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Sensor-Based Frequency, Time–Frequency and Higher-Order Signal Processing for Condition Monitoring, Structural Health Monitoring and Non-Destructive Testing (Second Edition)

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

Deadline for manuscript submissions: closed (25 October 2025) | Viewed by 14311

Special Issue Editors

Prof. Dr. Len Gelman

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

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

Special Issue Information

Dear Colleagues,

Following the success of our previous Special Issue, "Sensor-Based Frequency, Time-Frequency, and Higher Order Signal Processing for Condition Monitoring, Structural Health Monitoring, and Non-Destructive Testing", we are now accepting submissions for the second edition of this Special Issue. Sensor-based technologies for condition monitoring, structural health monitoring, and non-destructive testing have become very important in most industrial sectors and academic research.

The main challenges related to these technologies are as follows:

Most industrial assets/machineries are utilized in non-stationary operations;

Most excitations of engineering structures and materials and, therefore, sensor outputs are non-stationary;

One of the most important industrial requirements of these technologies is an effective diagnosis at an early stage of damage development.

Addressing these challenges requires novel signal processing developments that are related to intelligent sensors, frequency, time–frequency, and non-linear higher-order spectral analysis of sensor data, as well as those that are related to the adaptation of sensor-based technologies to non-stationary conditions for machineries, structures, and materials.

Therefore, this SI focuses on sensor-based technologies and systems for machineries, structures, and materials, with a main focus on novel signal processing developments related to intelligent sensors, the signal processing of sensor data, artificial intelligence for decision making, and the adaptation of sensor-based technologies to non-stationary conditions for machineries, structures, and materials.

This Special Issue will not cover non-novel case study papers. Potential authors need to make clear statements on the novelty of their paper, which should be based on comprehensive state-of-the art reviews.

The following keywords describe this SI:

  • Frequency, time–frequency, and higher-order signal processing for sensor-based technologies and systems for condition monitoring, structural health monitoring, and non-destructive testing;
  • Artificial intelligence for sensor-based technologies and systems for condition monitoring, structural health monitoring, and non-destructive testing;
  • Sensor-based structural health monitoring technologies and systems for engineering structures;
  • Sensor-based non-destructive testing technologies and systems for materials;
  • Sensor-based condition monitoring technologies and systems for machinery and complex electromechanical assets;
  • Adaptive sensor-based technologies and systems for condition monitoring, structural health monitoring, and non-destructive testing;
  • Sensor-based technologies and systems for linear and non-linear assets, structures, and materials;
  • Diagnostic feature extraction for sensor-based technologies and systems for condition monitoring, structural health monitoring, and non-destructive testing.

Prof. Dr. Len Gelman
Prof. Dr. Shuncong Zhong
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 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-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

  • non-destructive testing technologies
  • structural health monitoring
  • diagnostic feature extraction

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

Related Special Issue

Published Papers (6 papers)

