Search

Summary: Damage diagnosis in the structural field (mechanical, civil, aerospace, etc.) is a topic of active development and research. In recent years, considerable enhancements in this field have been achieved mainly due to advances in sensor technologies, the evolution of signal processing algorithms, and the increase of computational power. As one of the main consequences, the amount of data recorded from the sensorial equipment has steadily grown in quantity and complexity. In addition to that, these data are almost always significantly affected by many factors, which are not only related to the presence of damages but, for instance, also to the environmental and operative conditions under which the structural system is working. In order to handle these challenges, in the last few years, new deep learning models have been proposed, based on deep and heterogeneous architectures, able to deal with big data, also containing intricate diagnostic features that are difficult to be extracted. With this aim, this paper proposes a new vibration‐based structural diagnosis tool that exploits the power of convolutional neural networks (CNNs) to extract subtle damage‐related features from complex transmissibility function (TF) spectra even in presence of potentially confounding temperature variations. The diagnostic algorithm stems from the coupling of a CNN with an unsupervised anomaly detection algorithm based on autoencoders (AEs) to neutralize the effects of temperature variations and increase the damage diagnosis accuracy. The proposed approach is demonstrated with reference to a simple, but realistic, numerical case study of a structural beam subjected to temperature changes. [ABSTRACT FROM AUTHOR]

Structural Health Monitoring (SHM) has been researched for a long time and continues to be an active area of research. Initial work on SHM involved identification of hand-crafted features and predictive models relied on statistical methods. The recent improvements in computing capabilities, coupled with better integration of sensor data, has led to the emergence of more effective techniques in terms of scalability and predictive power. Machine learning offers a solution through automatic feature extraction algorithms, and scalable and noise robust models. Convolutional Neural Networks (CNN) have been used as state-of-art classifiers for images as well as for text. This paper proposes the use of the monitored structure's transmissibility functions for the structure under observation, which can be fed into a novel composite architecture consisting of Deep CNN followed by multivariate linear regressors to detect, localize, and quantify the damage extent in a system. The proposed method was tested on the Los Alamos' Eight degree-of-freedom (DOF) structure, and the Structural Beam Data from Laboratory of Mechanical Vibrations and Rotor Dynamics, University of Chile. This study on damage localization and quantification can be leveraged to comment on the safety and soundness of the structure under inspection and can help in making more informed inferences. It is expected that, in general, this will lead to extended structure life, which not only improves the resource utilization in terms of structure maintenance and its longevity but also decreases the carbon footprint and capital expenditure. [ABSTRACT FROM AUTHOR]

Abstract The dynamic behavior of structures can be studied by the identification of their modal parameters. Classical modal analysis methods are based on the relation between the forces applied to structures (inputs) and their vibration responses (outputs). In real operational conditions it is difficult, or even impossible, to measure the excitation. For this reason, operational modal analysis approaches which consider only output data are proposed. However, most of these output-only techniques are proposed under the assumption of white noise excitation. If additional components, like harmonics for instance, are present in the exciting force, they will not be separated from the natural frequencies. Consequently, this assumption is no longer valid. In this context, an operational modal identification technique is proposed in order to only identify real poles and eliminate spurious ones. It is a method based on transmissibility functions. The objective of the proposed paper is to identify modal parameters in operational conditions in the presence of harmonic excitations. Identification is performed using a method based on transmissibility measurements and then with the classical stochastic subspace identification method, which is based on white noise excitation. These two methods are first applied to numerical examples and then to a laboratory test. Results validate the novel ability of the method based on transmissibility measurements to eliminate harmonics, contrary to the stochastic subspace identification approach. [ABSTRACT FROM AUTHOR]

Nowadays, structural health monitoring is the area of a great interest of continuing research aiming at establishing a reliable condition monitoring strategy of civil engineering infrastructure. Such finding will allow moving from the schedule-based inspection policy to condition-based policy. This study is dedicated to identify the damage in reinforced concrete beams by using only frequency domain vibration measurements. In the meantime, statistical pattern recognition model was tested through the course of this research. A transmissibility-based damage detection and classification system was proposed. Subsequently, the measure distance of the spectra envelope (COSH) was suggested as classification tool. The proposed method was examined on datasets from numerical beam model and experimental measurements from 2.0m reinforced concrete beam. For the two models, the results of the proposed approach proved effective and managed to detect various levels of defects, and classify the defects according to their size. Having processed response signals to detect and classify state conditions, the devised approach is relevant to use in embedded online structural health monitoring.

