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Feature Review Papers in Fault Diagnosis & Sensors
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Feature Review Papers in Fault Diagnosis & Sensors

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

Deadline for manuscript submissions: 30 January 2026 | Viewed by 4597

Special Issue Editors

Prof. Dr. Len Gelman

E-Mail Website
Guest Editor
Department of Engineering, School of Computing and Engineering, University of Huddersfield, Huddersfield HD1 3DH, UK
Interests: condition monitoring; structural health monitoring; non-destructive testing
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Gilbert-Rainer Gillich

E-Mail Website
Guest Editor
Department of Engineering Science, Babeș-Bolyai University Cluj-Napoca, 320085 Resita, Romania
Interests: vibration; damage detection; signal analysis; structural engineering
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Shuncong Zhong

E-Mail Website
Guest Editor
School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou 350116, China
Interests: optics & terahertz; diagnosis; structural health monitoring; NDT&E
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue is a dedicated collection of comprehensive review papers that aims to bring together the latest advancements and insights in fault diagnosis for condition monitoring, structural health monitoring, non-destructive testing and sensor technologies.

Potential topics include, but not limited to, the following:

  • Fault detection and diagnosis;
  • Fault/failure prognosis;
  • Structural health monitoring;
  • Non-destructive testing (NDT);
  • Condition monitoring;
  • Digital twins for fault diagnosis;
  • Artificial intelligence for fault diagnosis.

To be a significant resource for the scientific community, this Special Issue provides a platform for the dissemination of the current state of the art and novel perspectives in the field. We look forward to receiving your contributions and making this a successful and impactful Special Issue.

Prof. Dr. Len Gelman
Prof. Dr. Gilbert-Rainer Gillich
Prof. Dr. Shuncong Zhong
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.

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

  • condition monitoring
  • structural health monitoring
  • non-destructive testing

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (4 papers)

