Article
Engineering, Multidisciplinary
Bowen Wei, Bin Xie, Huokun Li, Zimeng Zhong, Yun You
Summary: A method combining empirical mode decomposition and masking signal processing is proposed to address difficulties in extracting vibration signals of a high arch dam flow discharge structure under water flow excitation. By mixing different frequency parts of the signal and effectively inhibiting modal confusion, the method accurately identifies the modal parameters of the structure.
APPLIED MATHEMATICAL MODELLING
(2021)
Article
Engineering, Electrical & Electronic
Resul Azizi, Serhat Seker
Summary: A sequential ensemble of intelligence-based methods is proposed for fault detection and classification in microgrids. This method uses collective decision of learners to increase accuracy and is robust to overfitting due to its nonconvex optimization method. Additionally, the Hilbert-Huang transform is chosen for feature extraction to reduce noise sensitivity.
IEEE TRANSACTIONS ON POWER DELIVERY
(2022)
Article
Engineering, Mechanical
Soroosh Kamali, Mohammad Ali Hadianfard
Summary: In this work, a novel method called SOMI is proposed for estimating the modal parameters of structures. SOMI can accurately estimate modal damping ratios and mode shapes using the Frequency Domain Decomposition principles. It eliminates the problems encountered by the FDD method and achieves better accuracy and robustness in noisy conditions.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2023)
Article
Engineering, Electrical & Electronic
Uender Barbosa de Souza, Joao Paulo Lemos Escola, Leonardo da Cunha Brito
Summary: Signal processing methods such as Fourier Transform and Wavelet Transform are limited in examining non-linear and non-stationary processes, but the Hilbert-Huang Transform has been considered the most appropriate tool for dealing with such signals due to its adaptive nature and lack of limitation by the uncertainty principle. While HHT has some limitations, it has gained significant interest in the academic community and been applied in various fields in recent years.
DIGITAL SIGNAL PROCESSING
(2022)
Article
Engineering, Civil
Yansong Diao, Dantong Jia, Guodong Liu, Zuofeng Sun, Jing Xu
Summary: In this study, a new structural damage detection algorithm using the modified Hilbert-Huang transform and support vector machine is proposed. The algorithm is effective in analyzing nonlinear and non-stationary signals to detect the location and extent of damage.
JOURNAL OF CIVIL STRUCTURAL HEALTH MONITORING
(2021)
Article
Engineering, Biomedical
Xiaocai Shan, Shoudong Huo, Lichao Yang, Jun Cao, Jiaru Zou, Liangyu Chen, Ptolemaios Georgios Sarrigiannis, Yifan Zhao
Summary: This paper proposes a new time-frequency brain functional connectivity analysis framework based on Revised Hilbert-Huang Transform, which outperforms wavelet-based method in terms of accuracy and time-frequency resolution. The method shows potential in better differentiating epileptic patients and healthy controls.
IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING
(2021)
Article
Computer Science, Information Systems
Amir Reza Aqamohammadi, Taher Niknam, Sattar Shojaeiyan, Pierluigi Siano, Moslem Dehghani
Summary: This study proposes a smart fault detection method (FDM) for microgrids (MGs) based on the Hilbert-Huang transform (HHT) and deep neural networks (DNNs). The method aims to rapidly detect fault type, phase, and location data to protect MGs and restore services. The approach preprocesses branch current measurements using HHT and extracts features using singular value decomposition (SVD) for input to DNNs. Compared to previous studies, this method achieves higher fault-type identification accuracy and can determine new fault locations. Evaluation on IEEE 34-bus and MG systems demonstrates its effectiveness in terms of detection precision, computing time, and robustness to measurement uncertainties.
Article
Computer Science, Information Systems
Jinkun Jiang, Qi Zhang, Xiangjun Xin, Ran Gao, Xishuo Wang, Feng Tian, Qinghua Tian, Bingchun Liu, Yongjun Wang
Summary: This article proposes a blind modulation format identification method based on PCA and SVD. By eliminating the influence of phase rotation and implementing a denoise method, the quality of the constellation is improved, and accurate identification of seven modulation formats is achieved.
