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Feature Papers in Fault Diagnosis & Sensors 2023

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

Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 60356

Special Issue Editors


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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

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Guest Editor
Mechanical Engineering Department, California Polytechnic State University, San Luis Obispo, CA 93401, USA
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

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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 the scholars in this research field for an upcoming Special Issue, entitled Feature Papers in Fault Diagnosis & Sensors 2023. This 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 which 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 we can achieve additional milestones together.

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

Published Papers (18 papers)

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26 pages, 17114 KiB  
Article
Predicting Surface Roughness in Turning Complex-Structured Workpieces Using Vibration-Signal-Based Gaussian Process Regression
by Jianyong Chen, Jiayao Lin, Ming Zhang and Qizhe Lin
Sensors 2024, 24(7), 2117; https://doi.org/10.3390/s24072117 - 26 Mar 2024
Cited by 1 | Viewed by 679
Abstract
Surface roughness prediction is a pivotal aspect of the manufacturing industry, as it directly influences product quality and process optimization. This study introduces a predictive model for surface roughness in the turning of complex-structured workpieces utilizing Gaussian Process Regression (GPR) informed by vibration [...] Read more.
Surface roughness prediction is a pivotal aspect of the manufacturing industry, as it directly influences product quality and process optimization. This study introduces a predictive model for surface roughness in the turning of complex-structured workpieces utilizing Gaussian Process Regression (GPR) informed by vibration signals. The model captures parameters from both the time and frequency domains of the turning tool, encompassing the mean, median, standard deviation (STD), and root mean square (RMS) values. The signal is from the time to frequency domain and it is executed using Welch’s method complemented by time–frequency domain analysis employing three levels of Daubechies Wavelet Packet Transform (WPT). The selected features are then utilized as inputs for the GPR model to forecast surface roughness. Empirical evidence indicates that the GPR model can accurately predict the surface roughness of turned complex-structured workpieces. This predictive strategy has the potential to improve product quality, streamline manufacturing processes, and minimize waste within the industry. Full article
(This article belongs to the Special Issue Feature Papers in Fault Diagnosis & Sensors 2023)
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17 pages, 8378 KiB  
Article
Enhancing Seismic Damage Detection and Assessment in Highway Bridge Systems: A Pattern Recognition Approach with Bayesian Optimization
by Xiao Liang
Sensors 2024, 24(2), 611; https://doi.org/10.3390/s24020611 - 18 Jan 2024
Cited by 1 | Viewed by 1050
Abstract
Highway bridges stand as paramount elements within transportation infrastructure systems. The ability to ensure swift recovery after extreme events, such as earthquakes, is a fundamental trait of resilient communities. Consequently, expediting the recovery process necessitates near real-time diagnosis of structural damage to provide [...] Read more.
Highway bridges stand as paramount elements within transportation infrastructure systems. The ability to ensure swift recovery after extreme events, such as earthquakes, is a fundamental trait of resilient communities. Consequently, expediting the recovery process necessitates near real-time diagnosis of structural damage to provide dependable information. In this study, a data-driven approach for damage detection and assessment is investigated, focusing on bridge columns—the pivotal supporting elements of bridge systems—based on simulations derived from nonlinear time history analysis. This research introduces a set of cumulative intensity-based damage features, whose efficacy is demonstrated through unsupervised learning techniques. Leveraging the support vector machine, a prominent pattern recognition algorithm in supervised learning, alongside Bayesian optimization with a Gaussian process, seismic damage detection and assessment are explored. Encouragingly, the methodology yields high estimation accuracies for both binary outcomes (indicating the presence of damage or the occurrence of collapse) and multi-class classifications (indicating the severity of damage). This breakthrough opens avenues for the practical implementation of on-board sensor computing, enabling near real-time damage detection and assessment in bridge structures. Full article
(This article belongs to the Special Issue Feature Papers in Fault Diagnosis & Sensors 2023)
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28 pages, 5627 KiB  
Article
Moving-Principal-Component-Analysis-Based Structural Damage Detection for Highway Bridges in Operational Environments
by Ye Yuan, Xinqun Zhu and Jun Li
Sensors 2024, 24(2), 383; https://doi.org/10.3390/s24020383 - 8 Jan 2024
Viewed by 954
Abstract
With the deterioration of bridge performance and ever-increasing amounts of traffic, bridge safety is becoming a concern for the engineering community. A method that can assess a bridge’s condition in real time is urgently needed. The main factors that hinder bridge condition assessment [...] Read more.
