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Feature Papers in Fault Diagnosis & Sensors 2025
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Feature Papers in Fault Diagnosis & Sensors 2025

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

Deadline for manuscript submissions: 31 December 2025 | Viewed by 1411

Special Issue Editors

Prof. Dr. Francesc Pozo

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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 N 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: rotordynamics; fault diagnostics; rolling element bearings; oil-film bearings
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Jose Alfonso Antonino-Daviu

E-Mail Website1 Website2
Guest Editor
Department of Electrical Engineering, Universitat de València, 46022 Valencia, Spain
Interests: electric motors; fault diagnosis; transient analysis; signal processing; wavelet analysis; infrared thermography; time-frequency transforms
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 now compiling a collection of papers submitted by scholars in this research field for an upcoming Special Issue, entitled Feature Papers in Fault Diagnosis & Sensors 2025. This Special Issue will engage in topics such as fault detection and diagnosis, fault/failure prognosis, structural health monitoring, condition monitoring, intelligent sensors and sensor networks for fault diagnosis, digital twins for fault diagnosis, modeling, pattern recognition, machine learning, artificial intelligence and data analytics for fault diagnosis, failure prognosis and NDT.

The purpose of this Special Issue is to publish a set of papers that typifies the best, most insightful and influential original articles or comprehensive review papers. We are eager to publish papers that are widely read and highly influential within the field. We would also like to take this opportunity to call on more excellent scholars to join Fault Diagnosis & Sensors in order to achieve additional milestones together.

Prof. Dr. Francesc Pozo
Prof. Dr. Mohammad N 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 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

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

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

Related Special Issue

Published Papers (3 papers)

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Research

25 pages, 12941 KiB  
Article
Dynamic Multibody Modeling of Spherical Roller Bearings with Localized Defects for Large-Scale Rotating Machinery
by Luca Giraudo, Luigi Gianpio Di Maggio, Lorenzo Giorio and Cristiana Delprete
Sensors 2025, 25(8), 2419; https://doi.org/10.3390/s25082419 - 11 Apr 2025
Viewed by 165
Abstract
Early fault detection in rotating machinery is crucial for optimizing maintenance and minimizing downtime costs, especially in medium-to-large-scale industrial applications. This study presents a multibody model developed in the Simulink® Simscape environment to simulate the dynamic behavior of medium-sized spherical bearings. The [...] Read more.
Early fault detection in rotating machinery is crucial for optimizing maintenance and minimizing downtime costs, especially in medium-to-large-scale industrial applications. This study presents a multibody model developed in the Simulink® Simscape environment to simulate the dynamic behavior of medium-sized spherical bearings. The model includes descriptions of the six degrees of freedoms of each subcomponent, and was validated by comparison with experimental measurements acquired on a test rig capable of applying heavy radial loads. The results show a good fit between experimental and simulated signals in terms of identifying characteristic fault frequencies, which highlights the model’s ability to reproduce vibrations induced by localized defects on the inner and outer races. Amplitude differences can be attributed to simplifications such as neglected housing compliancies and lubrication effects, and do not alter the model’s effectiveness in detecting fault signatures. In conclusion, the developed model represents a promising tool for generating useful datasets for training diagnostic and prognostic algorithms, thereby contributing to the improvement of predictive maintenance strategies in industrial settings. Despite some amplitude discrepancies, the model proves useful for generating fault data and supporting condition monitoring strategies for industrial machinery. Full article
(This article belongs to the Special Issue Feature Papers in Fault Diagnosis & Sensors 2025)
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15 pages, 2034 KiB  
Article
An Innovative Study for Tool Wear Prediction Based on Stacked Sparse Autoencoder and Ensemble Learning Strategy
by Zhaopeng He, Tielin Shi and Xu Chen
Sensors 2025, 25(8), 2391; https://doi.org/10.3390/s25082391 - 9 Apr 2025
Viewed by 165
Abstract
Accurately predicting tool wear in real time is crucial to enhance the tool prognostics and health monitoring system in computerized numerical control (CNC) machining. This paper proposed a novel integrated deep learning model for predicting the wear of milling tools by fusing multi-sensor [...] Read more.
Accurately predicting tool wear in real time is crucial to enhance the tool prognostics and health monitoring system in computerized numerical control (CNC) machining. This paper proposed a novel integrated deep learning model for predicting the wear of milling tools by fusing multi-sensor features. The raw signals of vibration and cutting force acquired from the continuous cutting cycle were used to extract multi-sensor features throughout the full lifecycle of the milling tools in time, frequency, and wavelet domains, respectively. The sensitive features from these signals were identified through correlation analysis and used as input for the stacked sparse autoencoder (SSAE) model with backpropagation neural network (BPNN) as the regression layer to predict tool wear. SSAE models with different activation function configurations of hidden layers were utilized to construct deep neural network models with different prediction performance, which were taken as primary learners of integrated deep learning model. The intergrated SSAE model based on the stacking learning strategy applied the gradient boosting decision tree (GBDT) regression model with Bayes optimized hyperparameters as the secondary learner to predict tool wear. Compared to the single SSAE model and shallow machine learning models, the proposed method significantly improved both the prediction accuracy and reliability. Full article
(This article belongs to the Special Issue Feature Papers in Fault Diagnosis & Sensors 2025)
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22 pages, 11164 KiB  
Article
Acoustic Emission-Based Pipeline Leak Detection and Size Identification Using a Customized One-Dimensional DenseNet
by Faisal Saleem, Zahoor Ahmad, Muhammad Farooq Siddique, Muhammad Umar and Jong-Myon Kim
Sensors 2025, 25(4), 1112; https://doi.org/10.3390/s25041112 - 12 Feb 2025
Cited by 3 | Viewed by 801
Abstract
Effective leak detection and leak size identification are essential for maintaining the operational safety, integrity, and longevity of industrial pipelines. Traditional methods often suffer from high noise sensitivity, limited adaptability to non-stationary signals, and excessive computational costs, which limits their feasibility for real-time [...] Read more.
Effective leak detection and leak size identification are essential for maintaining the operational safety, integrity, and longevity of industrial pipelines. Traditional methods often suffer from high noise sensitivity, limited adaptability to non-stationary signals, and excessive computational costs, which limits their feasibility for real-time monitoring applications. This study presents a novel acoustic emission (AE)-based pipeline monitoring approach, integrating Empirical Wavelet Transform (EWT) for adaptive frequency decomposition with customized one-dimensional DenseNet architecture to achieve precise leak detection and size classification. The methodology begins with EWT-based signal segmentation, which isolates meaningful frequency bands to enhance leak-related feature extraction. To further improve signal quality, adaptive thresholding and denoising techniques are applied, filtering out low-amplitude noise while preserving critical diagnostic information. The denoised signals are processed using a DenseNet-based deep learning model, which combines convolutional layers and densely connected feature propagation to extract fine-grained temporal dependencies, ensuring the accurate classification of leak presence and severity. Experimental validation was conducted on real-world AE data collected under controlled leak and non-leak conditions at varying pressure levels. The proposed model achieved an exceptional leak detection accuracy of 99.76%, demonstrating its ability to reliably differentiate between normal operation and multiple leak severities. This method effectively reduces computational costs while maintaining robust performance across diverse operating environments. Full article
(This article belongs to the Special Issue Feature Papers in Fault Diagnosis & Sensors 2025)
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