Article
Acoustics
Qiankun Zhu, Depeng Cui, Qiong Zhang, Yongfeng Du
Summary: Vibration-based structural health monitoring (SHM) systems are useful for assessing structural safety performance. This study presents a noncontact approach using digital cameras to capture structural vibration information and a novel image preprocessing technique to enhance the quality of the signals. The system uses phase-based optical flow estimation, mode decomposition, and phase-based motion magnification to recognize the vibration of the structure.
JOURNAL OF SOUND AND VIBRATION
(2022)
Article
Computer Science, Interdisciplinary Applications
Enjian Cai, Yi Zhang, Ser Tong Quek
Summary: This paper proposes a novel multifrequency phase inference method for characterizing challenging nonstationary and small motions in optical measurement. Through practical applications, the proposed method demonstrates high-quality and clearer motion estimation of video components.
COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING
(2023)
Article
Acoustics
Aisha Javed, Jueon Park, Hyeongill Lee, Youkyung Han
Summary: Vibration analysis is widely used for structural health monitoring. Traditional contact-type sensors can degrade the performance of lightweight structures, so image-based non-contact sensors have been proposed. This study introduces a non-contact vibration signal separation technique to identify multiple factors causing vibrations at different frequencies in a structure. The technique detects vibrations using an edge-based measurement and uses motion magnification to generate magnified videos at different frequencies for separating vibration signals.
JOURNAL OF SOUND AND VIBRATION
(2023)
Article
Engineering, Multidisciplinary
L. Felipe-Sese, A. J. Molina-Viedma, M. Pastor-Cintas, E. Lopez-Alba, F. A. Diaz
Summary: Phase-Based Motion Magnification (PBMM) combined with Fringe Projection and 2D Digital Image Correlation (FP + 2D-DIC) is an effective method for visualizing imperceptible phenomena and obtaining 3D displacement maps using a single camera. The integration of these techniques is validated through tests and compared with other methods. The potential of this integrated approach for determining complex mode shapes is demonstrated.
Article
Engineering, Electrical & Electronic
Jiwen Zhou, Hongguang Li, Li Zhang, Xiaojian Wang, Yun Li
Summary: A novel iterative algorithm called AOFPS is proposed in this article for measuring vibration signals from videos without explicitly manipulating the phase, achieving robust and accurate vibration measurements while reducing computational expenses. Simulation and laboratory experiments demonstrate the effectiveness and accuracy of the proposed algorithm.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2021)
Article
Engineering, Multidisciplinary
Nicholas A. Valente, Celso T. do Cabo, Zhu Mao, Christopher Niezrecki
Summary: Phase-based motion magnification (PMM) is widely used in vibration and structural health monitoring. This study quantifies the relation between magnification and physical motion using synthetic simulation and experimental verification, and introduces image enhancement techniques to improve the accuracy of magnified motion quantification.
Article
Engineering, Civil
Jothi S. Thiyagarajan, Dionysius M. Siringoringo, Samten Wangchuk, Yozo Fujino
Summary: In the past earthquakes, damages on lights and utility poles mounted on elevated highway or railway bridges were observed, caused by excessive response amplification during large earthquakes. Investigation of seismic performance is needed to avoid this amplification, and non-contact vision sensing is seen as a promising alternative to conventional contact sensors for vibration testing. The non-contact vision method is effectively capable of obtaining the natural frequency and damping ratio of structures under ambient conditions.
SMART STRUCTURES AND SYSTEMS
(2021)
Article
Engineering, Mechanical
Kui Luo, Xuan Kong, Xiuyan Wang, Tengjiao Jiang, Gunnstein T. Froseth, Anders Ronnquist
Summary: This study proposes a method based on broad-band phase-based video motion magnification and line tracking algorithms for measuring small-amplitude vibration of cables, which improves the measurement accuracy.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2023)
Article
Acoustics
Yanda Shao, Ling Li, Jun Li, Senjian An, Hong Hao
Summary: This paper proposes a target-free full-field 3D tiny vibration measurement approach for civil engineering structures using a binocular vision system, based on deep learning and motion magnification, achieving high accuracy in measuring tiny vibrations.