With the deterioration of bridge performance and ever-increasing amounts of traffic, bridge safety is becoming a concern for the engineering community. A method that can assess a bridge’s condition in real time is urgently needed. The main factors that hinder bridge condition assessment are the uncertain operational environments. A new moving principal component analysis (MPCA)-based method is developed for structural damage detection in bridges in operational environments in this paper. Two main operational environmental factors, the environmental temperature and traffic loads, are studied in the assessment process to verify the robustness and practicality of the proposed method. The numerical and experimental results show that the proposed method is effective and accurate in assessing the bridge’s condition in operational environments. Full article
(This article belongs to the Special Issue Feature Papers in Fault Diagnosis & Sensors 2023)
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16 pages, 4473 KiB  
Article
Remaining Useful Life Estimation of Hollow Worn Railway Vehicle Wheels via On-Board Random Vibration-Based Wheel Tread Depth Estimation
by Ilias A. Iliopoulos and John S. Sakellariou
Sensors 2024, 24(2), 375; https://doi.org/10.3390/s24020375 - 8 Jan 2024
Viewed by 882
Abstract
The problem of remaining useful life estimation (RULE) of hollow worn railway vehicle wheels in terms of remaining mileage via wheel tread depth estimation using on-board vibration signals from a single accelerometer on the bogie frame is presently investigated. This is achieved based [...] Read more.
The problem of remaining useful life estimation (RULE) of hollow worn railway vehicle wheels in terms of remaining mileage via wheel tread depth estimation using on-board vibration signals from a single accelerometer on the bogie frame is presently investigated. This is achieved based on the introduction of a statistical time series method that employs: (i) advanced data-driven stochastic Functionally Pooled models for the modeling of the vehicle dynamics under different wheel tread depths in a range of interest until a critical limit, as well as tread depth estimation through a proper optimization procedure, and (ii) a wheel tread depth evolution function with respect to the vehicle running mileage that interconnects the estimated hollow wear with the remaining useful mileage. The method’s RULE performance is investigated via hundreds of Simpack-based Monte Carlo simulations with an Attiko Metro S.A. vehicle and many hollow worn wheels scenarios which are not used for the method’s training. The obtained results indicate the accurate estimation of the wheels tread depth with a mean absolute error of ∼0.07 mm that leads to a corresponding small error of ∼3% with respect to the wheels remaining useful mileage. In addition, the comparison with a recently introduced Multiple Model (MM)-based multi-health state classification method for RULE, demonstrates the better performance of the postulated method that achieves 81.17% True Positive Rate (TPR) which is significantly higher than the 45.44% of the MM method. Full article
(This article belongs to the Special Issue Feature Papers in Fault Diagnosis & Sensors 2023)
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23 pages, 13983 KiB  
Article
Enhancing the Performance of a Large Aperture Ultrasound System (LAUS): A Combined Approach of Simulation and Measurement for Transmitter–Receiver Optimization
by Prathik Prabhakara, Vera Lay, Frank Mielentz, Ernst Niederleithinger and Matthias Behrens
Sensors 2024, 24(1), 100; https://doi.org/10.3390/s24010100 - 24 Dec 2023
Viewed by 1094
Abstract
The Large Aperture Ultrasound System (LAUS) developed at BAM is known for its ability to penetrate thick objects, especially concrete structures commonly used in nuclear waste storage and other applications in civil engineering. Although the current system effectively penetrates up to ~9 m, [...] Read more.
