Structural impact localization method based on sensor array scanning

2021 ◽  
Author(s):  
Qi Chang ◽  
Yao Meng ◽  
Lele Chen ◽  
Jun Liu
Keyword(s):  
Sensor Array ◽  
Localization Method ◽  
Structural Impact ◽  
Impact Localization

Research on the Performance and Improvement of Uniform Linear Sensors Array-Based Impact Localization Method Under Vibration Conditions

2020 ◽  
Vol 20 (24) ◽  
pp. 14932-14939
Author(s):  
Zhenghao Zhang ◽  
Yongteng Zhong ◽  
Jiawei Xiang ◽  
Yongying Jiang ◽  
Zhiling Wang
Keyword(s):  
Localization Method ◽  
Sensors Array ◽  
Impact Localization

scholarly journals A Comparison Study of Extreme Learning Machine and Least Squares Support Vector Machine for Structural Impact Localization

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Qingsong Xu
Keyword(s):  
Support Vector Machine ◽  
Least Squares ◽  
Extreme Learning Machine ◽  
Learning Algorithm ◽  
Support Vector ◽  
Comparison Study ◽  
Fast Learning ◽  
Structural Impact ◽  
Learning Machine ◽  
Impact Localization

Extreme learning machine (ELM) is a learning algorithm for single-hidden layer feedforward neural network dedicated to an extremely fast learning. However, the performance of ELM in structural impact localization is unknown yet. In this paper, a comparison study of ELM with least squares support vector machine (LSSVM) is presented for the application on impact localization of a plate structure with surface-mounted piezoelectric sensors. Both basic and kernel-based ELM regression models have been developed for the location prediction. Comparative studies of the basic ELM, kernel-based ELM, and LSSVM models are carried out. Results show that the kernel-based ELM requires the shortest learning time and it is capable of producing suboptimal localization accuracy among the three models. Hence, ELM paves a promising way in structural impact detection.

Integrating passive- and active-sensing techniques using an L-shaped sensor array for impact and damage localization

2017 ◽  
Vol 29 (17) ◽  
pp. 3436-3443 ◽  
Author(s):  
Hwee Kwon Jung ◽  
Gyuhae Park
Keyword(s):  
Damage Detection ◽  
Prior Knowledge ◽  
Sensor Array ◽  
Piezoelectric Sensor ◽  
Active Sensing ◽  
Damage Localization ◽  
Reflected Waves ◽  
Structural Impact ◽  
Anisotropic Structures ◽  
Series Of Experiments

This article presents a technique for detecting structural impact and damage by integrating passive and active-sensing approaches. An L-shaped piezoelectric sensor array was used to detect and localize impacts by measuring the response of structures. It was found that since this method does not require prior knowledge of structures such as the direction-dependent wave velocity profiles, accurate results could be achieved even on anisotropic structures. This sensor array was then extended to include an active-sensing approach, and the same sensor array was used for damage detection by measuring scattering and reflected waves. A series of experiments was carried out to demonstrate the proposed techniques. The superior capability of the proposed techniques was experimentally demonstrated.

Structural Impact Localization Based on Optical Fiber Sagnac Sensing Technique

2018 ◽  
Vol 38 (10) ◽  
pp. 1006004
Author(s):  
程竹明 Cheng Zhuming ◽  
曾捷 Zeng Jie ◽  
常晨 Chang Chen ◽  
宋雪刚 Song Xuegang ◽  
梁大开 Liang Dakai
Keyword(s):  
Optical Fiber ◽  
Structural Impact ◽  
Impact Localization

scholarly journals A Novel Partial Discharge Localization Method in Substation Based on a Wireless UHF Sensor Array

Sensors ◽  
2017 ◽  
Vol 17 (8) ◽  
pp. 1909 ◽  
Author(s):  
Zhen Li ◽  
Lingen Luo ◽  
Nan Zhou ◽  
Gehao Sheng ◽  
Xiuchen Jiang
Keyword(s):  
Sensor Array ◽  
Partial Discharge ◽  
Localization Method

FBG sensor array-based-low speed impact localization system on composite plate

2015 ◽  
Vol 63 (5) ◽  
pp. 462-467 ◽  
Author(s):  
Yaozhang Sai ◽  
Mingshun Jiang ◽  
Qingmei Sui ◽  
Shizeng Lu ◽  
Lei Jia
Keyword(s):  
Composite Plate ◽  
Sensor Array ◽  
Localization System ◽  
Low Speed ◽  
Fbg Sensor ◽  
Impact Localization

scholarly journals Multiple Signal Classification-Based Impact Localization in Composite Structures Using Optimized Ensemble Empirical Mode Decomposition

2018 ◽  
Vol 8 (9) ◽  
pp. 1447 ◽  
Author(s):  
Yongteng Zhong ◽  
Jiawei Xiang ◽  
Xiaoyu Chen ◽  
Yongying Jiang ◽  
Jihong Pang
Keyword(s):  
Composite Structures ◽  
Sensor Array ◽  
Ensemble Empirical Mode Decomposition ◽  
Signal Classification ◽  
Music Algorithm ◽  
Multiple Signal Classification ◽  
Multiple Signal ◽  
Mode Decomposition ◽  
The Impact ◽  
Impact Localization

Multiple signal classification (MUSIC) algorithm-based structural health monitoring technology is a promising method because of its directional scanning ability and easy arrangement of the sensor array. However, in previous MUSIC-based impact location methods, the narrowband signals at a particular central frequency had to be extracted from the wideband Lamb waves induced by each impact using a wavelet transform. Additionally, the specific center frequency had to be obtained after carefully analyzing the impact signal, which is time consuming. Aiming at solving this problem, this paper presents an improved approach that combines the optimized ensemble empirical mode decomposition (EEMD) and two-dimensional multiple signal classification (2D-MUSIC) algorithm for real-time impact localization on composite structures. Firstly, the impact signal at an unknown position is obtained using a unified linear sensor array. Secondly, the fast Hilbert Huang transform (HHT) with an optimized EEMD algorithm is introduced to extract intrinsic mode functions (IMFs) from impact signals. Then, all IMFs in the whole frequency domain are directly used as the input vector of the 2D-MUSIC model separately to locate the impact source. Experimental data collected from a cross-ply glass fiber reinforced composite plate are used to validate the proposed approach. The results show that the use of optimized EEMD and 2D-MUSIC is suitable for real-time impact localization of composite structures.

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