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Structural Health Monitoring Using Sensors and Machine Learning
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Structural Health Monitoring Using Sensors and Machine Learning

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

Deadline for manuscript submissions: closed (20 July 2024) | Viewed by 15188

Special Issue Editors

Prof. Dr. Tat-Hean Gan

E-Mail Website
Guest Editor
Brunel Innovation Centre, Brunel University London, Uxbridge, UK
Interests: ultrasonic guided waves; non-destructive testing; artificial intelligence; non-contact ultrasonics; Industry 4.0; signal processing; sensors; instrumentations
Special Issues, Collections and Topics in MDPI journals
Dr. Kamran Pedram

E-Mail Website
Guest Editor
Electrical and Electronic Engineering, University of Greenwich, Greenwich ME4 4TB, UK
Interests: signal processing; ultrasonic guided wave testing; NDT; structural health monitoring; machine learning; AI; sensors; image processing

Special Issue Information

Dear Colleagues,

The need to carry out structural health monitoring (SHM) for old structures is growing due to the rising demand for infrastructure and transportation structure facilities. This issue focuses on the application of the most recent sensing technology as well as machine learning to structural health monitoring. This issue aims to gather research relating to innovative SHM methods that utilise the newest sensing and machine learning technologies to generate efficient and consistent techniques. We welcome research in the field of sensor-based SHM and machine learning aiming to supplement or replace conventional manual inspections, including the latest experimental and theoretical studies, findings, and computational investigations. Topics of interest include:

  • Structural health monitoring;
  • Digital twins;
  • Machine learning;
  • Damage detection;
  • Artificial intelligence;
  • Guided wave testing;
  • Acoustic emission;
  • Vibration;
  • Non-destructive testing;
  • Signal processing;
  • Real-time monitoring;
  • Modal analysis/updating;
  • Intelligent algorithms for data mining;
  • Optimal sensor placement;
  • Performance evaluation.

Prof. Dr. Tat-Hean Gan
Dr. Kamran Pedram
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 submissions that pass pre-check are 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.

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Published Papers (5 papers)