The Large Aperture Ultrasound System (LAUS) developed at BAM is known for its ability to penetrate thick objects, especially concrete structures commonly used in nuclear waste storage and other applications in civil engineering. Although the current system effectively penetrates up to ~9 m, further optimization is imperative to enhance the safety and integrity of disposal structures for radioactive or toxic waste. This study focuses on enhancing the system’s efficiency by optimizing the transducer spacing, ensuring that resolution is not compromised. An array of twelve horizontal shear wave transducers was used to find a balance between penetration depth and resolution. Systematic adjustments of the spacing between transmitter and receiver units were undertaken based on target depth ranges of known reflectors at depth ranges from 5 m to 10 m. The trade-offs between resolution and artifact generation were meticulously assessed. This comprehensive study employs a dual approach using both simulations and measurements to investigate the performance of transducer units spaced at 10 cm, 20 cm, 30 cm, and 40 cm. We found that for depths up to 5 m, a spacing of 10 cm for LAUS transducer units provided the best resolution as confirmed by both simulations and measurements. This optimal distance is particularly effective in achieving clear reflections and a satisfactory signal-to-noise ratio (SNR) in imaging scenarios with materials such as thick concrete structures. However, when targeting depths greater than 10 m, we recommend increasing the distance between the transducers to 20 cm. This increased spacing improves the SNR in comparison to other spacings, as seen in the simulation of a 10 m deep backwall. Our results emphasize the critical role of transducer spacing in achieving the desired SNR and resolution, especially in the context of depth imaging requirements for LAUS applications. In addition to the transducer spacing, different distances between individual sets of measurement positions were tested. Overall, keeping the minimal possible distance between measurement position offsets provides the best imaging results at greater depths. The proposed optimizations for the LAUS in this study are primarily relevant to applications on massive nuclear structures for nuclear waste management. This research highlights the need for better LAUS efficiency in applications such as sealing structures, laying the foundation for future technological advances in this field. Full article
(This article belongs to the Special Issue Feature Papers in Fault Diagnosis & Sensors 2023)
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16 pages, 5328 KiB  
Article
Sensitivity Analysis of Intensity-Modulated Plastic Optical Fiber Sensors for Effective Aging Detection in Rapeseed Transformer Oil
by Ugochukwu Elele, Azam Nekahi, Arshad Arshad, Kate McAulay and Issouf Fofana
Sensors 2023, 23(24), 9796; https://doi.org/10.3390/s23249796 - 13 Dec 2023
Cited by 1 | Viewed by 955
Abstract
As the focus tilts toward online detection methodologies for transformer oil aging, bypassing challenges associated with traditional offline methods, such as sample contamination and misinterpretation, fiber optic sensors are gaining traction due to their compact nature, cost-effectiveness, and resilience to electromagnetic disturbances that [...] Read more.
As the focus tilts toward online detection methodologies for transformer oil aging, bypassing challenges associated with traditional offline methods, such as sample contamination and misinterpretation, fiber optic sensors are gaining traction due to their compact nature, cost-effectiveness, and resilience to electromagnetic disturbances that are typical in high-voltage environments. This study delves into the sensitivity analysis of intensity-modulated plastic optical fiber sensors. The investigation encompasses key determinants such as the influence of optical source wavelengths, noise response dynamics, ramifications of varying sensing lengths, and repeatability assessments. Our findings highlight that elongating sensing length detrimentally affects both linearity response and repeatability, largely attributed to a diminished resistance to noise. Additionally, the choice of the optical source wavelength proved to be a critical variable in assessing sensor sensitivity. Full article
(This article belongs to the Special Issue Feature Papers in Fault Diagnosis & Sensors 2023)
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23 pages, 7828 KiB  
Article
Methodologies and Challenges for Optimal Sensor Placement in Historical Masonry Buildings
by Estefanía Chaves, Alberto Barontini, Nuno Mendes, Víctor Compán and Paulo B. Lourenço
Sensors 2023, 23(23), 9304; https://doi.org/10.3390/s23239304 - 21 Nov 2023
Cited by 1 | Viewed by 981
Abstract
As ageing structures and infrastructures become a global concern, structural health monitoring (SHM) is seen as a crucial tool for their cost-effective maintenance. Promising results obtained for modern and conventional constructions suggested the application of SHM to historical masonry buildings as well. However, [...] Read more.
As ageing structures and infrastructures become a global concern, structural health monitoring (SHM) is seen as a crucial tool for their cost-effective maintenance. Promising results obtained for modern and conventional constructions suggested the application of SHM to historical masonry buildings as well. However, this presents peculiar shortcomings and open challenges. One of the most relevant aspects that deserve more research is the optimisation of the sensor placement to tackle well-known issues in ambient vibration testing for such buildings. The present paper focuses on the application of optimal sensor placement (OSP) strategies for dynamic identification in historical masonry buildings. While OSP techniques have been extensively studied in various structural contexts, their application in historical masonry buildings remains relatively limited. This paper discusses the challenges and opportunities of OSP in this specific context, analysing and discussing real-world examples, as well as a numerical benchmark application to illustrate its complexities. This article aims to shed light on the progress and issues associated with OSP in masonry historical buildings, providing a detailed problem formulation, identifying ongoing challenges and presenting promising solutions for future improvements. Full article
(This article belongs to the Special Issue Feature Papers in Fault Diagnosis & Sensors 2023)
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21 pages, 11567 KiB  
Article
Guided Wave Characteristic Research and Probabilistic Crack Evaluation in Complex Multi-Layer Stringer Splice Joint Structure
by Jian Chen, Yusen Xu, Shenfang Yuan and Zhen Qin
Sensors 2023, 23(22), 9224; https://doi.org/10.3390/s23229224 - 16 Nov 2023
Viewed by 1008
Abstract
Multi-layer and multi-rivet connection structures are critical components in the structural integrity of a commercial aircraft, in which elements like skin, splice plate, strengthen patch, and stringer are fastened together layer by layer with multiple rows of rivets for assembling the fuselage and [...] Read more.