JOURNAL OF SOUND AND VIBRATION
(2022)
Article
Engineering, Multidisciplinary
Xi Wang, Fei Li, Qianzheng Du, Yang Zhang, Tao Wang, Guoqiang Fu, Caijiang Lu
Summary: A new deep learning-based vision measurement method is proposed to accurately measure micro-vibration displacements of objects under different illuminations and backgrounds. The method preprocesses the video, applies deep learning correlation techniques to zoom in on the target object and track its vibration trajectory, and converts pixel displacement to actual displacement. Experimental results demonstrate that the proposed method outperforms traditional methods, especially in complex environments, achieving exceptional accuracy.
Article
Engineering, Civil
ZongQing Hang, Pengxiang Bai, Wenkang Du, Can Cui, Dong Lei
Summary: This paper proposes a novel motion magnification algorithm called Euler fast motion detection (EFMD), which can achieve real-time measurement with low computation cost. Laboratory verification and field experiments demonstrate its high accuracy in measuring dynamic response.
JOURNAL OF CIVIL STRUCTURAL HEALTH MONITORING
(2023)
Article
Chemistry, Analytical
Enjian Cai, Dongsheng Li, Jianyuan Lin, Hongnan Li
Summary: This paper presents a novel use of spectra to make motion magnification robust to large movements. By exploiting spectra, artificial limitations and the magnification of small motions are avoided at similar frequency levels while ignoring large ones. Experimental results demonstrate the superiority of this method over current approaches.
Article
Engineering, Civil
Dionysius M. Siringoringo, Samten Wangchuk, Yozo Fujino
Summary: This paper introduces a vision-based method for measuring the vibrations of light poles on elevated highway bridges, using motion magnification and dynamic mode decomposition techniques. Experimental results demonstrate the accuracy and effectiveness of the proposed method in accurately extracting the vibration characteristics of light poles in non-contact long-distance measurements.
ENGINEERING STRUCTURES
(2021)
Article
Computer Science, Artificial Intelligence
Ricard Lado-Roige, Marco A. Perez
Summary: The goal of video motion magnification techniques is to amplify small movements in videos that were previously invisible. This has applications in various fields such as biomedicine, deepfake detection, structural modal analysis, and predictive maintenance. However, distinguishing small motions from noise is challenging, especially when magnifying subtle, sub-pixel movements. This work introduces a state-of-the-art model based on the Swin Transformer that offers improved tolerance to noisy inputs and produces higher-quality outputs with less noise, blurriness, and artifacts compared to prior techniques. The improved output image quality enables more precise measurements for applications relying on magnified video sequences and may facilitate further advancements in video motion magnification techniques in new technical fields.
KNOWLEDGE-BASED SYSTEMS
(2023)
Article
Construction & Building Technology
Samten Wangchuk, Dionysius M. Siringoringo, Yozo Fujino
Summary: This paper presents a study on modal analysis and tension estimation of stay cables using vision-based measurement. Microvibration of the cables is captured by a video camera and amplified using the phase-based video motion magnification method. Spatial displacements of the cables are extracted from cable images using a centroid searching method. Modal parameters of the cables are identified using the dynamic mode decomposition method. Tension of the cables is estimated by minimizing an error function iteratively. Laboratory-scale experiments and full-scale measurements on cable-stayed bridges confirm the accuracy and efficacy of the vision-based method.
STRUCTURAL CONTROL & HEALTH MONITORING
(2022)
Article
Engineering, Multidisciplinary
Junchao Guo, Qingbo He, Dong Zhen, Fengshou Gu, Andrew D. Ball
Summary: This paper proposes an iterative morphological difference product wavelet (MDPW) method for weak fault feature extraction and fault diagnosis of rolling bearing. The MDPW achieves noise suppression and fault feature enhancement through iterative computation and optimized parameters, and performs fault identification by analyzing the occurrence of fault defect frequencies in the spectrum.
STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL
(2023)
Article
Engineering, Electrical & Electronic
Duo Shan, Changming Cheng, Lingjian Li, Zhike Peng, Qingbo He
Summary: Fault diagnosis of gearboxes is crucial for the safe operation of industrial systems. This article proposes a novel semisupervised method for gearboxes using weighted label propagation and virtual adversarial training to overcome the shortage of labeled data in practical industrial applications. The proposed method leverages unlabeled data for pseudo label inference and introduces sample weights to reduce the negative effect of noisy labels. Experimental results demonstrate the effectiveness of the proposed semisupervised approach in leveraging unlabeled data to solve the labeled data shortage.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2023)
Article
Engineering, Industrial
Junchao Guo, Qingbo He, Dong Zhen, Fengshou Gu, Andrew D. Ball
Summary: This paper proposes a novel method for rotating machinery fault detection, which achieves multi-sensor data fusion using improved cyclic spectral covariance matrix (ICSCM) and motor current signal analysis. The proposed method adaptively acquires multi-sensor mode components and constructs ICSCM using sample entropy to preserve the interaction relationship between different sensors. The ICSCM is then incorporated into an extreme learning machine classifier for fault type identification. The proposed method has achieved satisfactory results and more reliable diagnosis accuracy than other state-of-the-art algorithms in rotating machinery fault detection.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2023)
Article
Engineering, Multidisciplinary
Junchao Guo, Qingbo He, Yang Yang, Dong Zhen, Fengshou Gu, Andrew D. Ball
Summary: This paper proposes a novel AM-FM demodulation method based on LMSB for extracting fault features from gearbox signals. The method can simultaneously demodulate multi-mesh frequency bands and multi-modulation components. The effectiveness of LMSB is demonstrated through numerical simulations and experimental analysis, showing its superiority over other demodulation techniques. This research provides a new perspective for gearbox fault detection.
STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL
(2023)
Review
Computer Science, Information Systems
Siliang Lu, Jingfeng Lu, Kang An, Xiaoxian Wang, Qingbo He
Summary: Edge computing is a promising paradigm for IoT-based machine signal processing and fault diagnosis, as it offloads computations onto IoT edge devices, improving computation efficiency and reducing storage and computation workloads on cloud servers.
IEEE INTERNET OF THINGS JOURNAL
(2023)
Article
Automation & Control Systems
Junchao Guo, Qingbo He, Dong Zhen, Fengshou Gu
Summary: This article proposes a novel fault detection scheme based on cyclic morphological modulation spectrum (CMMS) and hierarchical Teager permutation entropy (HTPE) for rotating machinery. The scheme uses CMMS to analyze the measured signal and obtain CMMS slices with different frequency bands, and utilizes HTPE for improved feature selection. Experimental results show that the proposed scheme effectively obtains fault features and achieves accurate fault classification and recognition.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2023)
Article
Engineering, Mechanical
Xiaoluo Yu, Changming Cheng, Yang Yang, Minggang Du, Qingbo He, Zhike Peng
Summary: This paper proposes a Maximumly Weighted Iteration (MWI) approach to solve ill-conditioned inverse problems in dynamics. The ill-condition of the system coefficient matrix is controlled by iterative weighted decomposition and a weighted term, avoiding matrix inversion. The numerical results show that MWI outperforms Truncated Singular Value Decomposition and Tikhonov regularization in terms of accuracy and anti-noise property. Two application cases demonstrate the potential of MWI in engineering practice.
INTERNATIONAL JOURNAL OF MECHANICAL SCIENCES
(2023)
Article
Engineering, Electrical & Electronic
Qihang Wu, Xiaoxi Ding, Qiang Zhang, Rui Liu, Shanshan Wu, Qingbo He
Summary: This study proposes an Intelligent Edge Fault Diagnosis System (IEDS) based on a lightweight intelligent architecture called Multiplication-Convolution Sparse Network (MCSN). The system enables real-time data processing, fault identification, and fault data filtering with high accuracy and lightweight performance.
