Article
Engineering, Multidisciplinary
Ahmad Amer, Fotis P. Kopsaftopoulos
Summary: A novel statistical framework for active-sensing SHM based on ultrasonic guided waves is proposed in this study, with three methods and corresponding statistical quantities experimentally evaluated for damage detection, showing increased sensitivity and robustness compared to conventional approaches, as well as better tracking capability of damage evolution.
STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL
(2022)
Article
Construction & Building Technology
Khizar Hayat, Saqib Mehboob, Qadir Bux alias Imran Latif Qureshi, Afsar Ali, Diyar Matiullah, Diyar Khan, Muhammad Altaf
Summary: This paper introduces a multistep damage identification process using a combination of multi-damage sensitivity feature and MATLAB programming for structural health monitoring of buildings. The algorithm utilizes accelerometers and a statistical subspace-based damage detection test to monitor changes in data and identify potential damage. The algorithm also includes a step to localize damage at the story level using jerk energy of acceleration. The effectiveness of the algorithm was demonstrated through laboratory tests on a steel shear frame model, successfully detecting induced damages.
Article
Engineering, Civil
Anne S. Kiremidjian
Summary: This paper provides an overview of damage diagnosis algorithms that utilize vibration signals obtained from structures over the past two decades. The focus of the paper is on algorithms that can be used to identify structural damage following extreme events, such as earthquakes, in a timely manner. The algorithms utilize measurements from accelerometers and gyroscopes to identify and classify damage. Example algorithms include those based on ARMA, wavelet energies, and rotation models. The algorithms are demonstrated using data from test structures and laboratory tests. The paper concludes by highlighting the need for further research and development for these algorithms to be viable in practice.
SMART STRUCTURES AND SYSTEMS
(2022)
Article
Chemistry, Analytical
Emrah Erduran, Frida Kristin Ulla, Lone Naess
Summary: A new framework for long-term monitoring of bridges is proposed using vibration-based damage indicators with physical correlation to effectively detect and locate damage levels on a simulated railway bridge. The framework demonstrates a clear picture of damage initiation and development over time, and successfully identifies the location of simulated damage even under high noise levels.
Article
Engineering, Civil
Mohammad Hassan Daneshvar, Alireza Gharighoran, Seyed Alireza Zareei, Abbas Karamodin
Summary: This study proposes hybrid distance methods to address the challenge of applying high-dimensional damage-sensitive features in data-driven methods, achieving accurate localization and quantification of structural damage. The two hybrid distance methods effectively reduce feature sample size, improve damage detection performance, and accurately locate and quantify damage under varying environmental and operational conditions.
JOURNAL OF CIVIL STRUCTURAL HEALTH MONITORING
(2021)
Article
Engineering, Multidisciplinary
X. Y. Li, S. J. Lin, S. S. Law, Y. Z. Lin, J. F. Lin
Summary: The article discusses the limitations of existing algorithms and damage indices for damage diagnosis, highlighting the potential benefits of combining different evaluation methodologies. By analyzing various test results, it is possible to enhance the final structural damage information effectively.
STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL
(2021)
Article
Engineering, Multidisciplinary
Andeas Panagiotopoulos, Tcherniak Dmitri, Fassois D. Spilios
Summary: This study focuses on detecting damage on the blade of an operating Vestas V27 wind turbine using a single vibration response sensor under varying environmental and operating conditions. Three different lengths of damage scenarios are examined, and the performance of robust vibration-based Statistical Time Series type methods is explored. The results show that single-sensor-based detection is feasible using the U-PCA-MM-AR method, with a True Positive Rate of 100% and a False Positive Rate of 4%, 1%, and 0% for the 15, 30, and 45 cm damage scenarios, respectively. This performance is comparable to that of the 8-sensor-based method.
STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL
(2023)
Article
Engineering, Multidisciplinary
Shabbir Ahmed, Fotis Kopsaftopoulos
Summary: In this article, a statistical damage detection and identification framework for metallic and composite materials based on acousto-ultrasonic guided wave-based structural health monitoring is proposed. The framework utilizes stochastic stationary time-series autoregressive (AR) models to model ultrasonic wave propagation and enables damage diagnosis. Experimental tests demonstrate the effectiveness and robustness of the proposed framework.
STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL
(2023)
Review
Chemistry, Analytical
Kareem Eltouny, Mohamed Gomaa, Xiao Liang
Summary: Structural damage detection using unsupervised learning methods has gained significant attention in the structural health monitoring field. This article reviews the recent literature on data-driven structural health monitoring using unsupervised learning methods, with a focus on real-world application and practicality. Novelty detection using vibration data is the most common approach in unsupervised learning SHM and is given more attention in this review. The challenges and limitations in translating SHM methods from research to practical applications are discussed, along with recommendations for future research directions to improve the reliability of SHM methods.
Article
Computer Science, Interdisciplinary Applications
Kejie Jiang, Qiang Han, Xiuli Du, Pinghe Ni
Summary: The paper presents a novel decentralized unsupervised structural condition diagnostic framework using deep auto-encoder and manifold learning, clarifying three damage diagnosis mechanisms. The proposed approach extracts features directly from original vibration data without the need for additional signal preprocessing and relies only on output signals, demonstrating elegant performance in structural damage detection and localization.
COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING
(2021)
Review
Computer Science, Interdisciplinary Applications
Vahid Reza Gharehbaghi, Ehsan Noroozinejad Farsangi, Mohammad Noori, T. Y. Yang, Shaofan Li, Andy Nguyen, Christian Malaga-Chuquitaype, Paolo Gardoni, Seyedali Mirjalili
Summary: This paper provides a comprehensive review of structural health monitoring in civil engineering structures, covering fundamental definitions, strategies, anomaly detection methods, validation benchmarks, and the pros and cons of each approach.
ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING
(2022)
Article
Construction & Building Technology
Qianen Xu, Zheng Zhou, Yang Liu
Summary: In this study, a cross-diagnosis method based on distributed strain data spatial window is proposed for accurately diagnosing the structural condition of a long-span suspension bridge. The method divides the distributed strain data into different spatial windows and separates the temperature effect from the strain monitoring data using the special symmetry of the environmental temperature effect. A diagnosis index of the structural condition is generated using a convolutional neural network, which effectively reflects the variation in the distributed strain correlation model caused by the damaged condition of the bridge.
STRUCTURAL CONTROL & HEALTH MONITORING
(2023)
Article
Engineering, Aerospace
Ning Yang, Sujuan Luo, Ying Lei
Summary: This paper proposes an improved method for structural damage diagnosis based on statistical moment, which reduces the required number of measurements by utilizing the temporal moment of partially measured structural responses. The method has a robust antinoise property and is capable of identifying structural damage when the measured responses are lower than the structural stiffness.
JOURNAL OF AEROSPACE ENGINEERING
(2021)
Article
Construction & Building Technology
M. Ragab, M. Lazhari, M. L. Nehdi
Summary: This study proposed a novel DL-based damage detection approach to automatically extract features from raw acceleration sensor data and identify damage locations in real-time. Parametric studies and system studies were conducted to optimize the network architecture and training data, achieving excellent damage localization and classification performance.
AUTOMATION IN CONSTRUCTION
(2022)
Article
Automation & Control Systems
Josef Koutsoupakis, Dimitrios Giagopoulos, Iraklis Chatziparasidis
Summary: Real-time monitoring of mechanical systems through vibration measurements enables fault detection and predictive maintenance. The use of Artificial Intelligence (AI) in damage detection provides automated means for Condition Monitoring (CM) and characterization of health states. This study proposes a novel CM framework using Convolutional Neural Networks (CNNs) for damage detection and identification, applied to an elevator door rail using simulated data. Rating: 8 out of 10.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2023)
Article
Engineering, Multidisciplinary
Tryfon-Chrysovalantis Aravanis, John Sakellariou, Spilios Fassois
Summary: The novel unsupervised functional model-based method shows ideal detection performance for robust damage detection under varying environmental and operating conditions. Comparative experiments with two alternative statistical time series methods demonstrate that the functional model-based and multiple model-based methods perform well, while the principal component analysis-based method shows degraded performance.
STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL
(2021)
Article
Engineering, Multidisciplinary
Vamvoudakis-Stefanou Kyriakos, Fassois Spilios, Sakellariou John
Summary: The novel method proposed is an unsupervised hypersphere-based healthy subspace approach for robust damage detection under non-quantifiable uncertainty using a limited number of random vibration response sensors. The method combines simplicity and full automation with high performance, systematically assessed through experimental case studies and comparisons with other robust detection methods, demonstrating excellent detection performance.
STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL
(2022)
Article
Engineering, Civil
Christos S. Sakaris, Musa Bashir, Yang Yang, Constantine Michailides, Jin Wang, John S. Sakellariou
Summary: This study investigates the problem of damaged tendon diagnosis in a new multibody offshore platform supporting a 10 MW Floating Offshore Wind Turbine. Vibration-based methodology is used for damage detection, damaged tendon identification and damage precise quantification, with successful results achieved through the Functional Model Based Method. The study demonstrates effective, reliable and quick damaged tendon diagnosis using the platform's dynamics under damaged tendons.
ENGINEERING STRUCTURES
(2021)
Article
Engineering, Mechanical
I. A. Iliopoulos, J. S. Sakellariou, S. D. Fassois
Summary: This study explores the feasibility of automated railway track segment characterisation using railway-vehicle-based random vibration signals and Statistical Time Series methods. The performance of three methods within a Multiple Model framework is assessed based on two distinct track characterisation problems, showing deterioration of the track segment and differences between nominally identical segments. The superiority of the employed methods over a state-of-the-art method is demonstrated through proper comparisons.
VEHICLE SYSTEM DYNAMICS
(2022)
Article
Engineering, Mechanical
C. S. Sakaris, J. S. Sakellariou, S. D. Fassois
Summary: The study introduces a random vibration data-based Functional Model Based Method (FMBM) for multi-site damage localization. Experimental validation shows high effectiveness in determining the number of damage sites and estimating their precise coordinates along with their associated uncertainty, with comparisons and sensitivity analysis also conducted.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2021)
Article
Green & Sustainable Science & Technology
Christos S. Sakaris, Yang Yang, Musa Bashir, Constantine Michailides, Jin Wang, John S. Sakellariou, Chun Li
Summary: This study investigates the structural health monitoring of a Floating Offshore Wind Turbine (FOWT) tendons under varying environmental and operating conditions (EOCs) for the first time. The research successfully achieves damage diagnosis, damaged tendon identification, and damage precise quantification, with very good damage detection results and accurate damaged tendon identification.
Article
Acoustics
T. -C. I. Aravanis, J. S. Sakellariou, S. D. Fassois
Summary: A robust method for precise damage localization is proposed, which is capable of providing accurate damage coordinate estimates even under varying operating conditions. The method only requires partial models and a few vibration sensors, without relying on large-scale finite element models or knowledge of the operating conditions. Experimental validation shows high achievable accuracy in the presence of considerable variations in the boundary conditions.
JOURNAL OF SOUND AND VIBRATION
(2022)
Article
Transportation Science & Technology
K. Kritikakos, S. D. Fassois, J. S. Sakellariou
Summary: This paper discusses the problem of robust and early detection of railway hunting and proposes two single-sensor 'dynamics-based' methods using adaptive Recursive AutoRegressive (RAR) modeling of the car-body lateral random vibration signal. The first method is based on a Degree-of-Stochasticity (DS) measure of the vibration signal, while the second method is based on the minimum underlying Damping Ratio (DR). Bayesian optimization is used to achieve fully automated tuning for both methods. The performance of the methods is systematically assessed through numerous Monte Carlo simulations using a high-fidelity SIMPACK-based vehicle model and three performance criteria: True Positive Rate (TPR), False Positive Rate (FPR), and Detection Delay Time (DDT) compared to conventional hunting initiation. The results show that both methods have excellent performance and robustness to suspension faults and worn track conditions, with the DR-based method exhibiting an advantage. Its performance and achievable robustness are also clearly superior to three alternative methods.
INTERNATIONAL JOURNAL OF RAIL TRANSPORTATION
(2023)
Article
Engineering, Mechanical
G. Vlachospyros, S. D. Fassois, J. S. Sakellariou
Summary: A robust data-driven method is introduced for on-board vibration-based degradation detection in railway suspensions. The method utilizes two lateral vibration acceleration sensors per vehicle half, one on the bogie and one on the vehicle body. It is validated through thousands of Monte Carlo simulation experiments and field tests, demonstrating perfect detection performance for "small" level degradation and somewhat less effective detection for "minor" degradation. The method's superiority over alternative schemes is also demonstrated through comparisons with an entropy-based approach.