Multi-layer and multi-rivet connection structures are critical components in the structural integrity of a commercial aircraft, in which elements like skin, splice plate, strengthen patch, and stringer are fastened together layer by layer with multiple rows of rivets for assembling the fuselage and wings. Their non-detachability and inaccessibility pose significant challenges for assessing their health states. Guided wave-based structural health monitoring (SHM) has shown great potential for on-line damage monitoring in hidden structural elements. However, the multi-layer and multi-rivet features introduce complex boundary conditions for guided wave propagation and sensor layouts. Few studies have discussed the guided wave characteristic and damage diagnosis in multi-layer and multi-rivet connection structures. This paper comprehensively researches guided wave propagation characteristics in the multi-layer stringer splice joint (MLSSJ) structure through experiments and numerical simulations for the first time, consequently developing sensor layout rules for such complex structures. Moreover, a Gaussian process (GP)-based probabilistic mining diagnosis method with path-wave band features is proposed. Experiments on a batch of MLSSJ specimens are performed for validation, in which increasing crack lengths are set in each specimen. The results indicate the effectiveness of the proposed probabilistic evaluation method. The maximum root mean squared error of the GP quantitative diagnosis is 1.5 mm. Full article
(This article belongs to the Special Issue Feature Papers in Fault Diagnosis & Sensors 2023)
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25 pages, 6243 KiB  
Article
A Fault Diagnosis Strategy for Analog Circuits with Limited Samples Based on the Combination of the Transformer and Generative Models
by Zhen Jia, Qiqi Yang, Yang Li, Siyu Wang, Peng Xu and Zhenbao Liu
Sensors 2023, 23(22), 9125; https://doi.org/10.3390/s23229125 - 11 Nov 2023
Cited by 1 | Viewed by 1113
Abstract
As a pivotal integral component within electronic systems, analog circuits are of paramount importance for the timely detection and precise diagnosis of their faults. However, the objective reality of limited fault samples in operational devices with analog circuitry poses challenges to the direct [...] Read more.
As a pivotal integral component within electronic systems, analog circuits are of paramount importance for the timely detection and precise diagnosis of their faults. However, the objective reality of limited fault samples in operational devices with analog circuitry poses challenges to the direct applicability of existing diagnostic methods. This study proposes an innovative approach for fault diagnosis in analog circuits by integrating deep convolutional generative adversarial networks (DCGANs) with the Transformer architecture, addressing the problem of insufficient fault samples affecting diagnostic performance. Firstly, the employment of the continuous wavelet transform in combination with Morlet wavelet basis functions serves as a means to derive time–frequency images, enhancing fault feature recognition while converting time-domain signals into time–frequency representations. Furthermore, the augmentation of datasets utilizing deep convolutional GANs is employed to generate synthetic time–frequency signals from existing fault data. The Transformer-based fault diagnosis model was trained using a mixture of original signals and generated signals, and the model was subsequently tested. Through experiments involving single and multiple fault scenarios in three simulated circuits, a comparative analysis of the proposed approach was conducted with a number of established benchmark methods, and its effectiveness in various scenarios was evaluated. In addition, the ability of the proposed fault diagnosis technique was investigated in the presence of limited fault data samples. The outcome reveals that the proposed diagnostic method exhibits a consistently high overall accuracy of over 96% in diverse test scenarios. Moreover, it delivers satisfactory performance even when real sample sizes are as small as 150 instances in various fault categories. Full article
(This article belongs to the Special Issue Feature Papers in Fault Diagnosis & Sensors 2023)
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19 pages, 2845 KiB  
Article
A Large-Scale Sensor Layout Optimization Algorithm for Improving the Accuracy of Inverse Finite Element Method
by Zhenyi Zhao, Kangyu Chen, Yimin Liu and Hong Bao
Sensors 2023, 23(19), 8176; https://doi.org/10.3390/s23198176 - 29 Sep 2023
Viewed by 989
Abstract
The inverse finite element method (iFEM) based on fiber grating sensors has been demonstrated as a shape sensing method for health monitoring of large and complex engineering structures. However, the existing optimization algorithms cause the local optima and low computational efficiency for high-dimensional [...] Read more.