Copyright of Journal of Engineering & Sustainable Development is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The modal characterization of automotive lighting systems becomes difficult using sensors due to the light weight of the elements which compose the component as well as the intricate access to allocate them. In experimental modal analysis, high speed 3D digital image correlation (HS 3D-DIC) is attracting the attention since it provides full-field contactless measurements of 3D displacements as main advantage over other techniques. Different methodologies have been published that perform modal identification, i.e., natural frequencies, damping ratios, and mode shapes using the full-field information. In this work, experimental modal analysis has been performed in a multi-component automotive lighting system using HS 3D-DIC. Base motion excitation was applied to simulate operating conditions. A recently validated methodology has been employed for modal identification using transmissibility functions, i.e., the transfer functions from base motion tests. Results make it possible to identify local and global behavior of the different elements of injected polymeric and metallic materials. [ABSTRACT FROM AUTHOR]

• Transmissibility functions under base excitation were obtained using DIC. • Transmissibility functions were adapted for FRF-based identification procedures. • The conversion of the data improved the modal identification and curve synthesis. • Dense maps of synthesis correlation and error highlight inaccuracies using the original data. In certain situations, employing a movable base acting as the excitation of a mechanical system is the best or even the only way to determine the response model for modal analysis. However, the obtained transmissibility functions must be modified prior to modal identification with a conventional procedure based on frequency response functions. Moreover, when employing vision techniques, the response curves are noisier and even poorly defined as the sensitivity is significantly lower than traditional sensors. Using the right model for curve-fitting is particularly relevant in this case. The current study performs an analysis of the adaptation of transmissibility functions, obtained by a vision technique, to improve the accuracy of the modal data estimation with conventional procedures. Two sets of transmissibility functions were evaluated: the originally obtained in the experiment, and the adapted one. After modal identification, significant differences were found concerning mode shapes and curve synthesis. The adaptation improved the accuracy of the identification in all the measurement points, proved by statistical indicators of the curve-fitting procedure like the correlation coefficient and the error between the synthesised and the experimental curves. [ABSTRACT FROM AUTHOR]

Transmissibility-based operational modal analysis (TOMA) is a recent research area that has seen its most significant developments over the last two decades. The advantage of this new approach is its independence from the excitation spectrum. All methods developed have been based on Response Transmissibility (RT) functions or Power Spectrum Density Transmissibility (PSDT) functions. The RT-based methods identify modal parameters from various load conditions and the PSDT-based methods use a single load condition by combining multiple reference outputs. This work proposes a unified concept for TOMA that relates the scalar RT and PSDT functions. This new concept shows that scalar PSDT functions combine n RT functions due to a single load. Two applications emerge from the unified concept: a) the matrix representation permits to identify modal parameters (natural frequencies, damping ratios and mode shapes) from PSDT functions in only one load condition, b) a procedure based on the joint diagonalization of matrices with PSDT functions from different load conditions allows the identification of RT functions. A numerical simulation and actual field data were used to assess the applications of unified concept. The results demonstrate the capacity of new concept to estimate modal parameters and RT functions of structures in operations conditions. [ABSTRACT FROM AUTHOR]

Abstract: Periodical loads, such as waves and rotating machinery, form a problem for operational modal analysis (OMA). In OMA only the vibrations of a structure of interest are measured and little to nothing is known about the loads causing these vibrations. Therefore, it is often assumed that all dynamics in the measured data are linked to the system of interest. Periodical loads defy this assumption as their periodical behavior is often visible within the measured vibrations. As a consequence most OMA techniques falsely associate the dynamics of the periodical load with the system of interest. Without additional information about the load, one is not able to correctly differentiate between structural dynamics and the dynamics of the load. In several applications, e.g. turbines and helicopters, it was observed that because of periodical loads one was unable to correctly identify one or multiple modes. Transmissibility based OMA (TOMA) is a completely different approach to OMA. By using transmissibility functions to estimate the structural dynamics of the system of interest, all influence of the load-spectrum can be eliminated. TOMA therefore allows to identify the modal parameters without being influenced by the presence of periodical loads, such as harmonics. One of the difficulties of TOMA is that the analyst is required to find two independent datasets, each associated with a different loading condition of the system of interest. This poses a dilemma for TOMA; how can an analyst identify two different loading conditions when little is known about the loads on the system? This paper tackles that problem by assuming that the loading conditions vary continuously over time, e.g. the changing wind directions. From this assumption TOMA is developed into a time-varying framework. This development allows TOMA to not only cope with the continuously changing loading conditions. The time-varying framework also enables the identification of the modal parameters from a single dataset. Moreover, the time-varying TOMA approach can be implemented in such a way that the analyst no longer has to identify different loading conditions. For these combined reasons the time-varying TOMA is less dependent on the user and requires less testing time than the earlier TOMA-technique. [Copyright &y& Elsevier]