VEHICLE SYSTEM DYNAMICS
(2023)
Article
Engineering, Electrical & Electronic
Nikolaos Kaliorakis, John S. Sakellariou, Spilios D. Fassois
Summary: This study investigates the prompt detection of early-stage hollow worn wheels in railway vehicles using on-board random vibration measurements. Two unsupervised statistical time series methods were proposed and assessed through case studies. The results show that both methods exhibit remarkable performance in detecting wheel wear.
Proceedings Paper
Engineering, Civil
Dimitrios M. Bourdalos, Ilias A. Iliopoulos, John S. Sakellariou
Summary: This study explores the feasibility of automated and robust detection of incipient faults in rotating machinery under different operating speeds using unsupervised vibration-based Statistical Time Series (STS) methods. The study employs two unsupervised methods to assess the detection performance and validates their effectiveness through experiments.
EUROPEAN WORKSHOP ON STRUCTURAL HEALTH MONITORING (EWSHM 2022), VOL 2
(2023)
Proceedings Paper
Engineering, Civil
Ioannis E. Saramantas, John S. Sakellariou, Spilios D. Fassois
Summary: This research addresses the problem of robust and unsupervised damage detection for a population of composite aerostructures based on random vibration response. Two robust damage detection methods are proposed and evaluated through Monte Carlo simulations. The results indicate excellent performance for the MM-TF-ARX method and inferior performance for the PCA-TF-ARX method.
EUROPEAN WORKSHOP ON STRUCTURAL HEALTH MONITORING (EWSHM 2022), VOL 2
(2023)
Proceedings Paper
Automation & Control Systems
Kiriakos Kritikakos, Spilios D. Fassois, John S. Sakellariou, Ilias Chronopoulos, Alexandros Deloukas, Ilias A. Iliopoulos, George Leoutsakos, Ilias Tountas, Georgios Vlachospyros
Summary: The study investigates two on board hunting detection methods, with the RAR-based method showing the highest effectiveness in terms of accuracy and shorter detection delay time.
Article
Engineering, Mechanical
Xuanen Kan, Yanjun Lu, Fan Zhang, Weipeng Hu
Summary: A blade disk system is crucial for the energy conversion efficiency of turbomachinery, but differences between blades can result in localized vibration. This study develops an approximate symplectic method to simulate vibration localization in a mistuned bladed disk system and reveals the influences of initial positive pressure, contact angle, and surface roughness on the strength of vibration localization.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2024)
Article
Engineering, Mechanical
Zimeng Liu, Cheng Chang, Haodong Hu, Hui Ma, Kaigang Yuan, Xin Li, Xiaojian Zhao, Zhike Peng
Summary: Considering the calculation efficiency and accuracy of meshing characteristics of gear pair with tooth root crack fault, a parametric model of cracked spur gear is established by simplifying the crack propagation path. The LTCA method is used to calculate the time-varying meshing stiffness and transmission error, and the results are verified by finite element method. The study also proposes a crack area share index to measure the degree of crack fault and determines the application range of simplified crack propagation path.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2024)
Article
Engineering, Mechanical
Rongjian Sun, Conggan Ma, Nic Zhang, Chuyo Kaku, Yu Zhang, Qirui Hou
Summary: This paper proposes a novel forward calculation method (FCM) for calculating anisotropic material parameters (AMPs) of the motor stator assembly, considering structural discontinuities and composite material properties. The method is based on multi-scale theory and decouples the multi-scale equations to describe the equivalence and equivalence preconditions of AMPs of two scale models. The effectiveness of this method is verified by modal experiments.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2024)
Article
Engineering, Mechanical
Hao Zhang, Jiangcen Ke
Summary: This research introduces an intelligent scheduling system framework to optimize the ship lock schedule of the Three Gorges Hub. By analyzing navigational rules, operational characteristics, and existing problems, a mixed-integer nonlinear programming model is formulated with multiple objectives and constraints, and a hybrid intelligent algorithm is constructed for optimization.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2024)
Article
Engineering, Mechanical
Jingjing He, Xizhong Wu, Xuefei Guan
Summary: A sensitivity and reliability enhanced ultrasonic method has been developed in this study to monitor and predict stress loss in pre-stressed multi-layer structures. The method leverages the potential breathing effect of porous cushion materials in the structures to increase the sensitivity of the signal feature to stress loss. Experimental investigations show that the proposed method offers improved accuracy, reliability, and sensitivity to stress change.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2024)
Article
Engineering, Mechanical
Benyamin Hosseiny, Jalal Amini, Hossein Aghababaei