The inverse finite element method (iFEM) based on fiber grating sensors has been demonstrated as a shape sensing method for health monitoring of large and complex engineering structures. However, the existing optimization algorithms cause the local optima and low computational efficiency for high-dimensional strain sensor layout optimization problems of complex antenna truss models. This paper proposes the improved adaptive large-scale cooperative coevolution (IALSCC) algorithm to obtain the strain sensors deployment on iFEM, and the method includes the initialization strategy, adaptive region partitioning strategy, and gbest selection and particle updating strategies, enhancing the reconstruction accuracy of iFEM for antenna truss structure and algorithm efficiency. The strain sensors optimization deployment on the antenna truss model for different postures is achieved, and the numerical results show that the optimization algorithm IALSCC proposed in this paper can well handle the high-dimensional sensor layout optimization problem. Full article
(This article belongs to the Special Issue Feature Papers in Fault Diagnosis & Sensors 2023)
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15 pages, 3960 KiB  
Article
The Proper Use of Fibre-Optic Sensors to Monitor the Condition of the Steam Boiler Hanger Rods
by Magdalena Palacz, Bolesław Bąk, Łukasz Felkowski, Piotr Duda and Iliya Iliev
Sensors 2023, 23(17), 7522; https://doi.org/10.3390/s23177522 - 30 Aug 2023
Cited by 1 | Viewed by 1046
Abstract
Fibre optic sensors with integrated Bragg gratings are widely used in the diagnostics of machinery and equipment. They achieved their popularity thanks to their relatively simple operating principles. In addition, they allow the continuous monitoring of several variable physical parameters of objects, such [...] Read more.
Fibre optic sensors with integrated Bragg gratings are widely used in the diagnostics of machinery and equipment. They achieved their popularity thanks to their relatively simple operating principles. In addition, they allow the continuous monitoring of several variable physical parameters of objects, such as strain or temperature change, which directly translates into immediate feedback regarding potential damage. However, despite the easy-to-understand operating principle, selecting a specific type for a particular application can be problematic. This article aims to present the process of selecting the optimal set of fibre-optic sensors with integrated Bragg grating, which can be used in the process of monitoring the stress state of hanger rods of an engineering object such as an industrial boiler. The hanger rods of such boilers require constant technical supervision; however, the current measurement methods do not provide an effective and non-invasive diagnostic method. Therefore, the authors have undertaken the task of developing a universal diagnostic strategy for hanger rods. To this end, they will present the results of an analysis of the applicability of FBGs, examples of the use of different types of sensors, their installation methods, and the technical capabilities of the equipment necessary to handle the signals recorded with these sensors. Exemplary results of strain measurements of a selected hanger rod performed by the traditional method used now and with a selected FBG fibre optic sensor will be presented. In conclusion, concrete technical suggestions will be presented to be implemented in the existing industrial facility during the next part of the study. Full article
(This article belongs to the Special Issue Feature Papers in Fault Diagnosis & Sensors 2023)
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31 pages, 11340 KiB  
Article
Design and Implementation of a Fuzzy Classifier for FDI Applied to Industrial Machinery
by Silvia Maria Zanoli and Crescenzo Pepe
Sensors 2023, 23(15), 6954; https://doi.org/10.3390/s23156954 - 4 Aug 2023
Viewed by 913
Abstract
In the present work, the design and the implementation of a Fault Detection and Isolation (FDI) system for an industrial machinery is proposed. The case study is represented by a multishaft centrifugal compressor used for the syngas manufacturing. The system has been conceived [...] Read more.
