Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
9.4
4.0
Journals
Sensors
Special Issues
Feature Papers in Fault Diagnosis & Sensors 2026
sensors-logo
Submit to Special Issue Submit Abstract to Special Issue Review for Sensors Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Feature Papers in Fault Diagnosis & Sensors 2026

A Special Issue of Sensors (ISSN 1424-8220) belonging to the section "Fault Diagnosis & Sensors".

Deadline for manuscript submissions: 31 December 2026 | Viewed by 2480

Editors

Prof. Dr. Francesc Pozo

E-Mail Website
Guest Editor
Department of Mathematics, Escola d’Enginyeria de Barcelona Est (EEBE), Universitat Politècnica de Catalunya (UPC), Campus Diagonal-Besòs (CDB), Eduard Maristany, 16, 08019 Barcelona, Spain
Interests: structural health monitoring; condition monitoring; piezoelectric transducers; PZT; data science; wind turbines
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Mohammad Noori

E-Mail Website
Guest Editor
1. Department of Mechanical Engineering, California Polytechnic State University, San Luis Obispo, CA 93405, USA
2. School of Civil Engineering, University of Leeds, Leeds LS2 9JT, UK
Interests: AI-based methods for structural health monitoring and dynamic response; random vibrations; hysteretic systems; seismic isolation; reliability and resilience
Special Issues, Collections and Topics in MDPI journals
Prof. Steven Chatterton

E-Mail Website
Guest Editor
Department of Mechanical Engineering, Politecnico di Milano, Via G. La Masa 1, 20156 Milano, Italy
Interests: rotor dynamics; fault diagnostics; rolling element bearings; oil-film bearings
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Jose Alfonso Antonino-Daviu

E-Mail Website
Guest Editor
Instituto Tecnológico de la Energía, Universitat Politècnica de València, 46022 Valencia, Spain
Interests: electrical machines; fault diagnosis; reliability; signal processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are pleased to announce that the Fault Diagnosis & Sensors section is currently compiling a collection of high-quality papers for an upcoming Special Issue entitled “Feature Papers in Fault Diagnosis & Sensors 2026”. This Special Issue aims to showcase outstanding contributions from scholars working in this research field. The Special Issue will cover, but is not limited to, the following topics: fault detection and diagnosis; fault and failure prognosis; structural health monitoring; condition monitoring; intelligent sensors and sensor networks for fault diagnosis; digital twins for fault diagnosis; modeling and simulation; pattern recognition; machine learning; artificial intelligence; and data analytics applied to fault diagnosis, failure prognosis, and non-destructive testing (NDT).

The purpose of this Special Issue is to publish a selection of papers that represent original, insightful, and influential research, including both high-quality original research articles and review papers. Our goal is to curate a collection of contributions that will be widely read and have a lasting impact on the field. We would also like to take this opportunity to invite outstanding researchers to engage with and contribute to the Fault Diagnosis & Sensors section, as we work together to achieve new milestones and advance this important research area.

Prof. Dr. Francesc Pozo
Prof. Dr. Mohammad Noori
Prof. Steven Chatterton
Prof. Dr. Jose Alfonso Antonino-Daviu
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 250 words) can be sent to the Editorial Office for assessment.

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-anonymized 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

  • fault detection and diagnosis
  • fault/failure prognosis
  • structural health monitoring
  • condition monitoring
  • non-destructive testing

