Topic Editors

Department of Robotics and Mechatronics, AGH University of Krakow, 30-059 Kraków, Poland
Prof. Dr. Lei Qiu
Research Center of Structural Health Monitoring and Prognosis, State Key Lab of Mechanics and Control of Mechanical Structures, Nanjing University of Aeronautics and Astronautics, 29 YuDao Street, Nanjing, China
School of Civil Aviation, Northwestern Polytechnical University, Xi'an 710072, China
Department of Robotics and Mechatronics, AGH University of Krakow, 30-962 Krakow, Poland

Structural Health Monitoring and Non-destructive Testing for Large-Scale Structures (2nd Edition)

Abstract submission deadline
31 August 2024
Manuscript submission deadline
31 December 2024
Viewed by
3924

Topic Information

Dear Colleagues,

This Topic is a continuation of the previous successful Topic “Structural Health Monitoring and Non-destructive Testing for Large-Scale Structures”, which was closed on 31 May 2023, and in which 44 papers were published. Structural health monitoring (SHM) and non-destructive testing (NDT) are of significant importance to civil, mechanical, aerospace, and offshore structures. Nowadays, we can find SHM and NDT applications being used on various structures with very different requirements. The SHM-NDT field involves a wide range of transdisciplinary areas, including smart materials, embedded sensors and actuators, damage diagnosis and prognosis, signal and image processing, wireless sensor networks, data interpretation, machine learning, data fusion, energy harvesting, etc. Since the 1970s, there has been a large and increasing volume of research on SHM and NDT; a great deal of effort has been focused on developing cost-effective, automatic, and reliable damage detection technologies. However, few industrial and commercial applications can be found in the literature. The practical implementation of strategies for the detection of structural damage to real structures outside of laboratory conditions is always one of the most demanding tasks for engineers. One reason for the rare transfer of research outcomes into industrial practice is that most of the methods that have been developed have been tested on simple beam and plate structures in the laboratory, while many practical problems only manifest themselves in complex structures. Another reason is the influence of environmental and operational variations (EOVs) on damage-sensitive features. Thus, for the successful development of SHM and NDT for large structures, techniques should be enhanced to toward having the capability of dealing with the influence of EOVs. In addition, signal/data processing plays an important role in the implementation of SHM and NDT technologies. The processing and interpretation of the massive amount of data generated through the long-term monitoring of large and complex structures (e.g., bridges, buildings, ships, aircrafts, wind turbines, pipes, etc.) has become an emerging challenge that needs to be addressed by the community. This topical collection brings together the most established as well as newly emerging SHM and NDT techniques that can be used for the detection and evaluation of defects and damage development in large-scale or full-scale structures. We cordially invite you to submit your cutting-edge research for consideration. Suitable topics include the following:

  • SHM and NDT for aerospace, civil, mechanical, and offshore infrastructures
  • Global monitoring of large structures
  • Large-area monitoring for a part/region of a larger structure
  • Localised monitoring and damage detection
  • SHM and NDT for composite, steel, and concrete structures
  • SHM and NDT of bridges, buildings, ships, aircrafts, wind turbines, pipes, and industrial machines
  • Novel algorithms for SHM and NDT
  • Strategies for the removal of EOVs for SHM and NDT
  • Advanced signal processing for SHM and NDT
  • Artificial intelligence and machine learning for SHM and NDT
  • Time series analysis and statistical approaches for SHM and NDT
  • Damage detection, diagnosis, and prognosis

Dr. Phong B. Dao
Prof. Dr. Lei Qiu
Dr. Liang Yu
Prof. Dr. Tadeusz Uhl
Topic Editors

Keywords

  • structural health monitoring
  • non-destructive testing
  • condition monitoring
  • damage detection
  • remaining useful life prediction
  • smart materials and structures
  • embedded sensors and actuators
  • composite structures
  • steel structures
  • reinforced concrete structures
  • ultrasonic testing
  • laser vibrometry
  • infrared thermography
  • terahertz testing
  • thermal and hyperspectral imaging

