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Wang B, Lee TL, Qin Y. Advances in Smart Materials and Structures. MATERIALS (BASEL, SWITZERLAND) 2023; 16:7206. [PMID: 38005134 PMCID: PMC10673296 DOI: 10.3390/ma16227206] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/04/2023] [Accepted: 11/16/2023] [Indexed: 11/26/2023]
Abstract
Smart materials and structures are capable of active or passive changes in terms of shapes (geometries), properties, and mechanical or electromagnetic responses, in reaction to an external stimulus, such as light, temperature, stress, moisture, and electric or magnetic fields [...].
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Affiliation(s)
- Bing Wang
- Fujian Provincial Key Laboratory of Terahertz Functional Devices and Intelligent Sensing, School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou 350108, China
| | - Tung Lik Lee
- ISIS Neutron and Muon Source, STFC Rutherford Appleton Laboratory, Harwell Campus, Didcot OX11 0QX, UK;
| | - Yang Qin
- School of Civil Aviation, Northwestern Polytechnical University, Xi’an 710072, China;
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2
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Yang Z, Yang H, Tian T, Deng D, Hu M, Ma J, Gao D, Zhang J, Ma S, Yang L, Xu H, Wu Z. A review in guided-ultrasonic-wave-based structural health monitoring: From fundamental theory to machine learning techniques. ULTRASONICS 2023; 133:107014. [PMID: 37178485 DOI: 10.1016/j.ultras.2023.107014] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/04/2023] [Revised: 04/10/2023] [Accepted: 04/11/2023] [Indexed: 05/15/2023]
Abstract
The development of structural health monitoring (SHM) techniques is of great importance to improve the structural efficiency and safety. With advantages of long propagation distances, high damage sensitivity, and economic feasibility, guided-ultrasonic-wave-based SHM is recognized as one of the most promising technologies for large-scale engineering structures. However, the propagation characteristics of guided ultrasonic waves in in-service engineering structures are highly complex, which results in difficulties in developing precise and efficient signal feature mining methods. The damage identification efficiency and reliability of existing guided ultrasonic wave methods cannot meet engineering requirements. With the development of machine learning (ML), numerous researchers have proposed improved ML methods that can be incorporated into guided ultrasonic wave diagnostic techniques for SHM of actual engineering structures. To highlight their contributions, this paper provides a state-of-the-art overview of the guided-wave-based SHM techniques enabled by ML methods. Accordingly, multiple stages required for ML-based guided ultrasonic wave techniques are discussed, including guided ultrasonic wave propagation modeling, guided ultrasonic wave data acquisition, wave signal pre-processing, guided wave data-based ML modeling, and physics-based ML modeling. By placing ML methods in the context of the guided-wave-based SHM for actual engineering structures, this paper also provides insights into future prospects and research strategies.
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Affiliation(s)
- Zhengyan Yang
- College of Transportation Engineering, Dalian Maritime University, Dalian 116026, China
| | - Hongjuan Yang
- State Key Laboratory of Structural Analysis for Industrial Equipment, Dalian University of Technology, Dalian 116024, China
| | - Tong Tian
- State Key Laboratory of Structural Analysis for Industrial Equipment, Dalian University of Technology, Dalian 116024, China
| | - Deshuang Deng
- State Key Laboratory of Structural Analysis for Industrial Equipment, Dalian University of Technology, Dalian 116024, China
| | - Mutian Hu
- School of Automation, Guangxi University of Science and Technology, Liuzhou 545000, China
| | - Jitong Ma
- College of Information Science and Technology, Dalian Maritime University, Dalian 116026, China
| | - Dongyue Gao
- College of Textile Science and Engineering, Jiangnan University, Wuxi 214122, China
| | - Jiaqi Zhang
- State Key Laboratory of Structural Analysis for Industrial Equipment, Dalian University of Technology, Dalian 116024, China
| | - Shuyi Ma
- Dalian University of Science and Technology, Dalian 116052, China
| | - Lei Yang
- State Key Laboratory of Structural Analysis for Industrial Equipment, Dalian University of Technology, Dalian 116024, China
| | - Hao Xu
- State Key Laboratory of Structural Analysis for Industrial Equipment, Dalian University of Technology, Dalian 116024, China
| | - Zhanjun Wu
- State Key Laboratory of Structural Analysis for Industrial Equipment, Dalian University of Technology, Dalian 116024, China.