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

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

Related Special Issues

Published Papers (4 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

26 pages, 11374 KB  
Article
Drive-By Time-Varying Feature Extraction for Bridge Damage Detection Using Second-Order Synchrosqueezing Transform
by Mingzhe Gao, Xinqun Zhu and Jianchun Li
Sensors 2026, 26(16), 5170; https://doi.org/10.3390/s26165170 - 15 Aug 2026
Viewed by 255
Abstract
Recently, drive-by bridge structural health monitoring has gained increasing attention due to its potential to be a cost-effective way to monitor the highway infrastructure. The pre-installed sensory system on a passing vehicle is used to capture the spatiotemporal response of the bridge for [...] Read more.
Recently, drive-by bridge structural health monitoring has gained increasing attention due to its potential to be a cost-effective way to monitor the highway infrastructure. The pre-installed sensory system on a passing vehicle is used to capture the spatiotemporal response of the bridge for structural health monitoring. The vehicle passing over the bridge is a time-varying process, and it is a big challenge to extract the time-varying characteristics of vehicle–bridge interaction systems for structural health monitoring. This paper aims to develop a drive-by time-varying feature extraction approach for bridge structural damage detection using the second-order synchrosqueezing transform. The research first examined the impact of various factors on the frequency changes in VBI systems, including the vehicle mass, stiffness, speed, road surface profiles, measurement noise, and different types of damage. When compared with traditional synchrosqueezing transform, the proposed method provides a clearer and more accurate time–frequency representation. A 6 m-long two-span bridge model is also built in the laboratory and the pre-installed wireless sensory system on a passing vehicle captures the vehicle and bridge interaction response. The time-varying features are extracted from dynamic responses of the vehicle passing over the bridge using the proposed method. Numerical and experimental results show that the proposed approach is effective and accurate enough to extract the time-varying features for bridge damage detection. Full article
(This article belongs to the Special Issue Feature Papers in Fault Diagnosis & Sensors 2026)
►▼ Show Figures

Figure 1

28 pages, 2256 KB  
Article
Towards Fault-Tolerant AGV Task Scheduling in Flexible Manufacturing Systems Using a Tree-Based Max-Plus Predictive Approach
by Dominik Zaborniak, Paweł Kasza, Marcin Pazera and Marcin Witczak
Sensors 2026, 26(12), 3898; https://doi.org/10.3390/s26123898 - 19 Jun 2026
Viewed by 421
Abstract
Efficient task assignment for mobile robots is a crucial challenge in modern intralogistics. This paper presents an integrated cyber-physical framework combining predictive tree search on switching max-plus linear systems with a physical IoT-based dispatch interface. The scheduling problem is modelled as a discrete [...] Read more.
Efficient task assignment for mobile robots is a crucial challenge in modern intralogistics. This paper presents an integrated cyber-physical framework combining predictive tree search on switching max-plus linear systems with a physical IoT-based dispatch interface. The scheduling problem is modelled as a discrete event system, where standard max-plus algebra captures robot synchronization, and a switching mechanism represents alternative resource assignments. To address real-world operational disturbances, the predictive model is enhanced with a fault-tolerant control (FTC) mechanism that dynamically estimates and adapts to non-stationary transport delays. The resulting decision space, which grows exponentially with the prediction horizon, is explored via a predictive tree search algorithm utilizing a quadratic cost function to penalize excessive and uneven transport times. The physical dispatch layer is realized using KIS.BOX IoT devices acting as operator-controlled stations, communicating with the central controller via a WebSocket/STOMP event stream and a lightweight REST API. Simulation results obtained in a Blender 3D environment demonstrate that the proposed FTC predictive strategy significantly reduces the variance of task completion times under fault conditions compared to a baseline First-In-First-Out approach. Furthermore, the IoT integration successfully simulates and validates the feasibility of human-in-the-loop task injection within a realistic, stochastic scenario. Full article
(This article belongs to the Special Issue Feature Papers in Fault Diagnosis & Sensors 2026)
►▼ Show Figures