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Research

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21 pages, 9182 KB  
Article
Optimizing 3D Laser Scanning Parameters for Early-Stage Defect Detectability in Subgrade Condition Monitoring
by Mengmeng Liu, Gang Liu, Mingzhi Zhao, Xin Zhang, Kai Yang and Yang Chen
Sensors 2025, 25(23), 7174; https://doi.org/10.3390/s25237174 - 24 Nov 2025
Viewed by 719
Abstract
Terrestrial three-dimensional laser scanning, which plays a crucial role in engineering surveying for assessing the surface smoothness of highway embankments by providing a level of precision and continuous three-dimensional information that conventional measurement methods cannot achieve, is examined in this study through a [...] Read more.
Terrestrial three-dimensional laser scanning, which plays a crucial role in engineering surveying for assessing the surface smoothness of highway embankments by providing a level of precision and continuous three-dimensional information that conventional measurement methods cannot achieve, is examined in this study through a series of field experiments designed to determine how station location, including sampling interval, station distance, and scanning angle, influences point cloud density, spatial distribution, laser reflectivity, and surface reconstruction accuracy, and the results demonstrate that point cloud quantity decreases as sampling interval, station distance, and scanning angle increase, that the resolution of reconstructed surface undulations diminishes accordingly, that scanning angle has only a limited effect on reconstruction fidelity, that locating the instrument as close as feasible to the target area and adopting a sampling interval of 0.03 m achieves an effective balance between measurement accuracy and operational efficiency, and that optimizing parameter selection by analyzing elevation deviations at key points enhances both data quality and model precision, thereby confirming the suitability of the proposed approach for reliable highway embankment condition monitoring. Full article
(This article belongs to the Special Issue Sensor-Based Frequency, Time–Frequency and Higher-Order Signal Processing for Condition Monitoring, Structural Health Monitoring and Non-Destructive Testing (Second Edition))
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24 pages, 16067 KB  
Article
Unveiling Turbulence-Induced Stress Dynamics in Dented Pipe Using Acoustic Emission and Time–Frequency Analysis
by Syed Muhamad Firdaus, Mazian Mohammad, Abdul Rahim Othman and Mohd Faridz Mod Yunoh
Sensors 2025, 25(23), 7127; https://doi.org/10.3390/s25237127 - 21 Nov 2025
Viewed by 625
Abstract
Dents are among the most common deformation defects in buried transmission pipelines, significantly influencing structural integrity and internal flow behaviour. This study examines the occurrence of turbulence in dented pipe sections using time–frequency analysis of acoustic emission (AE) responses. The approach aims to [...] Read more.
Dents are among the most common deformation defects in buried transmission pipelines, significantly influencing structural integrity and internal flow behaviour. This study examines the occurrence of turbulence in dented pipe sections using time–frequency analysis of acoustic emission (AE) responses. The approach aims to overcome the challenge of obtaining meaningful information from AE signals during conventional dent inspections. By correlating AE spectral characteristics with flow-induced turbulence, the study provides insights into how mechanical deformation influences AE signal behaviour, contributing to an improved assessment of pipeline integrity. In this study, AE signals were captured during flow loop tests on healthy, 5%, 15%, and 30% dented pipe sections to evaluate the influence of dent severity on turbulence behaviour. Time–frequency domain analysis using the Morlet wavelet transform on the starting, middle, and end segments of AE signals revealed a progressive increase in signal energy with increasing dent depth, reaching a maximum of 2.54 × 10−08 μE2/Hz − 2.54 × 10−08 μE2/Hz for the end segment of AE signals under the 30% dented pipe condition. Complementary computational fluid dynamics (CFD) simulations were performed to compute velocity streamlines and corresponding Reynolds numbers for validating the turbulence detection results. A strong correlation between the CWT coefficient energy and Reynolds number, with R2 values of 0.9633, 0.9007, and 0.9052 for the starting, middle, and end signal segments, respectively, was observed. These findings demonstrate that AE time–frequency analysis offers a reliable diagnostic approach for identifying and characterising dent-induced turbulence in pipeline systems. Full article
(This article belongs to the Special Issue Sensor-Based Frequency, Time–Frequency and Higher-Order Signal Processing for Condition Monitoring, Structural Health Monitoring and Non-Destructive Testing (Second Edition))
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20 pages, 47793 KB  
Article
AI-Enhanced IoT System for Assessing Bridge Deflection in Drive-By Conditions
by Leonardo Iacussi, Paolo Chiariotti and Alfredo Cigada
Sensors 2025, 25(1), 158; https://doi.org/10.3390/s25010158 - 30 Dec 2024
Cited by 4 | Viewed by 4259
Abstract
The increasing traffic on roads poses a significant challenge to the structural integrity of bridges and viaducts. Indirect structural monitoring offers a cost-effective and efficient solution for monitoring multiple infrastructures. The presented work aims to explore new sensing strategies based on digital MEMS [...] Read more.
The increasing traffic on roads poses a significant challenge to the structural integrity of bridges and viaducts. Indirect structural monitoring offers a cost-effective and efficient solution for monitoring multiple infrastructures. The presented work aims to explore new sensing strategies based on digital MEMS sensors integrated into an intelligent IoT infrastructure to predict the bridge deflection behaviour for indirect Bridge Structural Health Monitoring purposes. An experimental setup comprising a bridge model and vehicle equipped with a smart sensing node has been used to generate the dataset. Various models for bridge deflection estimation are deployed on the sensorized vehicle, exploiting edge AI capabilities of smart sensors. This study shows the potential of leveraging data-driven technologies to enhance the performance of low-cost sensors. Additionally, it demonstrates the viability of assessing static deflection shapes of bridges through indirect measurements on board vehicles, underlining the potential of this approach to make SHM more cost-effective and scalable. Full article
(This article belongs to the Special Issue Sensor-Based Frequency, Time–Frequency and Higher-Order Signal Processing for Condition Monitoring, Structural Health Monitoring and Non-Destructive Testing (Second Edition))
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15 pages, 5974 KB  
Article
Structural Damage Early Warning Method of Quayside Container Crane Based on Fuzzy Entropy Ratio Variation Deviation
by Jiahui Liu, Jian Zhao, Dong Zhao and Xianrong Qin
Sensors 2024, 24(23), 7575; https://doi.org/10.3390/s24237575 - 27 Nov 2024
Cited by 2 | Viewed by 1300
Abstract
Real-time monitoring and early warning of structures are essential for assessing structural health and ensuring safety maintenance. To improve the timeliness of early warnings for structural abnormal states in quayside container cranes (QCCs) with incomplete damage data, a structural abnormal state early warning [...] Read more.
Real-time monitoring and early warning of structures are essential for assessing structural health and ensuring safety maintenance. To improve the timeliness of early warnings for structural abnormal states in quayside container cranes (QCCs) with incomplete damage data, a structural abnormal state early warning method based on fuzzy entropy ratio variation deviation (FERVD) is proposed. First, monitoring data are subjected to dual-tree complex wavelet transform (DTCWT). The adaptive frequency bands obtained from the decomposition, combined with fuzzy entropy (FE), are used to extract response signal features and construct the FERVD warning indicator. Based on this indicator, dynamic thresholds for early warning are established to differentiate between structural health states and various damage conditions. Secondly, a finite element model of structure for QCCs is developed. By simulating damage at various locations and severities through the stiffness reduction of different elements, a comprehensive structural simulation monitoring dataset is generated. The efficacy of the proposed early warning method is validated through numerical experiments and engineering case studies. The numerical results demonstrate that the proposed method effectively distinguishes between different damage conditions and provides timely warnings for various damage states. Furthermore, engineering case analysis shows that when the structure is in a healthy state, the FERVD values at different monitoring points fluctuate within the threshold range, indicating the applicability of the proposed method in the structural health monitoring (SHM) of QCCs. Full article
(This article belongs to the Special Issue Sensor-Based Frequency, Time–Frequency and Higher-Order Signal Processing for Condition Monitoring, Structural Health Monitoring and Non-Destructive Testing (Second Edition))
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19 pages, 3445 KB  
Article
A Novel Diagnostic Feature for a Wind Turbine Imbalance Under Variable Speed Conditions
by Amir R. Askari, Len Gelman, Russell King, Daryl Hickey and Andrew D. Ball
Sensors 2024, 24(21), 7073; https://doi.org/10.3390/s24217073 - 2 Nov 2024
Cited by 4 | Viewed by 3633
Abstract
Dependency between the conventional imbalance diagnostic feature and the shaft rotational speed makes imbalance diagnosis challenging for variable-speed machines. This paper focuses on an investigation of this dependency and on a proposal for a novel imbalance diagnostic feature and a novel simplified version [...] Read more.
Dependency between the conventional imbalance diagnostic feature and the shaft rotational speed makes imbalance diagnosis challenging for variable-speed machines. This paper focuses on an investigation of this dependency and on a proposal for a novel imbalance diagnostic feature and a novel simplified version for this feature, which are independent of shaft rotational speed. An equivalent mass–spring–damper system is investigated to find a closed-form expression describing this dependency. By normalizing the conventional imbalance diagnostic feature by the obtained dependency, a diagnostic feature is proposed. By conducting comprehensive experimental trials with a wind turbine with a permissible imbalance, it is justified that the proposed simplified version of imbalance diagnostic feature is speed-invariant. Full article
(This article belongs to the Special Issue Sensor-Based Frequency, Time–Frequency and Higher-Order Signal Processing for Condition Monitoring, Structural Health Monitoring and Non-Destructive Testing (Second Edition))
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Review