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Hassani S, Dackermann U. A Systematic Review of Advanced Sensor Technologies for Non-Destructive Testing and Structural Health Monitoring. SENSORS (BASEL, SWITZERLAND) 2023; 23:s23042204. [PMID: 36850802 PMCID: PMC9965987 DOI: 10.3390/s23042204] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Revised: 01/26/2023] [Accepted: 02/13/2023] [Indexed: 05/27/2023]
Abstract
This paper reviews recent advances in sensor technologies for non-destructive testing (NDT) and structural health monitoring (SHM) of civil structures. The article is motivated by the rapid developments in sensor technologies and data analytics leading to ever-advancing systems for assessing and monitoring structures. Conventional and advanced sensor technologies are systematically reviewed and evaluated in the context of providing input parameters for NDT and SHM systems and for their suitability to determine the health state of structures. The presented sensing technologies and monitoring systems are selected based on their capabilities, reliability, maturity, affordability, popularity, ease of use, resilience, and innovation. A significant focus is placed on evaluating the selected technologies and associated data analytics, highlighting limitations, advantages, and disadvantages. The paper presents sensing techniques such as fiber optics, laser vibrometry, acoustic emission, ultrasonics, thermography, drones, microelectromechanical systems (MEMS), magnetostrictive sensors, and next-generation technologies.
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Perfetto D, Sharif Khodaei Z, De Luca A, Aliabadi MH, Caputo F. Experiments and modelling of ultrasonic waves in composite plates under varying temperature. ULTRASONICS 2022; 126:106820. [PMID: 35961156 DOI: 10.1016/j.ultras.2022.106820] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/17/2021] [Revised: 08/02/2022] [Accepted: 08/03/2022] [Indexed: 06/15/2023]
Abstract
Guided wave (GW) structural health monitoring (SHM) systems offer an attractive solution as an in-situ quasi real-time assessment of structural damage, but their sensitivity and efficiency may be impaired under varied environmental and operational conditions. Thus, virtual tests, such as that based on the Finite Element (FE) method, represent a valid approach for simulating and investigating SHM systems, enabling a substantial reduction in experimental campaigns. In this work, GW propagation characteristics in a carbon fibre-reinforced composite plate are studied under a varying temperature condition, representative of the aeronautics application. At first, GW SHM system was physically tested at room temperature (20°C), and the results were used to calibrate and assess the proposed FE modelling approaches, characterised by different element types and mesh sizes. A temperature independent averaged time compensation factor is proposed to mitigate the numerical data dependency on excitation frequency and propagation angle. Two temperature variations (from 20°C to -50°C, and 20°C to 65°C) were experimentally and numerically considered to investigate the effect of varying temperature on the GW. For all test cases, the compensated numerical data was compared to the experimental results, and discussed in terms of dispersion curves, focusing on the zero-order symmetric, S0, and antisymmetric, A0, modes. Results show that both 2D and 3D FE approaches can accurately predict the changes in GW due to varying temperature, with the group velocity of the A0 mode being less sensitive to temperature variations.
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Affiliation(s)
- Donato Perfetto
- Department of Engineering, University of Campania "L. Vanvitelli", 81031, Via Roma 29, Aversa, Italy.
