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Feature Papers in Fault Diagnosis & Sensors 2024

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

Deadline for manuscript submissions: 31 December 2024 | Viewed by 6596

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Special Issue Editors

Prof. Dr. Francesc Pozo

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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
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Prof. Dr. Mohammad N Noori

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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
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Prof. Steven Chatterton

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Politecnico di Milano, Department of Mechanical Engineering, Via G. La Masa 1, 20156 Milano, Italy
Interests: rotordynamics; fault diagnostics; rolling element bearings; oil-film bearings
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Prof. Dr. Jose Alfonso Antonino-Daviu

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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
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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 2024. 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 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 achieve additional milestones together.

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

Published Papers (10 papers)

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Research

23 pages, 15975 KiB

Open AccessArticle

Integrating the Capsule-like Smart Aggregate-Based EMI Technique with Deep Learning for Stress Assessment in Concrete

by Quoc-Bao Ta, Quang-Quang Pham, Ngoc-Lan Pham and Jeong-Tae Kim

Sensors 2024, 24(14), 4738; https://doi.org/10.3390/s24144738 - 21 Jul 2024

Viewed by 424

Abstract

This study presents a concrete stress monitoring method utilizing 1D CNN deep learning of raw electromechanical impedance (EMI) signals measured with a capsule-like smart aggregate (CSA) sensor. Firstly, the CSA-based EMI measurement technique is presented by depicting a prototype of the CSA sensor and a 2 degrees of freedom (2 DOFs) EMI model for the CSA sensor embedded in a concrete cylinder. Secondly, the 1D CNN deep regression model is designed to adapt raw EMI responses from the CSA sensor for estimating concrete stresses. Thirdly, a CSA-embedded cylindrical concrete structure is experimented with to acquire EMI responses under various compressive loading levels. Finally, the feasibility and robustness of the 1D CNN model are evaluated for noise-contaminated EMI data and untrained stress EMI cases. Full article

(This article belongs to the Special Issue Feature Papers in Fault Diagnosis & Sensors 2024)

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22 pages, 619 KiB

Open AccessArticle

Distributed State Observer for Systems with Multiple Sensors under Time-Delay Information Exchange

by Wen Fang and Fanglai Zhu

Sensors 2024, 24(13), 4382; https://doi.org/10.3390/s24134382 - 5 Jul 2024

Viewed by 305

Abstract

The issues of state estimations based on distributed observers for linear time-invariant (LTI) systems with multiple sensors are discussed in this paper. We deal with the scenario when the information exchange has known time delays, and aim at designing a distributed observer for each subsystem such that each distributed observer can estimate the system state asymptotically by rejecting the time delay. To begin with, by rewriting the target system in a connecting form, a subsystem which is affected by the time-delay states of other nodes is established. And then, for this subsystem, a distributed observer with time delay is constructed. Moreover, an equivalent state transformation is made for the observer error dynamic system based on the observable canonic decomposition theorem. Further, in order to ensure that the distributed observer error dynamic system is asymptotically stable even if there exists a time delay, a linear matrix inequality (LMI) which is relative to the Laplace matrix is elaborately set up, and a special Lyapunov function candidate based on the LMI is considered. Next, based on the Lyapunov function and Lyapunov stability theory, we prove that the error dynamic system of the distributed observer is asymptotically stable, and the observer gain is determined by a feasible solution of the LMI. Finally, a simulation example is given to illustrate the effectiveness of the proposed method. Full article

(This article belongs to the Special Issue Feature Papers in Fault Diagnosis & Sensors 2024)

