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Advances in Signal Processing and Sensing Technology for Improved Structural Health Monitoring

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

Deadline for manuscript submissions: closed (20 April 2025) | Viewed by 3312

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Special Issue Editors

Dr. Rajendra Prasath Palanisamy

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Guest Editor

Materials Physics and Applications (MPA), Los Alamos National Laboratory, Los Alamos, NM 87545, USA
Interests: structural health monitoring; acoustic non-destructive evaluation; machine learning; prognosis; signal processing

Dr. Alp Tugrul Findikoǧlu

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Guest Editor

Materials Physics and Applications (MPA), Los Alamos National Laboratory, Los Alamos, NM 87545, USA
Interests: structural health monitoring; acoustic metamaterial; acoustic non-destructive evaluation; high temperature acoustics; sensor technology

Dr. Oleksii Karpenko

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Guest Editor

Department of Electrical and Computer Engineering, Michigan State University, East Lansing, MI 48824, USA
Interests: non-destructive evaluation; ultrasonics; structural health monitoring; guided waves; measurements and instrumentation; FE modeling; microcontrollers; composite structures
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Special Issue Information

Dear Colleagues,

Structural health monitoring and non-destructive evaluation are vital tools for ensuring the operational safety of our infrastructure and various industrial process. With recent advancements in machine learning and sensing technology, structural health monitoring tools are expected to become more accurate, rapid and capable of damage diagnosis than before.

This Special Issue requests the submission of both review and original research articles related to the advancement of signal processing and sensing technology for improved structural health monitoring. Topics include but are not limited to the following:

Machine learning for feature extraction, imaging, signal processing, data fusion, and rapid damage diagnostics;
Sensing techniques such as phased array sensors, multi-sensor, etc.;
Damage diagnosis and prognosis using non-destructive evaluation;
Machine learning-assisted structural health monitoring;
Inspection of complex structures;
Damage imaging using advanced sensing techniques;
Multi sensor data fusion.

Dr. Rajendra Prasath Palanisamy
Dr. Alp Tugrul Findikoǧlu
Dr. Oleksii Karpenko
Guest Editors

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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 2600 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.

Keywords

acoustic sensing
defect imaging
thermal imaging
sensor development
machine learning
signal processing
non-destructive testing
composite inspection
material characterization
real-time monitoring
digital twin
training data scarcity

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Published Papers (3 papers)

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Research

16 pages, 4013 KiB

Open AccessArticle

Ultrasonic Sensor Modeling with Support Vector Regression

by Duy Ngoc Dang, Tri Minh Do, Rui Alexandre de Matos Araújo, Khang Hoang Vinh Nguyen and Can Duy Le

Sensors 2025, 25(3), 678; https://doi.org/10.3390/s25030678 - 23 Jan 2025

Viewed by 741

Abstract

This study proposes a novel approach for predicting the output behaviors of the Pepperl+Fuchs 3RG6232-3JS00-PF ultrasonic sensor. The sensor, integrated into the Festo MPS-PA Didactic System, serves to monitor the water level in a tank, facilitating water extraction to bottles delivered via a conveyor belt. This modeling approach represents the initial phase in the creation of a digital twin of the physical sensor, providing the capability for users to observe the sensor’s response and forecast its life cycle for maintenance objectives. This study utilizes the Festo MPS-PA Compact Didactic System and support vector regression (SVR) for data acquisition (DAQ), preprocessing, and model training with hyperparameter optimization. The objective of this modeling approach is to establish a digital framework for transition towards Industry 4.0. It holds the potential for creating a digital counterpart of the entire MPS-PA System when combining the proposed sensor modeling technique with computer-assisted design (CAD) software such as Siemens NX in the future. This would enable users to oversee the entire process in a three-dimensional visualization engine, such as Tecnomatix Plant Simulation. This research significantly contributes to the comprehension and application of digital twins in the realm of mechatronics and sensor systems technology. It also underscores the importance of digital twins in enhancing the efficiency and predictability of sensor systems. The method used in this paper involves predicting the rate of change (RoC) of the water level and then integrating this rate to estimate the actual water level, providing a robust approach for sensor data modeling and digital twin creation. The result shows a promising