In the present work, the design and the implementation of a Fault Detection and Isolation (FDI) system for an industrial machinery is proposed. The case study is represented by a multishaft centrifugal compressor used for the syngas manufacturing. The system has been conceived for the monitoring of the faults which may damage the multishaft centrifugal compressor: instrument single and multiple faults have been considered as well as process faults like fouling of the compressor stages and break of the thrust bearing. A new approach that combines Principal Component Analysis (PCA), Cluster Analysis and Pattern Recognition is developed. A novel procedure based on the statistical test ANOVA (ANalysis Of VAriance) is applied to determine the most suitable number of Principal Components (PCs). A key design issue of the proposed fault isolation scheme is the data Cluster Analysis performed to solve the practical issue of the complexity growth experienced when analyzing process faults, which typically involve many variables. In addition, an automatic online Pattern Recognition procedure for finding the most probable faults is proposed. Clustering procedure and Pattern Recognition are implemented within a Fuzzy Faults Classifier module. Experimental results on real plant data illustrate the validity of the approach. The main benefits produced by the FDI system concern the improvement of the maintenance operations, the enhancement of the reliability and availability of the compressor, the increase in the plant safety while achieving reduction in plant functioning costs. Full article
(This article belongs to the Special Issue Feature Papers in Fault Diagnosis & Sensors 2023)
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15 pages, 633 KiB  
Article
An Online Anomaly Detection Approach for Fault Detection on Fire Alarm Systems
by Emanuel Sousa Tomé, Rita P. Ribeiro, Inês Dutra and Arlete Rodrigues
Sensors 2023, 23(10), 4902; https://doi.org/10.3390/s23104902 - 19 May 2023
Cited by 4 | Viewed by 2328
Abstract
The early detection of fire is of utmost importance since it is related to devastating threats regarding human lives and economic losses. Unfortunately, fire alarm sensory systems are known to be prone to failures and frequent false alarms, putting people and buildings at [...] Read more.
The early detection of fire is of utmost importance since it is related to devastating threats regarding human lives and economic losses. Unfortunately, fire alarm sensory systems are known to be prone to failures and frequent false alarms, putting people and buildings at risk. In this sense, it is essential to guarantee smoke detectors’ correct functioning. Traditionally, these systems have been subject to periodic maintenance plans, which do not consider the state of the fire alarm sensors and are, therefore, sometimes carried out not when necessary but according to a predefined conservative schedule. Intending to contribute to designing a predictive maintenance plan, we propose an online data-driven anomaly detection of smoke sensors that model the behaviour of these systems over time and detect abnormal patterns that can indicate a potential failure. Our approach was applied to data collected from independent fire alarm sensory systems installed with four customers, from which about three years of data are available. For one of the customers, the obtained results were promising, with a precision score of 1 with no false positives for 3 out of 4 possible faults. Analysis of the remaining customers’ results highlighted possible reasons and potential improvements to address this problem better. These findings can provide valuable insights for future research in this area. Full article
(This article belongs to the Special Issue Feature Papers in Fault Diagnosis & Sensors 2023)
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26 pages, 8404 KiB  
Article
A Comparative Study of Smart THD-Based Fault Protection Techniques for Distribution Networks
by Wael Al Hanaineh, Jose Matas and Josep M. Guerrero
Sensors 2023, 23(10), 4874; https://doi.org/10.3390/s23104874 - 18 May 2023
Cited by 2 | Viewed by 1398
Abstract
The integration of Distributed Generators (DGs) into distribution systems (DSs) leads to more reliable and efficient power delivery for customers. However, the possibility of bi-directional power flow creates new technical problems for protection schemes. This poses a threat to conventional strategies because the [...] Read more.