Participating Journals

Journal Name Impact Factor CiteScore Launched Year First Decision (median) APC
Aerospace
aerospace
2.6 3.0 2014 22.3 Days CHF 2400 Submit
Applied Sciences
applsci
2.7 4.5 2011 16.9 Days CHF 2400 Submit
Energies
energies
3.2 5.5 2008 16.1 Days CHF 2600 Submit
Materials
materials
3.4 5.2 2008 13.9 Days CHF 2600 Submit
Sensors
sensors
3.9 6.8 2001 17 Days CHF 2600 Submit

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Published Papers (5 papers)

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14 pages, 2803 KiB  
Article
Vibration Signal Evaluation Based on K-Means Clustering as a Pre-Stage of Operational Modal Analysis for Structural Health Monitoring of Rotating Machines
Energies 2023, 16(23), 7848; https://doi.org/10.3390/en16237848 - 30 Nov 2023
Viewed by 582
Abstract
Rotating machines are key components in energy generation processes, and faults can lead to shutdowns or catastrophes encompassing economic and social losses. Structural Health Monitoring (SHM) of structures in operation is successfully performed via Operational Modal Analysis (OMA), which has advantages over traditional [...] Read more.
Rotating machines are key components in energy generation processes, and faults can lead to shutdowns or catastrophes encompassing economic and social losses. Structural Health Monitoring (SHM) of structures in operation is successfully performed via Operational Modal Analysis (OMA), which has advantages over traditional methods. In OMA, white noise inputs lead to the accurate extraction of modal parameters without taking the system out of operation. However, this excitation condition is not easy to attain for rotating machines used in power generation, and OMA can provide inaccurate information. This research investigates the applicability of machine learning as a pre-stage of OMA to differentiate adequate from inadequate excitations and prevent inaccurate extraction of modal parameters. Data from a rotor system was collected under different conditions and OMA was applied. In a training stage, measurements were characterized by statistical features and K-means was used to determine which features provided information about the excitation condition, that is, which excitation was adequate to extract the rotor’s modal parameters via OMA. In a testing stage, data were successfully classified as adequate or not adequate for OMA, achieving 100% accuracy and revealing the technique’s potential to support SHM of rotating machines. The technique is extendable to other monitoring systems based on OMA. Full article
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23 pages, 7397 KiB  
Article
Development of a Simulation Model for Digital Twin of an Oscillating Water Column Wave Power Generator Structure with Ocean Environmental Effect
Sensors 2023, 23(23), 9472; https://doi.org/10.3390/s23239472 - 28 Nov 2023
Cited by 1 | Viewed by 461
Abstract
This research article focuses on developing a baseline digital twin model for a wave power generator structure located in Yongsu-ri, Jeju-do, South Korea. First, this study performs a cause analysis on the discrepancy of the dynamic properties from the real structure and an [...] Read more.
This research article focuses on developing a baseline digital twin model for a wave power generator structure located in Yongsu-ri, Jeju-do, South Korea. First, this study performs a cause analysis on the discrepancy of the dynamic properties from the real structure and an existing simulation model and finds the necessity of modeling the non-structural masses and the environmental factors. The large amounts of the ballast are modeled in the finite element model to enhance the accuracy of the digital twin. Considering the influence of environmental factors such as tide level and wave direction, the added mass effect of structural members, one of the hydrodynamic effects, depending on the change of the ocean environments is calculated based on the rule of Det Norske Veritas and applied. The results indicate that non-structural mass components significantly impact the dynamic characteristics of the structure. Additionally, environmental factors have a greater effect on the dynamic behavior of the box-type structure compared to lightweight offshore structures. Full article
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15 pages, 2100 KiB  
Article
Lamb Wave-Based Structural Damage Detection: A Time Series Approach Using Cointegration
Materials 2023, 16(21), 6894; https://doi.org/10.3390/ma16216894 - 27 Oct 2023
Viewed by 490
Abstract
Although Lamb waves have found extensive use in structural damage detection, their practical applications remain limited. This limitation primarily arises from the intricate nature of Lamb wave propagation modes and the effect of temperature variations. Therefore, rather than directly inspecting and interpreting Lamb [...] Read more.