Summary: This paper presents a method for monitoring sub-second or sub-minute displacements using GBSAR signals, which employs spectral estimation to achieve multi-dimensional target detection. It improves the processing of MIMO radar data and enables high-resolution fast displacement monitoring from GBSAR signals.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2024)
Article
Engineering, Mechanical
Xianze Li, Hao Su, Ling Xiang, Qingtao Yao, Aijun Hu
Summary: This paper proposes a novel method for bearing fault identification, which can accurately identify faults with few samples under complex working conditions. The method is based on a Transformer meta-learning model, and the final result is determined by the weighted voting of multiple models.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2024)
Article
Engineering, Mechanical
Xiaomeng Li, Yi Wang, Guangyao Zhang, Baoping Tang, Yi Qin
Summary: Inspired by chaos fractal theory and slowly varying damage dynamics theory, this paper proposes a new health monitoring indicator for vibration signals of rotating machinery, which can effectively monitor the mechanical condition under both cyclo-stationary and variable operating conditions.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2024)
Article
Engineering, Mechanical
Hao Wang, Songye Zhu
Summary: This paper extends the latching mechanism to vibration control to improve energy dissipation efficiency. An innovative semi-active latched mass damper (LMD) is proposed, and different latching control strategies are tested and evaluated. The latching control can optimize the phase lag between control force and structural response, and provide an innovative solution to improve damper effectiveness and develop adaptive semi-active dampers.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2024)
Article
Engineering, Mechanical
Menghao Ping, Xinyu Jia, Costas Papadimitriou, Xu Han, Chao Jiang, Wang-Ji Yan
Summary: Identification of non-Gaussian processes is a challenging task in engineering problems. This article presents an improved orthogonal series expansion method to convert the identification of non-Gaussian processes into a finite number of non-Gaussian coefficients. The uncertainty of these coefficients is quantified using polynomial chaos expansion. The proposed method is applicable to both stationary and nonstationary non-Gaussian processes and has been validated through simulated data and real-world applications.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2024)
Article
Engineering, Mechanical
Lei Li, Wei Yang, Dongfa Li, Jianxin Han, Wenming Zhang
Summary: The frequency locking phenomenon induced by modal coupling can effectively overcome the dependence of peak frequency on driving strength in nonlinear resonant systems and improve the stability of peak frequency. This study proposes the double frequencies locking phenomenon in a three degrees of freedom (3-DOF) magnetic coupled resonant system driven by piezoelectricity. Experimental and theoretical investigations confirm the occurrence of first frequency locking and the subsequent switching to second frequency locking with the increase of driving force. Furthermore, a mass sensing scheme for double analytes is proposed based on the double frequencies locking phenomenon.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2024)
Article
Engineering, Mechanical
Kai Ma, Jingtao Du, Yang Liu, Ximing Chen
Summary: This study explores the feasibility of using nonlinear energy sinks (NES) as replacements for traditional linear tuned mass dampers (TMD) in practical engineering applications, specifically in diesel engine crankshafts. The results show that NES provides better vibration attenuation for the crankshaft compared to TMD under different operating conditions.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2024)
Article
Engineering, Mechanical
Wentao Xu, Li Cheng, Shuaihao Lei, Lei Yu, Weixuan Jiao
Summary: In this study, a high-precision hydraulic mechanical stand and a vertical mixed-flow pumping station device were used to conduct research on cavitation signals of mixed-flow pumps. By analyzing the water pressure pulsation signal, it was found that the power spectrum density method is more sensitive and capable of extracting characteristics compared to traditional time-frequency domain analysis. This has significant implications for the identification and prevention of cavitation in mixed-flow pump machinery.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2024)
Article
Engineering, Mechanical
Xiaodong Chen, Kang Tai, Huifeng Tan, Zhimin Xie
Summary: This paper addresses the issue of parasitic motion in microgripper jaws and its impact on clamping accuracy, and proposes a symmetrically stressed parallelogram mechanism as a solution. Through mechanical modeling and experimental validation, the effectiveness of this method is demonstrated.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2024)
Article
Engineering, Mechanical
Zhifeng Shi, Gang Zhang, Jing Liu, Xinbin Li, Yajun Xu, Changfeng Yan
Summary: This study provides useful guidance for early bearing fault detection and diagnosis by investigating the effects of crack inclination and propagation direction on the vibration characteristics of bearings.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2024)