The integration of Distributed Generators (DGs) into distribution systems (DSs) leads to more reliable and efficient power delivery for customers. However, the possibility of bi-directional power flow creates new technical problems for protection schemes. This poses a threat to conventional strategies because the relay settings have to be adjusted depending on the network topology and operational mode. As a solution, it is important to develop novel fault protection techniques to ensure reliable protection and avoid unnecessary tripping. In this regard, Total Harmonic Distortion (THD) can be used as a key parameter for evaluating the grid’s waveform quality during fault events. This paper presents a comparison between two DS protection strategies that employ THD levels, estimated amplitude voltages, and zero-sequence components as instantaneous indicators during the faults that function as a kind of fault sensor to detect, identify, and isolate faults. The first method uses a Multiple Second Order Generalized Integrator (MSOGI) to obtain the estimated variables, whereas the second method uses a single SOGI for the same purpose (SOGI-THD). Both methods rely on communication lines between protective devices (PDs) to facilitate coordinated protection. The effectiveness of these methods is assessed by using simulations in MATLAB/Simulink considering various factors such as different types of faults and DG penetrations, different fault resistances and fault locations in the proposed network. Moreover, the performance of these methods is compared with conventional overcurrent and differential protections. The results show that the SOGI-THD method is highly effective in detecting and isolating faults with a time interval of 6–8.5 ms using only three SOGIs while requiring only 447 processor cycles for execution. In comparison to other protection methods, the SOGI-THD method exhibits a faster response time and a lower computational burden. Furthermore, the SOGI-THD method is robust to harmonic distortion, as it considers pre-existing harmonic content before the fault and avoids interference with the fault detection process. Full article
(This article belongs to the Special Issue Feature Papers in Fault Diagnosis & Sensors 2023)
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12 pages, 3489 KiB  
Article
Environmental Robustness and Resilience of Direct-Write Ultrasonic Transducers Made from P(VDF-TrFE) Piezoelectric Coating
by Jin Kyu Han, Voon-Kean Wong, David Boon Kiang Lim, Percis Teena Christopher Subhodayam, Ping Luo and Kui Yao
Sensors 2023, 23(10), 4696; https://doi.org/10.3390/s23104696 - 12 May 2023
Cited by 1 | Viewed by 1393
Abstract
Conformability, lightweight, consistency and low cost due to batch fabrication in situ on host structures are the attractive advantages of ultrasonic transducers made of piezoelectric polymer coatings for structural health monitoring (SHM). However, knowledge about the environmental impacts of piezoelectric polymer ultrasonic transducers [...] Read more.
Conformability, lightweight, consistency and low cost due to batch fabrication in situ on host structures are the attractive advantages of ultrasonic transducers made of piezoelectric polymer coatings for structural health monitoring (SHM). However, knowledge about the environmental impacts of piezoelectric polymer ultrasonic transducers is lacking, limiting their widespread use for SHM in industries. The purpose of this work is to evaluate whether direct-write transducers (DWTs) fabricated from piezoelectric polymer coatings can withstand various natural environmental impacts. The ultrasonic signals of the DWTs and properties of the piezoelectric polymer coatings fabricated in situ on the test coupons were evaluated during and after exposure to various environmental conditions, including high and low temperatures, icing, rain, humidity, and the salt fog test. Our experimental results and analyses showed that it is promising for the DWTs made of piezoelectric P(VDF-TrFE) polymer coating with an appropriate protective layer to pass various operational conditions according to US standards. Full article
(This article belongs to the Special Issue Feature Papers in Fault Diagnosis & Sensors 2023)
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29 pages, 17163 KiB  
Article
Structural Health Monitoring of Composite Pipelines Utilizing Fiber Optic Sensors and an AI-Based Algorithm—A Comprehensive Numerical Study
by Wael A. Altabey, Zhishen Wu, Mohammad Noori and Hamed Fathnejat
Sensors 2023, 23(8), 3887; https://doi.org/10.3390/s23083887 - 11 Apr 2023
Cited by 17 | Viewed by 2768
Abstract
In this paper, a structural health monitoring (SHM) system is proposed to provide automatic early warning for detecting damage and its location in composite pipelines at an early stage. The study considers a basalt fiber reinforced polymer (BFRP) pipeline with an embedded Fiber [...] Read more.
In this paper, a structural health monitoring (SHM) system is proposed to provide automatic early warning for detecting damage and its location in composite pipelines at an early stage. The study considers a basalt fiber reinforced polymer (BFRP) pipeline with an embedded Fiber Bragg grating (FBG) sensory system and first discusses the shortcomings and challenges with incorporating FBG sensors for accurate detection of damage information in pipelines. The novelty and the main focus of this study is, however, a proposed approach that relies on designing an integrated sensing-diagnostic SHM system that has the capability to detect damage in composite pipelines at an early stage via implementation of an artificial intelligence (AI)-based algorithm combining deep learning and other efficient machine learning methods using an Enhanced Convolutional Neural Network (ECNN) without retraining the model. The proposed architecture replaces the softmax layer by a k-Nearest Neighbor (k-NN) algorithm for inference. Finite element models are developed and calibrated by the results of pipe measurements under damage tests. The models are then used to assess the patterns of the strain distributions of the pipeline under internal pressure loading and under pressure changes due to bursts, and to find the relationship of strains at different locations axially and circumferentially. A prediction algorithm for pipe damage mechanisms using distributed strain patterns is also developed. The ECNN is designed and trained to identify the condition of pipe deterioration so the initiation of damage can be detected. The strain results from the current method and the available experimental results in the literature show excellent agreement. The average error between the ECNN data and FBG sensor data is 0.093%, thus confirming the reliability and accuracy of the proposed method. The proposed ECNN achieves high performance with 93.33% accuracy (P%), 91.18% regression rate (R%) and a 90.54% F1-score (F%). Full article
(This article belongs to the Special Issue Feature Papers in Fault Diagnosis & Sensors 2023)
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Review

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65 pages, 3771 KiB  
Review
Prognostic and Health Management of Critical Aircraft Systems and Components: An Overview
by Shuai Fu and Nicolas P. Avdelidis
Sensors 2023, 23(19), 8124; https://doi.org/10.3390/s23198124 - 27 Sep 2023
Cited by 4 | Viewed by 35781
Abstract
Prognostic and health management (PHM) plays a vital role in ensuring the safety and reliability of aircraft systems. The process entails the proactive surveillance and evaluation of the state and functional effectiveness of crucial subsystems. The principal aim of PHM is to predict [...] Read more.