| | - Zahra Sharif Khodaei
- Department of Aeronautics, Imperial College London, SW7 2AZ, Exhibition Road, London, UK
| | - Alessandro De Luca
- Department of Engineering, University of Campania "L. Vanvitelli", 81031, Via Roma 29, Aversa, Italy
| | - M H Aliabadi
- Department of Aeronautics, Imperial College London, SW7 2AZ, Exhibition Road, London, UK
| | - Francesco Caputo
- Department of Engineering, University of Campania "L. Vanvitelli", 81031, Via Roma 29, Aversa, Italy
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Scott MJ, Verhagen WJC, Bieber MT, Marzocca P. A Systematic Literature Review of Predictive Maintenance for Defence Fixed-Wing Aircraft Sustainment and Operations. SENSORS (BASEL, SWITZERLAND) 2022; 22:s22187070. [PMID: 36146419 PMCID: PMC9502349 DOI: 10.3390/s22187070] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/20/2022] [Revised: 09/10/2022] [Accepted: 09/15/2022] [Indexed: 05/27/2023]
Abstract
In recent decades, the increased use of sensor technologies, as well as the increase in digitalisation of aircraft sustainment and operations, have enabled capabilities to detect, diagnose, and predict the health of aircraft structures, systems, and components. Predictive maintenance and closely related concepts, such as prognostics and health management (PHM) have attracted increasing attention from a research perspective, encompassing a growing range of original research papers as well as review papers. When considering the latter, several limitations remain, including a lack of research methodology definition, and a lack of review papers on predictive maintenance which focus on military applications within a defence context. This review paper aims to address these gaps by providing a systematic two-stage review of predictive maintenance focused on a defence domain context, with particular focus on the operations and sustainment of fixed-wing defence aircraft. While defence aircraft share similarities with civil aviation platforms, defence aircraft exhibit significant variation in operations and environment and have different performance objectives and constraints. The review utilises a systematic methodology incorporating bibliometric analysis of the considered domain, as well as text processing and clustering of a set of aligned review papers to position the core topics for subsequent discussion. This discussion highlights state-of-the-art applications and associated success factors in predictive maintenance and decision support, followed by an identification of practical and research challenges. The scope is primarily confined to fixed-wing defence aircraft, including legacy and emerging aircraft platforms. It highlights that challenges in predictive maintenance and PHM for researchers and practitioners alike do not necessarily revolve solely on what can be monitored, but also covers how robust decisions can be made with the quality of data available.
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Affiliation(s)
- Michael J. Scott
- School of Engineering (Aerospace Engineering and Aviation), Royal Melbourne Institute of Technology (RMIT), Melbourne, VIC 3000, Australia
| | - Wim J. C. Verhagen
- School of Engineering (Aerospace Engineering and Aviation), Royal Melbourne Institute of Technology (RMIT), Melbourne, VIC 3000, Australia
| | - Marie T. Bieber
- Air Transport & Operations, Faculty of Aerospace Engineering, Delft University of Technology, 2629 HS Delft, The Netherlands
| | - Pier Marzocca
- School of Engineering (Aerospace Engineering and Aviation), Royal Melbourne Institute of Technology (RMIT), Melbourne, VIC 3000, Australia
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Finite Element Modeling Approaches, Experimentally Assessed, for the Simulation of Guided Wave Propagation in Composites. SUSTAINABILITY 2022. [DOI: 10.3390/su14116924] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Today, structural health monitoring (SHM) systems based on guided wave (GW) propagation represent an effective methodology for understating the structural integrity of primary and secondary structures, also made of composite materials. However, the sensitivity to damage detection promoted by these systems can be altered by such factors as the geometry of the monitored parts, as well as the environmental and operational conditions (EOCs). Experimental investigations are fundamental but require a long time period and are costly, especially for tests in real-life scenarios. Experimentally validated simulations can help designers to improve SHM effectiveness due to the possibility of further broadening study on the different geometries, load cases, and material types with less effort. From this point of view, this paper presents two finite element (FE) modeling approaches for the simulation of GW propagation in composite panels. The case study consists of a flat and a curved composite panel. The two approaches herein investigated are based on implicit and explicit finite element analysis (FEA) formulations. The comparison of the predicted measures against the experimental dataset allowed the assessment of the levels of accuracy provided by both modeling approaches with respect to the dispersion curves. Furthermore, to assess the different curvature sensitivities of the proposed numerical and experimental approaches, the extracted dispersion curves for both flat and curved panels were compared.