Although Lamb waves have found extensive use in structural damage detection, their practical applications remain limited. This limitation primarily arises from the intricate nature of Lamb wave propagation modes and the effect of temperature variations. Therefore, rather than directly inspecting and interpreting Lamb wave responses for insights into the structural health, this study proposes a novel approach, based on a two-step cointegration-based computation procedure, for structural damage evaluation using Lamb wave data represented as time series that exhibit some common trends. The first step involves the composition of Lamb wave series sharing a common upward (or downward) trend of temperature. In the second step, the cointegration analysis is applied for each group of Lamb wave series, which represents a certain condition of damage. So, a cointegration analysis model of Lamb wave series is created for each damage condition. The geometrical and statistical features of Lamb wave series and cointegration residual series are used for detecting and distinguishing damage conditions. These features include the shape, peak-to-peak amplitude, and variance of the series. The validity of this method is confirmed through its application to the Lamb wave data collected from both undamaged and damaged aluminium plates subjected to temperature fluctuations. The proposed approach can find its application not only in Lamb wave-based damage detection, but also in other structural health monitoring (SHM) systems where the data can be arranged in the form of sharing common environmental and/or operational trends. Full article
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18 pages, 8284 KiB  
Article
An Online Fatigue Damage Evaluation Method for Gas Turbine Hot Components
Energies 2023, 16(19), 6785; https://doi.org/10.3390/en16196785 - 23 Sep 2023
Cited by 1 | Viewed by 748
Abstract
The failure of gas turbines’ hot components due to fatigue significantly affects their efficient and stable operation. Conducting online damage assessment of components subjected to complex cyclic loads based on the working conditions of gas turbines can provide real-time reflection of component fatigue [...] Read more.
The failure of gas turbines’ hot components due to fatigue significantly affects their efficient and stable operation. Conducting online damage assessment of components subjected to complex cyclic loads based on the working conditions of gas turbines can provide real-time reflection of component fatigue damage and achieve the purpose of predictive maintenance. In this study, we propose an online cycle counting method that considers temperature fluctuations during the cycle process. Our method is based on the four-point online rainflow counting method by coupling the counting variable with time, introducing the concept of the duration time for full cycles and half cycles, and incorporating a characteristic temperature that better represents the temperature information during the cycle process. With reference to the characteristic temperature, our proposed method comprehensively considers the form and parameters of subsequent life assessment models. This paper provides a detailed explanation of the proposed method and applies it to the fatigue damage assessment of turbine vanes in a micro gas turbine, thereby verifying its accuracy and applicability. Full article
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16 pages, 4335 KiB  
Article
Bibliometric Analysis of Engine Vibration Detection
Aerospace 2023, 10(9), 819; https://doi.org/10.3390/aerospace10090819 - 20 Sep 2023
Viewed by 763
Abstract
After many years of development, the technology of analyzing the working condition of power units based on vibration signals has received relatively stable applications, but the accuracy and the degree of automation and intelligence for fault diagnosis are still inadequate due to the [...] Read more.
After many years of development, the technology of analyzing the working condition of power units based on vibration signals has received relatively stable applications, but the accuracy and the degree of automation and intelligence for fault diagnosis are still inadequate due to the limitations in the ongoing development of key technologies. With the development of big data and artificial intelligence technology, the involvement of new technologies will be an important boost to the development of this field. In this study, in order to support subsequent research, bibliometrics is used as a tool to sort the development of the technology in this field at the macro level. At the micro level, key publications in the literature are studied to better understand the development status at the technical level and prepare for the selection of entry points to facilitate in-depth innovation in the future. Full article
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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: State of the art of wind turbine blade condition monitoring by the approach of vibroacoustic analysis
Authors: Wenxian Yang
Affiliation: University of Huddersfield

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