Prognostic and health management (PHM) plays a vital role in ensuring the safety and reliability of aircraft systems. The process entails the proactive surveillance and evaluation of the state and functional effectiveness of crucial subsystems. The principal aim of PHM is to predict the remaining useful life (RUL) of subsystems and proactively mitigate future breakdowns in order to minimize consequences. The achievement of this objective is helped by employing predictive modeling techniques and doing real-time data analysis. The incorporation of prognostic methodologies is of utmost importance in the execution of condition-based maintenance (CBM), a strategic approach that emphasizes the prioritization of repairing components that have experienced quantifiable damage. Multiple methodologies are employed to support the advancement of prognostics for aviation systems, encompassing physics-based modeling, data-driven techniques, and hybrid prognosis. These methodologies enable the prediction and mitigation of failures by identifying relevant health indicators. Despite the promising outcomes in the aviation sector pertaining to the implementation of PHM, there exists a deficiency in the research concerning the efficient integration of hybrid PHM applications. The primary aim of this paper is to provide a thorough analysis of the current state of research advancements in prognostics for aircraft systems, with a specific focus on prominent algorithms and their practical applications and challenges. The paper concludes by providing a detailed analysis of prospective directions for future research within the field. Full article
(This article belongs to the Special Issue Feature Papers in Fault Diagnosis & Sensors 2023)
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28 pages, 4743 KiB  
Review
Smart Detecting and Versatile Wearable Electrical Sensing Mediums for Healthcare
by Ahsan Ali, Muaz Ashfaq, Aleen Qureshi, Umar Muzammil, Hamna Shaukat, Shaukat Ali, Wael A. Altabey, Mohammad Noori and Sallam A. Kouritem
Sensors 2023, 23(14), 6586; https://doi.org/10.3390/s23146586 - 21 Jul 2023
Cited by 12 | Viewed by 3509
Abstract
A rapidly expanding global population and a sizeable portion of it that is aging are the main causes of the significant increase in healthcare costs. Healthcare in terms of monitoring systems is undergoing radical changes, making it possible to gauge or monitor the [...] Read more.
A rapidly expanding global population and a sizeable portion of it that is aging are the main causes of the significant increase in healthcare costs. Healthcare in terms of monitoring systems is undergoing radical changes, making it possible to gauge or monitor the health conditions of people constantly, while also removing some minor possibilities of going to the hospital. The development of automated devices that are either attached to organs or the skin, continually monitoring human activity, has been made feasible by advancements in sensor technologies, embedded systems, wireless communication technologies, nanotechnologies, and miniaturization being ultra-thin, lightweight, highly flexible, and stretchable. Wearable sensors track physiological signs together with other symptoms such as respiration, pulse, and gait pattern, etc., to spot unusual or unexpected events. Help may therefore be provided when it is required. In this study, wearable sensor-based activity-monitoring systems for people are reviewed, along with the problems that need to be overcome. In this review, we have shown smart detecting and versatile wearable electrical sensing mediums in healthcare. We have compiled piezoelectric-, electrostatic-, and thermoelectric-based wearable sensors and their working mechanisms, along with their principles, while keeping in view the different medical and healthcare conditions and a discussion on the application of these biosensors in human health. A comparison is also made between the three types of wearable energy-harvesting sensors: piezoelectric-, electrostatic-, and thermoelectric-based on their output performance. Finally, we provide a future outlook on the current challenges and opportunities. Full article
(This article belongs to the Special Issue Feature Papers in Fault Diagnosis & Sensors 2023)
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