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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: 25 October 2025 | Viewed by 5532

Special Issue Editors


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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

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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 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

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

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Related Special Issue

Published Papers (3 papers)

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Research

20 pages, 47793 KiB  
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 1 | Viewed by 1408
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
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15 pages, 5974 KiB  
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
Viewed by 665
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
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19 pages, 3445 KiB  
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 1 | Viewed by 2617
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
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