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22 pages, 1687 KiB

Open AccessArticle

Domain Adaptation for Bearing Fault Diagnosis Based on SimAM and Adaptive Weighting Strategy

by Ziyi Tang, Xinhao Hou, Xinheng Huang, Xin Wang and Jifeng Zou

Sensors 2024, 24(13), 4251; https://doi.org/10.3390/s24134251 - 30 Jun 2024

Viewed by 464

Abstract

Domain adaptation techniques are crucial for addressing the discrepancies between training and testing data distributions caused by varying operational conditions in practical bearing fault diagnosis. However, transfer fault diagnosis faces significant challenges under complex conditions with dispersed data and distinct distribution differences. Hence, this paper proposes CWT-SimAM-DAMS, a domain adaptation method for bearing fault diagnosis based on SimAM and an adaptive weighting strategy. The proposed scheme first uses Continuous Wavelet Transform (CWT) and Unsharp Masking (USM) for data preprocessing, and then feature extraction is performed using the Residual Network (ResNet) integrated with the SimAM module. This is combined with the proposed adaptive weighting strategy based on Joint Maximum Mean Discrepancy (JMMD) and Conditional Adversarial Domain Adaption Network (CDAN) domain adaptation algorithms, which minimizes the distribution differences between the source and target domains more effectively, thus enhancing domain adaptability. The proposed method is validated on two datasets, and experimental results show that it improves the accuracy of bearing fault diagnosis. Full article

(This article belongs to the Special Issue Feature Papers in Fault Diagnosis & Sensors 2024)

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19 pages, 7015 KiB

Open AccessArticle

Pipeline Leak Detection: A Comprehensive Deep Learning Model Using CWT Image Analysis and an Optimized DBN-GA-LSSVM Framework

by Muhammad Farooq Siddique, Zahoor Ahmad, Niamat Ullah, Saif Ullah and Jong-Myon Kim

Sensors 2024, 24(12), 4009; https://doi.org/10.3390/s24124009 - 20 Jun 2024

Viewed by 606

Abstract

Detecting pipeline leaks is an essential factor in maintaining the integrity of fluid transport systems. This paper introduces an advanced deep learning framework that uses continuous wavelet transform (CWT) images for precise detection of such leaks. Transforming acoustic signals from pipelines under various conditions into CWT scalograms, followed by signal processing by non-local means and adaptive histogram equalization, results in new enhanced leak-induced scalograms (ELIS) that capture detailed energy fluctuations across time-frequency scales. The fundamental approach takes advantage of a deep belief network (DBN) fine-tuned with a genetic algorithm (GA) and unified with a least squares support vector machine (LSSVM) to improve feature extraction and classification accuracy. The DBN-GA framework precisely extracts informative features, while the LSSVM classifier precisely distinguishes between leaky and non-leak conditions. By concentrating solely on the advanced capabilities of ELIS processed through an optimized DBN-GA-LSSVM model, this research achieves high detection accuracy and reliability, making a significant contribution to pipeline monitoring and maintenance. This innovative approach to capturing complex signal patterns can be applied to real-time leak detection and critical infrastructure safety in several industrial applications. Full article

(This article belongs to the Special Issue Feature Papers in Fault Diagnosis & Sensors 2024)

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31 pages, 19204 KiB

Open AccessArticle

Refinement and Validation of the Minimal Information Data-Modelling (MID) Method for Bridge Management

by Connor O’Higgins, David Hester, Patrick McGetrick, Wai Kei Ao and Elizabeth J. Cross

Sensors 2024, 24(12), 3879; https://doi.org/10.3390/s24123879 - 15 Jun 2024

Viewed by 351

Abstract

Various approaches have been proposed for bridge structural health monitoring. One of the earliest approaches proposed was tracking a bridge’s natural frequency over time to look for abnormal shifts in frequency that might indicate a change in stiffness. However, bridge frequencies change naturally as the structure’s temperature changes. Data models can be used to overcome this problem by predicting normal changes to a structure’s natural frequency and comparing it to the historical normal behaviour of the bridge and, therefore, identifying abnormal behaviour. Most of the proposed data modelling work has been from long-span bridges where you generally have large datasets to work with. A more limited body of research has been conducted where there is a sparse amount of data, but even this has only been demonstrated on single bridges. Therefore, the novelty of this work is that it expands on previous work using sparse instrumentation across a network of bridges. The data collected from four in-operation bridges were used to validate data models and test the capabilities of the data models across a range of bridge types/sizes. The MID approach was found to be able to detect an average frequency shift of 0.021 Hz across all of the data models. The significance of this demonstration across different bridge types is the practical utility of these data models to be used across entire bridge networks, enabling accurate and informed decision making in bridge maintenance and management. Full article

