On the use of the matching pursuit decomposition signal processing technique for structural health monitoring

2005 ◽  
Author(s):  
Santanu Das ◽  
Antonia Papandreou-Suppappola ◽  
Xu Zhou ◽  
Aditi Chattopadhyay
Keyword(s):  
Signal Processing ◽  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Matching Pursuit ◽  
Processing Technique ◽  
Signal Processing Technique ◽  
Structural Health ◽  
Matching Pursuit Decomposition

Structural Health Monitoring Using Iot

2019 ◽  
pp. 144-147
Author(s):  
Sureshkumar M.P ◽  
Vennila G.
Keyword(s):  
Structural Health Monitoring ◽  
Construction Industry ◽  
Health Monitoring ◽  
Continuous Monitoring ◽  
Processing Technique ◽  
Harsh Environments ◽  
Signal Processing Technique ◽  
Computing Technique ◽  
Structural Health ◽  
The World

In construction industry maintenance should be given utmost importance and focus. For continuous monitoring of maintenance Internet of Things (IoT) can be used. IoT can be used to monitor the structure from anywhere. Structural health monitoring using IoT is the latest technique employed all over the world, especially the buildings exposed to harsh environments. Sensors were used to collect the data from the structure from which we can identify the deterioration and the method to rectify. Cloud computing technique was also employed. A simple signal processing technique helps us to interact with buildings, which was the blessing of IoT.  This paper presents the state of art survey about current research and implementations put into practice.

Structural Health Monitoring of Glass/Epoxy Composite Plates Using PZT and PMN-PT Transducers

2010 ◽  
Vol 133 (1) ◽  
Author(s):  
Valeria La Saponara ◽  
David A. Horsley ◽  
Wahyu Lestari
Keyword(s):  
Signal Processing ◽  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Wavelet Transforms ◽  
Composite Structures ◽  
Mode Conversion ◽  
High Stress ◽  
Composite Plates ◽  
Signal Processing Technique ◽  
Structural Health

The structural health monitoring of composite structures presents many challenges, ranging from sensors’ reliability and sensitivity to signal processing and a robust assessment of life to failure. In this research project, sensors constructed with both PZT-4 ceramic and single-crystal PMN-PT, i.e., Pb(Mg1/3Nb2/3)O3−PbTiO3, were investigated for structural health monitoring of composite plates. Fiberglass/epoxy specimens were manufactured with a delamination starter located in the middle of the plate, and were subjected to axial tensile fatigue at a high stress ratio. A surface-mounted PMN-PT pair and a surface-mounted PZT-4 pair were positioned on each side of the delamination starter and excited in turns at set intervals during fatigue loading. This project had two goals: (1) assess the performance of the two piezoelectric materials and (2) develop a signal processing technique based on wavelet transforms capable of detecting damage features that are independent of the transducers (being damaged concurrently to the host composite specimens) and thus can estimate life to failure of the composite specimens. Results indicate that the PMN-PT transducers may be more resilient to fatigue damage of the host structure and possibly generate less dispersive Lamb waves. However, these aspects are compounded with higher costs and manufacturing difficulties. Moreover, the proposed signal processing method shows promise in estimating impending failure in composites: It could, in principle, capture and quantify the complex wave propagation problem of dispersion, scattering, and mode conversion across a delamination front, and it will be further investigated.

Time–frequency and time–scale analyses for structural health monitoring

2006 ◽  
Vol 365 (1851) ◽  
pp. 449-477 ◽  
Author(s):  
Wiesław J Staszewski ◽  
Amy N Robertson
Keyword(s):  
Signal Processing ◽  
Structural Health Monitoring ◽  
Damage Detection ◽  
Health Monitoring ◽  
Time Scale ◽  
Time Frequency ◽  
Structural Health ◽  
Variant Analysis

Signal processing is one of the most important elements of structural health monitoring. This paper documents applications of time-variant analysis for damage detection. Two main approaches, the time–frequency and the time–scale analyses are discussed. The discussion is illustrated by application examples relevant to damage detection.

scholarly journals Application of Machine Learning with Impedance Based Techniques for Structural Health Monitoring of Civil Infrastructure

2019 ◽  
Vol 8 (6S4) ◽  
pp. 1139-1148
Keyword(s):  
Machine Learning ◽  
Signal Processing ◽  
Structural Health Monitoring ◽  
Damage Detection ◽  
Soft Computing ◽  
Health Monitoring ◽  
Multiple Cracks ◽  
Civil Infrastructure ◽  
Structural Health ◽  
Soft Computing Techniques

Increased attentiveness on the environmental and effects of aging, deterioration and extreme events on civil infrastructure has created the need for more advanced damage detection tools and structural health monitoring (SHM). Today, these tasks are performed by signal processing, visual inspection techniques along with traditional well known impedance based health monitoring EMI technique. New research areas have been explored that improves damage detection at incipient stage and when the damage is substantial. Addressing these issues at early age prevents catastrophe situation for the safety of human lives. To improve the existing damage detection newly developed techniques in conjugation with EMI innovative new sensors, signal processing and soft computing techniques are discussed in details this paper. The advanced techniques (soft computing, signal processing, visual based, embedded IOT) are employed as a global method in prediction, to identify, locate, optimize, the damage area and deterioration. The amount and severity, multiple cracks on civil infrastructure like concrete and RC structures (beams and bridges) using above techniques along with EMI technique and use of PZT transducer. In addition to survey advanced innovative signal processing, machine learning techniques civil infrastructure connected to IOT that can make infrastructure smart and increases its efficiency that is aimed at socioeconomic, environmental and sustainable development.

scholarly journals Composite Plate Phased Array Structural Health Monitoring Signal Reconstruction Based on Orthogonal Matching Pursuit Algorithm

2017 ◽  
Vol 2017 ◽  
pp. 1-7 ◽  
Author(s):  
Yajie Sun ◽  
Feihong Gu ◽  
Sai Ji ◽  
Lihua Wang
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Phased Array ◽  
Matching Pursuit ◽  
Sampling Rate ◽  
Sampling Theorem ◽  
Structural Health ◽  
Array Technology ◽  
Ultrasonic Phased Array ◽  
Matching Pursuit Algorithm

In order to ensure the safety of composite components, structural health monitoring is needed to detect structural performance in real-time at the early stage of damage occurred. This is difficult to detect complex components with single sensor detection technology, so that ultrasonic phased array technology using multisensor detection will be selected. Ultrasonic phased array technology can scan the structure in all directions and angles without moving or less moving the probe and becomes the first choice of structural health monitoring. However, a large amount of data will be generated when using ultrasonic phased array with Nyquist sampling theorem for structural health monitoring and is difficult to storage, transmission, and processing. Besides, traditional Nyquist sampling cannot satisfy the sampling of large amounts of data without distortion, so a more efficient acquisition technique must be chosen. Compressive sensing theory can ensure that if the signal is sparse, it can be sampled in low sampling rate which is much less than two times of the sampling rate as defined by Nyquist sampling theorem for a large number of data and reconstructed in high probability. Then, the experiment result indicated that the orthogonal matching pursuit algorithm can reconstruct the signal completely and accurately.

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