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Special Issue "Sparsity-based Sensing in Nondestructive Testing and Structural Health Monitoring"

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: 30 June 2020.

Special Issue Editors

Dr. Luca De Marchi
E-Mail Website
Guest Editor
Department of Electrical, Electronic and Information Engineering, Alma Mater Studiorum Università di Bologna, Viale Risorgimento 2, 40136 Bologna, Italy
Interests: Acoustic and Ultrasonic Sensors; Structural Health Monitoring; Lamb Waves Inspections; Embedded Systems; Modal Analysis; Mixed Signal Processing
Special Issues and Collections in MDPI journals
Prof. Dr. Zhibo Yang
E-Mail Website
Guest Editor
Xi’an Jiaotong University, School of Mechanical Engineering, Xi’an, China, 710049
Interests: wave simulation; signal processing; wavelet analysis; sparse representation; structural health monitoring; blade tip timing; finite element method; modal analysis; damage detection; fault diagnosis; non-destructive evaluation
Prof. Dr. Kailiang Xu
E-Mail Website
Guest Editor
Fudan University, Department of Electronic Engineering, Shanghai, China
Interests: wave simulation; signal processing; inverse problem; and the development of multi-wave imaging techniques; Elasticity characterization for medical ultrasound and non-destructive evaluation
Dr. Joel B. Harley
E-Mail Website
Guest Editor
SmartDATA Lab, Dept. of Electrical and Computer Eng., University of Florida, USA
Interests: diagnostics; acoustics; time-series analysis

Special Issue Information

Dear Colleagues,

Insufficient sampling rates and/or missing data in spatial or time domains may hamper the possibility of achieving high wavenumber or frequency resolution, which is fundamental for reliable signal interpretation in structural health monitoring (SHM) and nondestructive testing and evaluation (NDT&E) applications.

To minimize the risk of misinterpretation, long acquisition procedures or dense sensor networks have to be used. However, in many of these applications, the collected signals usually have an extremely sparse representation in proper domains, which can be used to simplify the signal acquisition and interpretation. In fact, considering the sparsity of the important information of interest (e.g., the model parameters, defect localization, etc.), novel paradigms can overcome what is dictated by the conventional Nyquist sampling theory and significantly facilitate the sensing efficiency. From the signal processing point of view, sparsity-promoting strategies can be applied to obtain high-resolution signal representations, and to provide an efficient solution to the ill-posed problem encountered in many large-scale media monitoring due to the intrinsically limited nature of sensor networks’ cardinality.

This Special Issue will focus on sparse sensing, optimal sensor networks, and sparse signal processing for theoretical, analytical, and experimental investigations which may pave new paths to data acquisition and smart sensing in a broad range of SHM and NDT&E applications.

Potential topics include, but are not limited to:

  • Sparse and smart sensor networks in NDT/SHM;
  • Sparse methods for sensor network optimization;
  • Compressed sensing and sparse data representation;
  • Sparse projection of high-resolution transform, such as high-resolution Radon transform and dispersive Radon transform;
  • Inverse problem involving sparse methods;
  • Sparse sensing for non-destructive defect imaging;
  • High-resolution media characterization, such as high-resolution dispersion curves extraction;
  • Sparse data-driven strategies and deep-learning methods for NDT/SHM.

Dr. Luca De Marchi
Prof. Dr. Zhibo Yang
Prof. Dr. Kailiang Xu
Dr. Joel B. Harley
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (2 papers)

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Research

Open AccessArticle
Noise Reduction of Welding Crack AE Signal Based on EMD and Wavelet Packet
Sensors 2020, 20(3), 761; https://doi.org/10.3390/s20030761 - 30 Jan 2020
Abstract
The acoustic emission (AE) signal collected by a sensor in the welding process has an overlapping frequency band and weak characteristics under a complex noise background. It is difficult for the wavelet noise reduction method, with single basis function, to effectively match the [...] Read more.
The acoustic emission (AE) signal collected by a sensor in the welding process has an overlapping frequency band and weak characteristics under a complex noise background. It is difficult for the wavelet noise reduction method, with single basis function, to effectively match the different characteristic information of the welding crack AE signal. Taking into account the adaptive decomposition characteristics of Empirical Mode Decomposition (EMD), a novel wavelet packet noise reduction method for welding AE signal was proposed. The welding AE signal was adaptively decomposed into several Intrinsic Mode Functions (IMFs) by the EMD. The effective IMFs were selected by the frequency distribution characteristics of the welding crack AE signal. A wavelet packet, with a specific basis function, was subsequently performed on the effective IMFs, which were reconstructed to be the welding crack AE signal. The simulated and experimental results indicated that the proposed method can effectively achieve noise reduction of the welding crack AE signal, which provided a mean for structure crack detection in the welding process. Full article
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Open AccessArticle
A Personalized Diagnosis Method to Detect Faults in a Bearing Based on Acceleration Sensors and an FEM Simulation Driving Support Vector Machine
Sensors 2020, 20(2), 420; https://doi.org/10.3390/s20020420 - 11 Jan 2020
Abstract
Classification of faults in mechanical components using machine learning is a hot topic in the field of science and engineering. Generally, every real-world running mechanical system exhibits personalized vibration behaviors that can be measured with acceleration sensors. However, faulty samples of such systems [...] Read more.
Classification of faults in mechanical components using machine learning is a hot topic in the field of science and engineering. Generally, every real-world running mechanical system exhibits personalized vibration behaviors that can be measured with acceleration sensors. However, faulty samples of such systems are difficult to obtain. Therefore, machine learning methods, such as support vector machine (SVM), neural network (NNs), etc., fail to obtain agreeable fault detection results through smart sensors. A personalized diagnosis fault method is proposed to activate the smart sensor networks using finite element method (FEM) simulations. The method includes three steps. Firstly, the cosine similarity updated FEM models with faults are constructed to obtain simulation signals (fault samples). Secondly, every simulation signal is separated into sub-signals to solve the time-domain indexes to generate the faulty training samples. Finally, the measured signals of unknown samples (testing samples) are inserted into the trained SVM to classify faults. The personalized diagnosis method is applied to detect bearing faults of a public bearing dataset. The classification accuracy ratios of six types of faults are 90% and 92.5%, 87.5% and 87.5%, 85%, and 82.5%, respectively. It confirms that the present personalized diagnosis method is effectiveness to detect faults in the absence of fault samples. Full article
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