(This article belongs to the Special Issue Feature Papers in Fault Diagnosis & Sensors 2024)

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18 pages, 5050 KiB

Open AccessArticle

Design of Evaluation Classification Algorithm for Identifying Conveyor Belt Mistracking in a Continuous Transport System’s Digital Twin

by Gabriel Fedorko, Vieroslav Molnar, Beata Stehlikova, Peter Michalik and Jan Saliga

Sensors 2024, 24(12), 3810; https://doi.org/10.3390/s24123810 - 13 Jun 2024

Viewed by 479

Abstract

A prerequisite for continuous transport systems’ operation is their digital transformation, which interprets operating conditions based on the availability of a wide range of data and information in the form of measured quantities that can be obtained, for example, by experimental measurement. To implement digital transformation in continuous transport systems, it is necessary to examine and analyze the informative value of individual measured quantities in detail. Research in this area must focus on identifying addressable quantities with a clear, informative value. Such an approach enables the monitoring of continuous transport systems operation and performance of operational diagnostics, the objective of which should be identifying undesirable operating conditions. Within this paper, research will be presented aiming to verify the hypothesis that, based on a measurement of selected parameters, it is possible to identify belt mistracking in a continuous transport system. Belt mistracking is an undesirable condition that can cause a conveyor belt to converge and thus seriously turn off an entire transport system. The research results confirmed the established hypothesis. Based on this, an evaluation algorithm was created for on-time evaluation. The proposed algorithm is also suitable for the needs of a digital twin of a continuous transport system. Full article

(This article belongs to the Special Issue Feature Papers in Fault Diagnosis & Sensors 2024)

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16 pages, 3724 KiB

Open AccessArticle

Phase-Resolved Partial Discharge (PRPD) Pattern Recognition Using Image Processing Template Matching

by Aliyu Abubakar and Christos Zachariades

Sensors 2024, 24(11), 3565; https://doi.org/10.3390/s24113565 - 31 May 2024

Viewed by 513

Abstract

This paper proposes a new method for recognizing, extracting, and processing Phase-Resolved Partial Discharge (PRPD) patterns from two-dimensional plots to identify specific defect types affecting electrical equipment without human intervention while retaining the principals that make PRPD analysis an effective diagnostic technique. The proposed method does not rely on training complex deep learning algorithms which demand substantial computational resources and extensive datasets that can pose significant hurdles for the application of on-line partial discharge monitoring. Instead, the developed Cosine Cluster Net (CCNet) model, which is an image processing pipeline, can extract and process patterns from any two-dimensional PRPD plot before employing the cosine similarity function to measure the likeness of the patterns to predefined templates of known defect types. The PRPD pattern recognition capabilities of the model were tested using several manually classified PRPD images available in the existing literature. The model consistently produced similarity scores that identified the same defect type as the one from the manual classification. The successful defect type reporting from the initial trials of the CCNet model together with the speed of the identification, which typically does not exceed four seconds, indicates potential for real-time applications. Full article

(This article belongs to the Special Issue Feature Papers in Fault Diagnosis & Sensors 2024)

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25 pages, 13193 KiB

Open AccessArticle

FPGA-Microprocessor Based Sensor for Faults Detection in Induction Motors Using Time-Frequency and Machine Learning Methods

by Roque Alfredo Osornio-Rios, Isaias Cueva-Perez, Alvaro Ivan Alvarado-Hernandez, Larisa Dunai, Israel Zamudio-Ramirez and Jose Alfonso Antonino-Daviu

Sensors 2024, 24(8), 2653; https://doi.org/10.3390/s24082653 - 22 Apr 2024

Viewed by 1291

Abstract

Induction motors (IM) play a fundamental role in the industrial sector because they are robust, efficient, and low-cost machines. Changes in the environment, installation errors, or modifications to working conditions can generate faults in induction motors. The trend on IM fault detection is focused on the design techniques and sensors capable of evaluating multiple faults with various signals using non-invasive analysis. The methodology is based on processing electric current signals by applying the short-time Fourier transform (STFT). Additionally, the computation of the mean and standard deviation of infrared thermograms is proposed as main indicators. The proposed system combines both parameters by means of Support Vector Machine and k-nearest-neighbor classifiers. The development of the diagnostic system was done with digital hardware implementations using a Xilinx PYNQ Z2 card that integrates an FPGA with a microprocessor, thus taking advantage of the acquisition and processing of digital signals and images in hardware. The proposed method has proved to be effective for the classification of healthy (HLT), misalignment (MAMT), unbalance (UNB), damaged bearing (BDF), and broken rotor bar (BRB) faults with an accuracy close to 99%. Full article