Figure 1

28 pages, 5795 KB  
Article
Evaluating the Influence of Normalisation Procedures on a Negative Selection Algorithm to Enhance Damage Detection
by Alberto Barontini, Maria-Giovanna Masciotta, Luís F. Ramos, Paulo Amado-Mendes, Paulo B. Lourenço and Giuseppe Brando
Sensors 2026, 26(11), 3492; https://doi.org/10.3390/s26113492 - 1 Jun 2026
Viewed by 606
Abstract
Damage detection is a critical component of Structural Health Monitoring (SHM) strategies. Data-driven machine learning methods are widely employed for this purpose. However, their effective application to the management of structures and infrastructure requires addressing key challenges, in particular the limited knowledge during [...] Read more.
Damage detection is a critical component of Structural Health Monitoring (SHM) strategies. Data-driven machine learning methods are widely employed for this purpose. However, their effective application to the management of structures and infrastructure requires addressing key challenges, in particular the limited knowledge during training of how damage affects monitored features. Although one-class classification algorithms may be adopted, their performance strongly depends on the appropriate definition of their components and the tuning of their parameters. When this optimisation is performed using undamaged data only, it may result in classifiers that are insensitive to small-scale damage. Within this context, the present study investigates the sensitivity of damage detection performance of a Deterministically Generated Negative Selection Algorithm to feature scaling and two intrinsic algorithm parameters. A novel strategy for generating artificial damaged data to support parameter tuning is proposed and evaluated against alternative approaches. A wide range of parameter values is explored, considering multiple pairs of monitored features as detection spaces, and four feature scaling methods are compared. Feature scaling is a fundamental aspect of classification problems. Thus, the main findings may be generalised to other machine learning algorithms for damage detection. To ensure full control over the monitoring data and underlying phenomena, a numerical case study is adopted. A replicable framework for generating controlled yet realistic structural monitoring data is presented. The simulated monitoring captures the natural frequencies of a bridge, accounting for temperature effects, and incorporates three damage scenarios: one diffuse and two localised at critical locations. The analysis highlights the importance of conducting anomaly detection in feature spaces where damage affects each feature differently. Pairing a damage-sensitive feature with one unaffected by damage (e.g., temperature) may be advantageous when damage effects are not known a priori. Although suboptimal, the proposed strategy for generating damaged data for parameter tuning outperforms approaches based solely on undamaged data. The identified parameter trends suggest that small detector radii and relatively short censoring distances improve algorithm performance across all normalisation strategies and damage scenarios. The results show that feature scaling has limited influence for large damage extents but becomes critical for the early detection of minor damage. Z-score normalisation provides the best overall balance between false negatives and false positives, whereas methods using denominator multipliers smaller than one provide higher True Positive Rates for small damage extents. Full article
(This article belongs to the Special Issue Feature Papers in Fault Diagnosis & Sensors 2026)
►▼ Show Figures

Figure 1

20 pages, 3736 KB  
Article
Design and Evaluation of a Flexible Substrate-Based Microstrip Sensor for Partial Discharge Detection in High-Voltage Equipment
by Shuhao Dong and Xiao Hu
Sensors 2026, 26(11), 3304; https://doi.org/10.3390/s26113304 - 22 May 2026
Viewed by 481
Abstract
Partial discharge (PD) detection effectively identifies insulation defects in power equipment. Radio frequency (RF) methods for PD detection offer promising advantages due to their non-invasive measurement capability and ability to locate discharge sources. However, microstrip antennas used as RF sensors for PD detection [...] Read more.
Partial discharge (PD) detection effectively identifies insulation defects in power equipment. Radio frequency (RF) methods for PD detection offer promising advantages due to their non-invasive measurement capability and ability to locate discharge sources. However, microstrip antennas used as RF sensors for PD detection suffer from narrow bandwidth and limited installation flexibility. To address these limitations, this paper presents a novel flexible microstrip antenna design. By incorporating a partial ground plane and oblique-cut meandering techniques and optimizing the structural parameters using an improved whale optimization algorithm (I-WOA), the operating bandwidth is expanded from 0.612–0.625 GHz to 0.346–2.0 GHz, while the overall size is reduced to 75.3% of its original dimensions. The antenna’s performance was validated through GTEM cell measurements and PD calibration pulse tests, confirming its suitability for RF detection of PD in power equipment such as transformers and cable joints. Notably, when the antenna was conformally wrapped around a cable joint, the response amplitude increased by 14%. This study contributes to the development of a low-cost, broadband, and flexibly installable RF sensor for partial discharge detection. Full article
(This article belongs to the Special Issue Feature Papers in Fault Diagnosis & Sensors 2026)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-4
Back to TopTop