learning-based frameworks, specifically neural networks, have been employed as alternative tool to tomographic algorithms to perform damage detection, localization and/or quantification, successfully overcoming some intrinsic limitations of classic methods. However, the black box-like nature of neural networks has built mistrust in such tools, thus creating a gap between their employment in the academic world and in industrial applications. This work aims at reducing such a gap by presenting an explainable machine learning framework for ultrasonic guided wave-based damage diagnosis. Specifically, a convolutional neural network for classification is employed to detect possible damage affecting thin-walled structures. The capabilities of the framework are demonstrated by means of a realistic numerical case study involving crack-like damage affecting a metal plate. Moreover, the behavior of the convolutional neural network is explained through the layer-wise relevance propagation framework. This allows comparing the ultrasonic guided waves features learned by the machine learning algorithm to the intuition of human experts, with the purpose of building trust in the network and, possibly, underlying damage-related physical phenomena hidden to the human eye
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