(This article belongs to the Special Issue Feature Papers in Fault Diagnosis & Sensors 2024)

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24 pages, 28419 KiB

Open AccessArticle

Intelligent Fault Diagnosis of Rolling Bearing Based on Gramian Angular Difference Field and Improved Dual Attention Residual Network

by Anshi Tong, Jun Zhang and Liyang Xie

Sensors 2024, 24(7), 2156; https://doi.org/10.3390/s24072156 - 27 Mar 2024

Cited by 2 | Viewed by 787

Abstract

With the rapid development of smart manufacturing, data-driven deep learning (DL) methods are widely used for bearing fault diagnosis. Aiming at the problem of model training crashes when data are imbalanced and the difficulty of traditional signal analysis methods in effectively extracting fault features, this paper proposes an intelligent fault diagnosis method of rolling bearings based on Gramian Angular Difference Field (GADF) and Improved Dual Attention Residual Network (IDARN). The original vibration signals are encoded as 2D-GADF feature images for network input; the residual structures will incorporate dual attention mechanism to enhance the integration ability of the features, while the group normalization (GN) method is introduced to overcome the bias caused by data discrepancies; and then the model is trained to complete the classification of faults. In order to verify the superiority of the proposed method, the data obtained from Case Western Reserve University (CWRU) bearing data and bearing fault experimental equipment were compared with other popular DL methods, and the proposed model performed optimally. The method eventually achieved an average identification accuracy of 99.2% and 97.9% on two different types of datasets, respectively. Full article

(This article belongs to the Special Issue Feature Papers in Fault Diagnosis & Sensors 2024)

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35 pages, 17863 KiB

Open AccessArticle

A Secure Dual-Layer Fault Protection Strategy for Distribution Network with DERs: Enhancing Security in the Face of Communication Challenges

by Wael Al Hanaineh, Jose Matas, Josep M. Guerrero and Mostafa Bakkar

Sensors 2024, 24(4), 1057; https://doi.org/10.3390/s24041057 - 6 Feb 2024

Viewed by 751

Abstract

Earlier protection methods mainly focused on using communication channels to transmit trip signals between the protective devices (PDs), with no solutions provided in the case of communication failure. Therefore, this paper introduces a dual-layer protection system to ensure secure protection against fault events in the Distribution Systems (DSs), particularly in light of communication failures. The initial layer uses the Total Harmonic Distortion (THD), the estimates of the amplitude voltages, and the zero-sequence grid voltage components, functioning as a fault sensor, to formulate an adaptive algorithm based on a Finite State Machine (FSM) for the detection and isolation of faults within the grid. This layer primarily relies on communication protocols for effective coordination. A Second-Order Generalized Integrator (SOGI) expedites the derivation of the estimated variables, ensuring fast detection with minimal computational overhead. The second layer uses the behavior of the positive- and negative-sequence components of the grid voltages during fault events to locate and isolate these faults. In the event that the first layer exposes a communication failure, the second layer will automatically be activated to ensure secure protection as it operates, using the local information of the Protective devices (PDs), without the need for communication channels to transmit trip signals between the PDs. The proposed protection system has been assessed using simulations with MATLAB/Simulink and providing experimental results considering an IEEE 9-bus standard radial system. The obtained results confirm the capability of the system for identifying and isolating different types of faults, varying conditions, and modifications to the grid configuration. The results show good behavior of the initial THD-based layer, with fast time responses ranging from 6 to 8.5 ms in all the examined scenarios. In contrast, the sequence-based layer exhibits a protection time response of approximately 150 ms, making it a viable backup option in the event of a communication failure. Full article

(This article belongs to the Special Issue Feature Papers in Fault Diagnosis & Sensors 2024)

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