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Research

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22 pages, 31331 KiB  
Article
A Zero-Shot Learning Approach for Blockage Detection and Identification Based on the Stacking Ensemble Model
by Chaoqun Li, Zao Feng, Mingkai Jiang and Zhenglang Wang
Sensors 2024, 24(17), 5596; https://doi.org/10.3390/s24175596 - 29 Aug 2024
Viewed by 769
Abstract
A data-driven approach to defect identification requires many labeled samples for model training. Yet new defects tend to appear during data acquisition cycles, which can lead to a lack of labeled samples of these new defects. Aiming at solving this problem, we proposed [...] Read more.
A data-driven approach to defect identification requires many labeled samples for model training. Yet new defects tend to appear during data acquisition cycles, which can lead to a lack of labeled samples of these new defects. Aiming at solving this problem, we proposed a zero-shot pipeline blockage detection and identification method based on stacking ensemble learning. The experimental signals were first decomposed using variational modal decomposition (VMD), and then, the information entropy was calculated for each intrinsic modal function (IMF) component to construct the feature sets. Second, the attribute matrix was established according to the attribute descriptions of the defect categories, and the stacking ensemble attribute learner was used for the attribute learning of defect features. Finally, defect identification was accomplished by comparing the similarity within the attribute matrices. The experimental results show that target defects can be identified even without targeted training samples. The model showed better classification performance on the six sets of experimental data, and the average recognition accuracy of the model for unknown defect categories reached 72.5%. Full article
(This article belongs to the Special Issue Structural Health Monitoring Using Sensors and Machine Learning)
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14 pages, 3490 KiB  
Article
Mitigating the Impact of Temperature Variations on Ultrasonic Guided Wave-Based Structural Health Monitoring through Variational Autoencoders
by Rafael Junges, Luca Lomazzi, Lorenzo Miele, Marco Giglio and Francesco Cadini
Sensors 2024, 24(5), 1494; https://doi.org/10.3390/s24051494 - 25 Feb 2024
Cited by 5 | Viewed by 1501
Abstract
Structural health monitoring (SHM) has become paramount for developing cheaper and more reliable maintenance policies. The advantages coming from adopting such process have turned out to be particularly evident when dealing with plated structures. In this context, state-of-the-art methods are based on exciting [...] Read more.
Structural health monitoring (SHM) has become paramount for developing cheaper and more reliable maintenance policies. The advantages coming from adopting such process have turned out to be particularly evident when dealing with plated structures. In this context, state-of-the-art methods are based on exciting and acquiring ultrasonic-guided waves through a permanently installed sensor network. A baseline is registered when the structure is healthy, and newly acquired signals are compared to it to detect, localize, and quantify damage. To this purpose, the performance of traditional methods has been overcome by data-driven approaches, which allow processing a larger amount of data without losing diagnostic information. However, to date, no diagnostic method can deal with varying environmental and operational conditions (EOCs). This work aims to present a proof-of-concept that state-of-the-art machine learning methods can be used for reducing the impact of EOCs on the performance of damage diagnosis methods. Generative artificial intelligence was leveraged to mitigate the impact of temperature variations on ultrasonic guided wave-based SHM. Specifically, variational autoencoders and singular value decomposition were combined to learn the influence of temperature on guided waves. After training, the generative part of the algorithm was used to reconstruct signals at new unseen temperatures. Moreover, a refined version of the algorithm called forced variational autoencoder was introduced to further improve the reconstruction capabilities. The accuracy of the proposed framework was demonstrated against real measurements on a composite plate. Full article
(This article belongs to the Special Issue Structural Health Monitoring Using Sensors and Machine Learning)
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19 pages, 6206 KiB  
Article
Prediction of Technical State of Mechanical Systems Based on Interpretive Neural Network Model
by Evgeniy Kononov, Andrey Klyuev and Mikhail Tashkinov
Sensors 2023, 23(4), 1892; https://doi.org/10.3390/s23041892 - 8 Feb 2023
Cited by 6 | Viewed by 1708
Abstract
A classic problem in prognostic and health management (PHM) is the prediction of the remaining useful life (RUL). However, until now, there has been no algorithm presented to achieve perfect performance in this challenge. This study implements a less explored approach: binary classification [...] Read more.
A classic problem in prognostic and health management (PHM) is the prediction of the remaining useful life (RUL). However, until now, there has been no algorithm presented to achieve perfect performance in this challenge. This study implements a less explored approach: binary classification of the state of mechanical systems at a given forecast horizon. To prove the effectiveness of the proposed approach, tests were conducted on the C-MAPSS sample dataset. The obtained results demonstrate the achievement of an almost maximal performance threshold. The explainability of artificial intelligence (XAI) using the SHAP (Shapley Additive Explanations) feature contribution estimation method for classification models trained on data with and without a sliding window technique is also investigated. Full article
(This article belongs to the Special Issue Structural Health Monitoring Using Sensors and Machine Learning)
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20 pages, 8469 KiB  
Article
Diversion Detection in Small-Diameter HDPE Pipes Using Guided Waves and Deep Learning
by Abdullah Zayat, Mohanad Obeed and Anas Chaaban
Sensors 2022, 22(24), 9586; https://doi.org/10.3390/s22249586 - 7 Dec 2022
Cited by 2 | Viewed by 4942
Abstract
In this paper, we propose a novel technique for the inspection of high-density polyethylene (HDPE) pipes using ultrasonic sensors, signal processing, and deep neural networks (DNNs). Specifically, we propose a technique that detects whether there is a diversion on a pipe or not. [...] Read more.
In this paper, we propose a novel technique for the inspection of high-density polyethylene (HDPE) pipes using ultrasonic sensors, signal processing, and deep neural networks (DNNs). Specifically, we propose a technique that detects whether there is a diversion on a pipe or not. The proposed model transmits ultrasound signals through a pipe using a custom-designed array of piezoelectric transmitters and receivers. We propose to use the Zadoff–Chu sequence to modulate the input signals, then utilize its correlation properties to estimate the pipe channel response. The processed signal is then fed to a DNN that extracts the features and decides whether there is a diversion or not. The proposed technique demonstrates an average classification accuracy of 90.3% (when one sensor is used) and 99.6% (when two sensors are used) on 34 inch pipes. The technique can be readily generalized for pipes of different diameters and materials. Full article
(This article belongs to the Special Issue Structural Health Monitoring Using Sensors and Machine Learning)
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Review

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28 pages, 4453 KiB  
Review
Influence of Smart Sensors on Structural Health Monitoring Systems and Future Asset Management Practices
by D. M. G. Preethichandra, T. G. Suntharavadivel, Pushpitha Kalutara, Lasitha Piyathilaka and Umer Izhar
Sensors 2023, 23(19), 8279; https://doi.org/10.3390/s23198279 - 6 Oct 2023
Cited by 10 | Viewed by 5065
Abstract
Recent developments in networked and smart sensors have significantly changed the way Structural Health Monitoring (SHM) and asset management are being carried out. Since the sensor networks continuously provide real-time data from the structure being monitored, they constitute a more realistic image of [...] Read more.
Recent developments in networked and smart sensors have significantly changed the way Structural Health Monitoring (SHM) and asset management are being carried out. Since the sensor networks continuously provide real-time data from the structure being monitored, they constitute a more realistic image of the actual status of the structure where the maintenance or repair work can be scheduled based on real requirements. This review is aimed at providing a wealth of knowledge from the working principles of sensors commonly used in SHM, to artificial-intelligence-based digital twin systems used in SHM and proposes a new asset management framework. The way this paper is structured suits researchers and practicing experts both in the fields of sensors as well as in asset management equally. Full article
(This article belongs to the Special Issue Structural Health Monitoring Using Sensors and Machine Learning)
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