Article
Engineering, Multidisciplinary
Supriyo Mahata, Piyush Shakya, N. Ramesh Babu
Summary: Grinding is a finishing operation to achieve desired surface finish, but wheel wear is a primary constraint for productivity. A new methodology using Hilbert Huang transform and support vector machine accurately identifies wheel wear in cylindrical grinding. Monitoring wheel condition with accelerometers allows for reliable wear detection during grinding.
PRECISION ENGINEERING-JOURNAL OF THE INTERNATIONAL SOCIETIES FOR PRECISION ENGINEERING AND NANOTECHNOLOGY
(2021)
Article
Construction & Building Technology
Jiantao Li, Xinqun Zhu, Jian Guo
Summary: This paper presents a new method based on successive variational mode decomposition (SVMD) to extract the dynamic components of a bridge and estimate its modal parameters. The effectiveness and feasibility of the proposed method are investigated through numerical analysis and experimental tests.
ADVANCES IN STRUCTURAL ENGINEERING
(2022)
Article
Automation & Control Systems
Mingjin Zhang, Hongyu Chen, Tingyuan Yan, Hao Sun, Lianhuo Wu
Summary: This paper analyzed the application of Hilbert-Huang transform for modal parameter identification in order to accurately obtain the modal parameters of long-span bridge in wind tunnel test. A band-pass filter was designed to filter the original signal and eliminate the mode mixing effect. Meanwhile, an endpoint data extension method based on SVM was proposed to suppress the end effects of empirical mode decomposition. The improved algorithm was applied to identify modal parameters of Oujiang Bridge under ambient excitation, and the results showed that the improved method can accurately identify the main modal parameters and reduce frequency identification errors.
MEASUREMENT & CONTROL
(2023)
Article
Statistics & Probability
Rungang Han, Pixu Shi, Anru R. Zhang
Summary: This article introduces a novel dimension reduction framework called functional tensor singular value decomposition (FTSVD) for tensors with one functional mode and several tabular modes. It is motivated by high-order longitudinal data analysis. The proposed method successfully estimates both singular vectors and functions of the low-rank structure in the observed data using tensor algebra and the theory of reproducing kernel Hilbert space (RKHS). The superiority of the framework is demonstrated through extensive experiments on simulated and real data. The detailed explanations for this article are available online.
JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
(2023)
Article
Engineering, Civil
Guangyong Sun, Wenju Li, Quantian Luo, Qing Li
Summary: This study developed an effective method for identifying modal parameters of vibrating structures using DIC technique, SVD, and nonlinear least square iteration. Experimental results demonstrated the advantages of this method over other techniques, showing effective identification of modal parameters in practical applications.
THIN-WALLED STRUCTURES
(2021)
Article
Engineering, Mechanical
Xu-Qiang Shang, Tian -Li Huang, Hua-Peng Chen, Wei-Xin Ren, Meng -Lin Lou
Summary: Modal identification is essential for structural condition monitoring, and Variational mode decomposition (VMD) is widely used for this purpose with excellent performance. However, it is challenging to preset the decomposition parameters due to abnormal impulses and heavy noise in practical engineering. To tackle this issue, a new method called orthogonal and recursive VMD (ORVMD) is proposed, which consists of recursive VMD (RVMD) and a rough-to-precise decomposition scheme based on an orthogonal algorithm. The proposed ORVMD, combined with the Hilbert transform (HT), is employed for modal parameter estimation. Experimental results demonstrate that ORVMD outperforms existing methods in separating multi-component signals, making it an efficient method for identifying the natural frequencies and damping ratios of structures.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2023)
Article
Acoustics
Xuanting Hao, Lian Shen
Summary: This study proposes a data-driven analysis framework based on the adaptive two-dimensional Hilbert-Huang transform for quantitatively characterizing the spatial variability of nearshore wave fields. Through simulations and calculations, the features of coastal wave processes including refraction, shoaling and breaking are investigated, and three integral quantities for characterizing wave fields are proposed.