IEEE SENSORS JOURNAL
(2023)
Article
Engineering, Electrical & Electronic
Jixu Zhang, Tianqi Li, Qingbo He, Zhike Peng
Summary: In this article, a novel method is proposed to deal with the performance degradation of DOA estimation caused by the interaction of instantaneous frequencies in low SNR. The method adopts the unified general parameterized TF transform with multiple sensors and considers the sparseness of the signal in the angle domain. It enhances the ability to extract valid TF points and improves the accuracy of DOA estimation.
IEEE SENSORS JOURNAL
(2023)
Article
Engineering, Electrical & Electronic
Changming Cheng, Duo Shan, Yuxuan Teng, Baoxuan Zhao, Zhike Peng, Qingbo He
Summary: In order to ensure the reliability and security of gearboxes, accurate and efficient fault diagnosis is highly valued. However, most deep learning methods require sufficient labeled data, which is often lacking in practical industrial applications. Therefore, a semisupervised approach based on a hybrid classification network and weighted pseudo-labeling is proposed to address this problem.
IEEE SENSORS JOURNAL
(2023)
Article
Automation & Control Systems
Sha Wei, Yang Yang, Minggang Du, Qingbo He, Zhike Peng
Summary: The decomposition problem for multiple sinusoidal component signals has been developed in the past decades. However, many complex time series are made up of wave-shape components, which invalidates signal decomposition methods based on sinusoidal components. In this article, a varying wave-shape component decomposition (VWCD) method is proposed to extract time-varying and weak characteristics of wave-shape components from a multicomponent signal. The potential and effectiveness of the proposed VWCD method are verified by some simulated signals with different signal-to-noise ratios, a real-world electroencephalography seizure signal, and an experimental chest wall vibration signal from a microwave vital sign monitoring system.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
(2023)
Article
Automation & Control Systems
Tianyu Liu, Bojian Chen, Weiguo Huang, Lisa Jackson, Lei Mao, Qingbo He, Qiang Wu
Summary: The reliability of manufacturing tooling is crucial for intelligent manufacturing processes. However, limited and unbalanced data pose challenges for accurate tool wear assessment. This study proposes a combined CGAN-HQOA model that generates tool data with higher similarity to real data, resulting in improved tool wear condition assessment using convolutional neural network. The effectiveness of the proposed method is verified using unbalanced data and different cutting tools, demonstrating the superiority of the generated data and the accuracy of tool wear assessment. These findings are valuable for practical applications with limited test data.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
(2023)
Article
Multidisciplinary Sciences
Chong Li, Xinxin Liao, Zhi-Ke Peng, Guang Meng, Qingbo He
Summary: Bio-mechanoreceptors have inspired the design of micro-motion sensors, but achieving high sensitivity and broadband sensing remains a challenge. In this study, researchers developed a Metamaterial Mechanoreceptor (MMR) that mimics rat vibrissae. The MMR uses piezoelectric resonators with distributed zero effective masses, enabling highly sensitive and broadband micro-motion sensing. The MMR offers promising applications in spatio-temporal sensing, remote-vibration monitoring, and smart-driving assistance.
NATURE COMMUNICATIONS
(2023)
Article
Engineering, Electrical & Electronic
Qihang Wu, Xiaoxi Ding, Linhua Zhao, Rui Liu, Qingbo He, Yimin Shao
Summary: This study proposes a multiplication-convolution sparse network (MCSN) with interpretable sparse kernels, which effectively mines fault features from spectrum signals and enhances the interpretability and reliability of the model. Experimental results show that the proposed MCSN achieves high fault recognition accuracy and outperforms other open-source network models.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2023)
Article
Engineering, Industrial
Kui Hu, Qingbo He, Changming Cheng, Zhike Peng
Summary: This paper proposes an adaptive incremental diagnosis model (AIDM) with incremental capabilities, which can achieve quick reconstruction and updating by adding new output nodes and adopting knowledge distillation loss. A new dynamic weight correction algorithm is also introduced to realize the stable and reliable incremental training and dynamic updating of IFD models.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