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32 pages, 2534 KB  
Review
Recent Advances in Non-Destructive Testing Technology for Coated Steel Structure Welds
by Zhiyong Ji, Dongsheng Xu, Honglun Wang, Junzhe Chen and Yunwei Fu
Sensors 2025, 25(22), 6923; https://doi.org/10.3390/s25226923 - 13 Nov 2025
Cited by 1 | Viewed by 2357
Abstract
The fabrication of a steel structure facility in the aerospace sector was executed through the implementation of welding techniques. In order to reduce the effects of environmental corrosion and extend its service life, it is typically coated with a protective layer. Nevertheless, conventional [...] Read more.
The fabrication of a steel structure facility in the aerospace sector was executed through the implementation of welding techniques. In order to reduce the effects of environmental corrosion and extend its service life, it is typically coated with a protective layer. Nevertheless, conventional non-destructive testing (NDT) techniques generally necessitate preliminary procedures, such as coating removal and surface grinding, prior to inspection, leading to elevated costs and diminished efficiency. Consequently, the investigation into NDT methodologies for welds encased under coatings is of considerable practical significance. The objective of this paper is to comprehensively review and thoroughly analyze the latest research progress in NDT techniques for detecting defects in coated steel welds, seeking feasible approaches for achieving NDT on coated steel structures. Firstly, the paper examines the hazards of common weld defects and the challenges coatings pose to NDT operations. The text then proceeds to expound upon the principles, research advancements, and application scenarios of multiple NDT methods currently available for detecting defects beneath coatings. A comparative summary of these methods is provided, focusing on detection capabilities, coating penetration abilities, key advantages, and limitations. In conclusion, the paper provides insights into future development trends. Full article
(This article belongs to the Special Issue Sensor-Based Frequency, Time–Frequency and Higher-Order Signal Processing for Condition Monitoring, Structural Health Monitoring and Non-Destructive Testing (Second Edition))
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