Abstract: Operational modal parameter estimation (OMA) techniques perform system identification without or with only limited knowledge of the operational inputs acting on the system. However, most of the current operational identification techniques impose multiple conditions on the spectral content of the unknown inputs. As a consequence, modeling errors occur if these assumptions are not met. Therefore, there is a general interest in operational identification techniques that can operate independent of the unknown input spectra. This paper introduces poly-reference Transmissibility based Operational Modal Analysis (pTOMA). pTOMA uses parametrically estimated transmissibility functions associated with different loading conditions to obtain the system eigenvalues and eigenvectors using output-only data. Unlike most OMA techniques no strong assumptions are necessary considering the input spectrum. The method is therefore able to correctly identify the system parameters while the excitation may contain (varying) harmonics or strong coloration. A framework to use pTOMA is formulated, the algorithm is introduced and the claimed properties are illustrated by means of a numerical experiment. [Copyright &y& Elsevier]

Abstract: Operational modal analysis (OMA) is the collective term for different techniques that estimate the modal parameters of a linear structure using only the structural responses to unknown excitations. Therefore, OMA is the preferred approach when operational forces are hard to measure, when operational conditions are hard to replicate in a controlled environment or when an experimental modal analysis (EMA) is too time-consuming. However, OMA does not allow us to determine the relative contribution of each mode, i.e. the mode shapes found with OMA are unscaled. This paper introduces a novel approach to estimate the relative contributions of all modes within a given bandwidth and to reconstruct frequency response functions (FRF) that are proportional to the true FRFs of the system. This novel technique requires only response data and a general knowledge of the input locations of the dominant forces acting on the system. It is shown that even when only a limited number of input locations are known the proposed method can still be used by considering multiple loading conditions. Finally, since the technique is based on transmissibility functions, there are no necessary assumptions considering the spectral content of the excitations. Several numerical examples illustrate the claimed properties and are used to quantify the influence of measurement and ambient noise. [Copyright &y& Elsevier]

Abstract: This paper deals with the use of transmissibility functions for damage localization. The first part is dedicated to a critical review of the state-of-the-art highlighting the major difficulties when using transmissibility functions for damage detection and localization. In the second part, an analytical study is presented for non dispersive systems such as chain-like mass-spring systems. The link between the transmissibility function and the mechanical properties of four subsystems defined by the boundary conditions, the position of the excitation and the two measurement locations used for the computation of the transmissibility functions is derived. This result is used to discuss the situations in which damage localization is likely to work. The last section discusses the extension of these results to more general dispersive systems such as beams or plates. [Copyright &y& Elsevier]

• Reduced order models (ROMs) are employed to perform vibration-based crack detection. • Mesh morphing is used to parametrize the ROMs with respect to the crack geometry. • Clustering is employed to handle parametric dependence of the ROMs more efficiently. • A transmissibility based damage index is used, requiring output-only measurements. • Time series models are used to compute transmissibilities from noisy measurements. In this work parametric reduced order models (pROMs) for cracked shells are developed and applied to crack detection problems. Mesh morphing is employed to allow for parameterization of the models with respect to the crack location and size, while a clustering approach is adopted to partition the parameter space in sub-domains, for which efficient local reduced order models (ROMs) are constructed. Subsequently, the proposed ROMs are employed as forward simulators within an inverse problem setting, aiming to identify the location of the fault (crack). An output-only scheme is adopted, in the absence of information regarding the loading time history, based on transmissibility functions. The resulting scheme is tested through numerical examples involving realistic geometries, consisting of curved shells or multiple components. [ABSTRACT FROM AUTHOR]

One of the current challenges in structural health monitoring (SHM) is to take the most advantage of large amounts of data to deliver accurate damage measurements and predictions. Deep Learning methods tackle these problems by finding complex relations hidden in the data available. Amongst these, Capsule Neural Networks (CapsNets) have recently been developed, achieving promising results in benchmark Deep Learning problems. In this paper, Capsule Networks are expanded to locate and to quantify structural damage. The proposed approach is evaluated in two case studies: a system with springs and masses that simulate a structure, and a beam with different damage scenarios. For both case studies, training and validation sets are created using Finite Element (FE) models and calibrated with experimental data, which is also used for testing. The main contributions of this study are: A novel CapsNets-based method for dual classification–regression task in SHM, analysis of both routing algorithms (dynamic routing and Expectation–Maximization routing) in the context of SHM, and analysis of generalization between FE models and real-life experiments. The results show that the proposed Capsule Networks with dynamic routing achieve better results than Convolutional Neural Networks (CNN), especially when it comes to false positive values. • Novel Capsule Neural Networks for structural damage assessment • Capsule Neural Networks for dual purpose goals: Damage localization and quantification. • Exploration of dynamic routing and EM routing for structural damage assessment. [ABSTRACT FROM AUTHOR]