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Aurelio Dominguez-Gonzalez

Bio: Aurelio Dominguez-Gonzalez is an academic researcher from Autonomous University of Queretaro. The author has contributed to research in topics: Structural health monitoring & Induction motor. The author has an hindex of 15, co-authored 52 publications receiving 543 citations.


Papers
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Journal ArticleDOI
TL;DR: A new methodology and a nonlinear autoregressive exogenous model (NARX)-based recurrent neural network (NN) model is presented for accurate response prediction of large structures using a recent signal processing concept, empirical mode decomposition, mutual information index from the information theory, and a probabilistic Bayesian-based training algorithm.

124 citations

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TL;DR: In this paper, the main control techniques applied to suppress vibrations in civil structures using smart materials are reviewed, remarking on the advantages and disadvantages of smart actuators and control strategies tendencies in smart civil structures.
Abstract: Smart civil structures are capable of partially compensating the undesirable effects due to external perturbations; they sense and react to the environment in a predictable and desirable form through the integration of several elements, such as sensors, actuators, signal processors, and power sources, working with control strategies. This article will focus on reviewing the main control techniques applied to suppress vibrations in civil structures using smart materials, remarking on the advantages and disadvantages of smart actuators and control strategies tendencies in smart civil structures.

53 citations

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TL;DR: In this paper, the authors present a review of the time-frequency techniques for modal parameters identification of civil structures from acquired dynamic signals as well as the factors that affect the estimation accuracy.
Abstract: A major trust of modal parameters identification (MPI) research in recent years has been based on using artificial and natural vibrations sources because vibration measurements can reflect the true dynamic behavior of a structure while analytical prediction methods, such as finite element models, are less accurate due to the numerous structural idealizations and uncertainties involved in the simulations. This paper presents a state-of-the-art review of the time-frequency techniques for modal parameters identification of civil structures from acquired dynamic signals as well as the factors that affect the estimation accuracy. Further, the latest signal processing techniques proposed since 2012 are also reviewed. These algorithms are worth being researched for MPI of large real-life structures because they provide good time-frequency resolution and noise-immunity.

50 citations

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TL;DR: In this article, a fusion of wavelet-packet and empirical mode decomposition (EMD) method is presented for the automated and online identification-location of single or multiple-combined damage in a scaled model of a five-bay truss-type structure.
Abstract: Structural health monitoring (SHM) is a relevant topic for civil systems and involves the monitoring, data processing and interpretation to evaluate the condition of a structure, in order to detect damage. In real structures, two or more sites or types of damage can be present at the same time. It has been shown that one kind of damaged condition can interfere with the detection of another kind of damage, leading to an incorrect assessment about the structure condition. Identifying combined damage on structures still represents a challenge for condition monitoring, because the reliable identification of a combined damaged condition is a difficult task. Thus, this work presents a fusion of methodologies, where a single wavelet-packet and the empirical mode decomposition (EMD) method are combined with artificial neural networks (ANNs) for the automated and online identification-location of single or multiple-combined damage in a scaled model of a five-bay truss-type structure. Results showed that the proposed methodology is very efficient and reliable for identifying and locating the three kinds of damage, as well as their combinations. Therefore, this methodology could be applied to detection-location of damage in real truss-type structures, which would help to improve the characteristics and life span of real structures.

41 citations

Journal ArticleDOI
TL;DR: This article presents a state-of-the-art review of the sensing technologies used in structural health monitoring and some candidate sensor technologies with potential of use in this area are reviewed, where the main issues that affect their implementation in real-life schemes are also discussed.
Abstract: In the last years, the occurrence of natural hazards around the world has evinced the necessity of having structural health monitoring schemes that can allow the continuous assessment of the structural integrity of the civil structures or infrastructures, in order to avoid potential economic or human loses; further, it also allows the application of new sensing technologies and signal processing algorithms. An important step in a structural health monitoring strategy is the appropriate selection of the sensor used to measure the required physical variable. Although several reviews have been published, they focus on presenting and/or explaining the methodologies and signal processing techniques used in structural health monitoring. This article presents a state-of-the-art review of the sensing technologies used in structural health monitoring. Further, some candidate sensor technologies with potential of use in this area are also reviewed, where the main issues that affect their implementation in real-life schemes are also discussed.

39 citations


Cited by
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01 Jan 2014

872 citations

Journal ArticleDOI
TL;DR: The biggest challenge in realization of health monitoring of large real-life structures is automated detection of damage out of the huge amount of very noisy data collected from dozens of sensors on a daily, weekly, and monthly basis.
Abstract: Signal processing is the key component of any vibration-based structural health monitoring (SHM). The goal of signal processing is to extract subtle changes in the vibration signals in order to detect, locate and quantify the damage and its severity in the structure. This paper presents a state-of-the-art review of recent articles on signal processing techniques for vibration-based SHM. The focus is on civil structures including buildings and bridges. The paper also presents new signal processing techniques proposed in the past few years as potential candidates for future SHM research. The biggest challenge in realization of health monitoring of large real-life structures is automated detection of damage out of the huge amount of very noisy data collected from dozens of sensors on a daily, weekly, and monthly basis. The new methodologies for on-line SHM should handle noisy data effectively, and be accurate, scalable, portable, and efficient computationally.

349 citations

Journal ArticleDOI
TL;DR: A systematic review under both scientometric and qualitative analysis is presented to present the current state of AI adoption in the context of CEM and discuss its future research trends.

303 citations

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TL;DR: In this article, the authors present a state-of-the-art review of vibration monitoring methods and signal processing techniques for structural health monitoring in manufacturing operations, which can be used as a tool to acquire, visualize and analyse the sampled data collected in any machining operation which can then be used for decision making about maintenance strategies.
Abstract: Machines without vibrations in the working environment are something non-existent. During machining operations, these vibrations are directly linked to problems in systems having rotating or reciprocating parts, such as bearings, engines, gear boxes, shafts, turbines and motors. Vibration analysis has proved to be a measure for any cause of inaccuracy in manufacturing processes and components or any maintenance decisions related to the machine. The non-contact measurement of vibration signal is very important for reliable structural health monitoring for quality assurance, optimizing profitability of products and services, to enhance manufacturing productivity and to reduce regular periodic inspections. This paper presents a state-of-the-art review of recent vibration monitoring methods and signal processing techniques for structural health monitoring in manufacturing operations. These methods and techniques are used as a tool to acquire, visualize and analyse the sampled data collected in any machining operation which can then be used for decision making about maintenance strategies.

271 citations