6.99 %

error percentage. Full article

(This article belongs to the Special Issue Advances in Signal Processing and Sensing Technology for Improved Structural Health Monitoring)

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32 pages, 7399 KiB

Open AccessArticle

Improved Intelligent Condition Monitoring with Diagnostic Indicator Selection

by Urszula Jachymczyk, Paweł Knap and Krzysztof Lalik

Sensors 2025, 25(1), 137; https://doi.org/10.3390/s25010137 - 29 Dec 2024

Viewed by 826

Abstract

In this study, a predictive maintenance (PdM) system focused on feature selection for the detection and classification of simulated defects in wind turbine blades has been developed. Traditional PdM systems often rely on numerous, broadly chosen diagnostic indicators derived from vibration data, yet many of these features offer little added value and may even degrade model performance. General feature selection methods might not be suitable for PdM solutions, as information regarding observed faults is often misinterpreted or lost. To address these issues, a structured feature selection method based on correlation analysis supplemented with comprehensive visual evaluation was proposed. Unlike generic dimensionality reduction techniques, this approach preserves critical domain-specific information and avoids misinterpretation of fault indicators. By applying the proposed method, it was possible to successfully filter out redundant features, enabling simpler machine learning (ML) models to match or even surpass the performance of more complex deep learning (DL) architectures. The best results were achieved by a deep neural network trained on the full dataset, with accuracy, precision, recall, and F1 score of 97.30%, 97.23%, 97.23%, and 97.23%, respectively, while the top-performing ML model (a voting classifier trained on the reduced dataset) attained scores of 97.13%, 96.99%, 96.95%, and 96.94%. The proposed method for reducing condition indicators successfully decreased their number by approximately 3.27 times, simultaneously significantly reducing computational time of prediction, reaching up to 50% reduction for complex models. In doing so, we lowered computational demands and improved classification efficiency without compromising accuracy for ML models. Although feature reduction did not similarly benefit the metrics for DL models, these findings highlight that well-chosen, domain-relevant condition indicators can streamline data input and deliver interpretable, cost-effective PdM solutions suitable for industrial applications. Full article

(This article belongs to the Special Issue Advances in Signal Processing and Sensing Technology for Improved Structural Health Monitoring)

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15 pages, 1764 KiB

Open AccessArticle

Optimal Design of a Sensor Network for Guided Wave-Based Structural Health Monitoring Using Acoustically Coupled Optical Fibers

by Rohan Soman, Jee Myung Kim, Alex Boyer and Kara Peters

Sensors 2024, 24(19), 6354; https://doi.org/10.3390/s24196354 - 30 Sep 2024

Viewed by 1190

Abstract

Guided waves (GW) allow fast inspection of a large area and hence have received great interest from the structural health monitoring (SHM) community. Fiber Bragg grating (FBG) sensors offer several advantages but their use has been limited for the GW sensing due to its limited sensitivity. FBG sensors in the edge-filtering configuration have overcome this issue with sensitivity and there is a renewed interest in their use. Unfortunately, the FBG sensors and the equipment needed for interrogation is quite expensive, and hence their number is restricted. In the previous work by the authors, the number and location of the actuators was optimized for developing a SHM system with a single sensor and multiple actuators. But through the use of the phenomenon of acoustic coupling, multiple locations on the structure may be interrogated with a single FBG sensor. As a result, a sensor network with multiple sensing locations and a few actuators is feasible and cost effective. This paper develops a two-step methodology for the optimization of an actuator–sensor network harnessing the acoustic coupling ability of FBG sensors. In the first stage, the actuator–sensor network is optimized based on the application demands (coverage with at least three actuator–sensor pairs) and the cost of the instrumentation. In the second stage, an acoustic coupler network is designed to ensure high-fidelity measurements with minimal interference from other bond locations (overlap of measurements) as well as interference from features in the acoustically coupled circuit (fiber end, coupler, etc.). The non-sorting genetic algorithm (NSGA-II) is implemented for finding the optimal solution for both problems. The analytical implementation of the cost function is validated experimentally. The results show that the optimization does indeed have the potential to improve the quality of SHM while reducing the instrumentation costs significantly. Full article

(This article belongs to the Special Issue Advances in Signal Processing and Sensing Technology for Improved Structural Health Monitoring)

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