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Heesch M, Dziendzikowski M, Mendrok K, Dworakowski Z. Diagnostic-Quality Guided Wave Signals Synthesized Using Generative Adversarial Neural Networks. SENSORS (BASEL, SWITZERLAND) 2022; 22:3848. [PMID: 35632256 PMCID: PMC9143698 DOI: 10.3390/s22103848] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Revised: 05/13/2022] [Accepted: 05/18/2022] [Indexed: 06/15/2023]
Abstract
Guided waves are a potent tool in structural health monitoring, with promising machine learning algorithm applications due to the complexity of their signals. However, these algorithms usually require copious amounts of data to be trained. Collecting the correct amount and distribution of data is costly and time-consuming, and sometimes even borderline impossible due to the necessity of introducing damage to vital machinery to collect signals for various damaged scenarios. This data scarcity problem is not unique to guided waves or structural health monitoring, and has been partly addressed in the field of computer vision using generative adversarial neural networks. These networks generate synthetic data samples based on the distribution of the data they were trained on. Though there are multiple researched methods for simulating guided wave signals, the problem is not yet solved. This work presents a generative adversarial network architecture for guided waves generation and showcases its capabilities when working with a series of pitch-catch experiments from the OpenGuidedWaves database. The network correctly generates random signals and can accurately reconstruct signals it has not seen during training. The potential of synthetic data to be used for training other algorithms was confirmed in a simple damage detection scenario, with the classifiers trained exclusively on synthetic data and evaluated on real signals. As a side effect of the signal reconstruction process, the network can also compress the signals by 98.44% while retaining the damage index information they carry.
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Affiliation(s)
- Mateusz Heesch
- Department of Robotics and Mechatronics, AGH University of Science and Technology, Al. A. Mickiewicza 30, 30-059 Krakow, Poland; (M.H.); (K.M.)
| | - Michał Dziendzikowski
- Airworthiness Division, Air Force Institute of Technology, ul. Ks. Boleslawa 6, 01-496 Warsaw, Poland;
| | - Krzysztof Mendrok
- Department of Robotics and Mechatronics, AGH University of Science and Technology, Al. A. Mickiewicza 30, 30-059 Krakow, Poland; (M.H.); (K.M.)
| | - Ziemowit Dworakowski
- Department of Robotics and Mechatronics, AGH University of Science and Technology, Al. A. Mickiewicza 30, 30-059 Krakow, Poland; (M.H.); (K.M.)
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De Luca A, Perfetto D, Lamanna G, Aversano A, Caputo F. Numerical Investigation on Guided Waves Dispersion and Scattering Phenomena in Stiffened Panels. MATERIALS 2021; 15:ma15010074. [PMID: 35009223 PMCID: PMC8746058 DOI: 10.3390/ma15010074] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/22/2021] [Revised: 12/20/2021] [Accepted: 12/21/2021] [Indexed: 11/25/2022]
Abstract
The aim of this work is to propose a numerical methodology based on the finite element (FE) method to investigate the dispersive behavior of guided waves transmitted, converted, and reflected by reinforced aluminum and composite structures, highlighting their differences. The dispersion curves of such modes can help designers in improving the damage detection sensitivity of Lamb wave based structural health monitoring (SHM) systems. A preliminary phase has been carried out to assess the reliability of the modelling technique. The accuracy of the results has been demonstrated for aluminum and composite flat panels by comparing them against experimental tests and semi-analytical data, respectively. Since the good agreement, the FE method has been used to analyze the phenomena of dispersion, scattering, and mode conversion in aluminum and composite panels characterized by a structural discontinuity, as a stiffener. The research activity allowed emphasizing modes conversion at the stiffener, offering new observations with respect to state of the art. Converted modes propagate with a slightly slower speed than the incident ones. Reflected waves, instead, have been found to travel with the same velocity of the incident ones. Moreover, waves reflected in the composite stiffened plate appeared different from those that occurred in the aluminum one for the aspects herein discussed.
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