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Review

46 pages, 3080 KB  
Review
Machine Learning for Structural Health Monitoring of Aerospace Structures: A Review
by Gennaro Scarselli and Francesco Nicassio
Sensors 2025, 25(19), 6136; https://doi.org/10.3390/s25196136 - 4 Oct 2025
Viewed by 1005
Abstract
Structural health monitoring (SHM) plays a critical role in ensuring the safety and performance of aerospace structures throughout their lifecycle. As aircraft and spacecraft systems grow in complexity, the integration of machine learning (ML) into SHM frameworks is revolutionizing how damage is detected, [...] Read more.
Structural health monitoring (SHM) plays a critical role in ensuring the safety and performance of aerospace structures throughout their lifecycle. As aircraft and spacecraft systems grow in complexity, the integration of machine learning (ML) into SHM frameworks is revolutionizing how damage is detected, localized, and predicted. This review presents a comprehensive examination of recent advances in ML-based SHM methods tailored to aerospace applications. It covers supervised, unsupervised, deep, and hybrid learning techniques, highlighting their capabilities in processing high-dimensional sensor data, managing uncertainty, and enabling real-time diagnostics. Particular focus is given to the challenges of data scarcity, operational variability, and interpretability in safety-critical environments. The review also explores emerging directions such as digital twins, transfer learning, and federated learning. By mapping current strengths and limitations, this paper provides a roadmap for future research and outlines the key enablers needed to bring ML-based SHM from laboratory development to widespread aerospace deployment. Full article
(This article belongs to the Special Issue Feature Review Papers in Fault Diagnosis & Sensors)
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34 pages, 9482 KB  
Review
Methodologies for Remote Bridge Inspection—Review
by Diogo Ribeiro, Anna M. Rakoczy, Rafael Cabral, Vedhus Hoskere, Yasutaka Narazaki, Ricardo Santos, Gledson Tondo, Luis Gonzalez, José Campos Matos, Marcos Massao Futai, Yanlin Guo, Adriana Trias, Joaquim Tinoco, Vanja Samec, Tran Quang Minh, Fernando Moreu, Cosmin Popescu, Ali Mirzazade, Tomás Jorge, Jorge Magalhães, Franziska Schmidt, João Ventura and João Fonsecaadd Show full author list remove Hide full author list
Sensors 2025, 25(18), 5708; https://doi.org/10.3390/s25185708 - 12 Sep 2025
Viewed by 958
Abstract
This article addresses the state of the art of methodologies for bridge inspection with potential for inclusion in Bridge Management Systems (BMS) and within the scope of the IABSE Task Group 5.9 on Remote Inspection of Bridges. The document covers computer vision approaches, [...] Read more.
This article addresses the state of the art of methodologies for bridge inspection with potential for inclusion in Bridge Management Systems (BMS) and within the scope of the IABSE Task Group 5.9 on Remote Inspection of Bridges. The document covers computer vision approaches, including 3D geometric reconstitution (photogrammetry, LiDAR, and hybrid fusion strategies), damage and component identification (based on heuristics and Artificial Intelligence), and non-contact measurement of key structural parameters (displacements, strains, and modal parameters). Additionally, it addresses techniques for handling the large volumes of data generated by bridge inspections (Big Data), the use of Digital Twins for asset maintenance, and dedicated applications of Augmented Reality based on immersive environments for bridge inspection. These methodologies will contribute to safe, automated, and intelligent assessment and maintenance of bridges, enhancing resilience and lifespan of transportation infrastructure under changing climate. Full article
(This article belongs to the Special Issue Feature Review Papers in Fault Diagnosis & Sensors)
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21 pages, 2712 KB  
Review
The State of the Art and Potentialities of UAV-Based 3D Measurement Solutions in the Monitoring and Fault Diagnosis of Quasi-Brittle Structures
by Mohammad Hajjar, Emanuele Zappa and Gabriella Bolzon
Sensors 2025, 25(16), 5134; https://doi.org/10.3390/s25165134 - 19 Aug 2025
Viewed by 1005
Abstract
The structural health monitoring (SHM) of existing infrastructure and heritage buildings is essential for their preservation and safety. This is a review paper which focuses on modern three-dimensional (3D) measurement techniques, particularly those that enable the assessment of the structural response to environmental [...] Read more.
The structural health monitoring (SHM) of existing infrastructure and heritage buildings is essential for their preservation and safety. This is a review paper which focuses on modern three-dimensional (3D) measurement techniques, particularly those that enable the assessment of the structural response to environmental actions and operational conditions. The emphasis is on the detection of fractures and the identification of the crack geometry. While traditional monitoring systems—such as pendula, callipers, and strain gauges—have been widely used in massive, quasi-brittle structures like dams and masonry buildings, advancements in non-contact and computer-vision-based methods are increasingly offering flexible and efficient alternatives. The integration of drone-mounted systems facilitates access to challenging inspection zones, enabling the acquisition of quantitative data from full-field surface measurements. Among the reviewed techniques, digital image correlation (DIC) stands out for its superior displacement accuracy, while photogrammetry and time-of-flight (ToF) technologies offer greater operational flexibility but require additional processing to extract displacement data. The collected information contributes to the calibration of digital twins, supporting predictive simulations and real-time anomaly detection. Emerging tools based on machine learning and digital technologies further enhance damage detection capabilities and inform retrofitting strategies. Overall, vision-based methods show strong potential for outdoor SHM applications, though practical constraints such as drone payload and calibration requirements must be carefully managed. Full article
(This article belongs to the Special Issue Feature Review Papers in Fault Diagnosis & Sensors)
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28 pages, 3303 KB  
Review
Structural Fault Detection and Diagnosis for Combine Harvesters: A Critical Review
by Haiyang Wang, Liyun Lao, Honglei Zhang, Zhong Tang, Pengfei Qian and Qi He
Sensors 2025, 25(13), 3851; https://doi.org/10.3390/s25133851 - 20 Jun 2025
Cited by 1 | Viewed by 1087
Abstract
Combine harvesters, as essential equipment in agricultural engineering, frequently experience structural faults due to their complex structure and harsh working conditions, which severely affect their reliability and operational efficiency, leading to significant downtime and reduced agricultural productivity during critical harvesting periods. Therefore, developing [...] Read more.
Combine harvesters, as essential equipment in agricultural engineering, frequently experience structural faults due to their complex structure and harsh working conditions, which severely affect their reliability and operational efficiency, leading to significant downtime and reduced agricultural productivity during critical harvesting periods. Therefore, developing accurate and timely Fault Detection and Diagnosis (FDD) techniques is crucial for ensuring food security. This paper provides a systematic and critical review and analysis of the latest advancements in research on data-driven FDD methods for structural faults in combine harvesters. First, it outlines the typical structural sections of combine harvesters and their common structural fault types. Subsequently, it details the core steps of data-driven methods, including the acquisition of operational data from various sensors (e.g., vibration, acoustic, strain), signal preprocessing methods, signal processing and feature extraction techniques covering time-domain, frequency-domain, time–frequency domain combination, and modal analysis among others, and the use of machine learning and artificial intelligence models for fault pattern learning and diagnosis. Furthermore, it explores the required system and technical support for implementing such data-driven FDD methods, such as the applications of on-board diagnostic units, remote monitoring platforms, and simulation modeling. It provides an in-depth analysis of the key challenges currently encountered in this field, including difficulties in data acquisition, signal complexity, and insufficient model robustness, and consequently proposes future research directions, aiming to provide insights for the development of intelligent maintenance and efficient and reliable operation of combine harvesters and other complex agricultural machinery. Full article
(This article belongs to the Special Issue Feature Review Papers in Fault Diagnosis & Sensors)
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