Structural health monitoring from discrete binary data through pattern recognition

2016 ◽  
pp. 1840-1845 ◽  
Author(s):  
H Salehi ◽  
R Burgueño ◽  
S Das ◽  
S Biswas ◽  
S Chakrabartty
Keyword(s):  
Pattern Recognition ◽  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Binary Data ◽  
Structural Health

scholarly journals Pattern recognition framework using asynchronous discrete binary data for condition and damage assessment in plate-like structures

2019 ◽  
Vol 30 (8) ◽  
pp. 1200-1215 ◽  
Author(s):  
Hadi Salehi ◽  
Rigoberto Burgueño
Keyword(s):  
Pattern Recognition ◽  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Damage Assessment ◽  
Binary Data ◽  
Data Communication ◽  
K Nearest Neighbor ◽  
Energy Aware ◽  
Structural Health ◽  
Self Powered

Recent advances in energy harvesting technologies have led to the evolution of self-powered structural health monitoring techniques that are energy-efficient. Concurrent to the emergence of self-powered sensing has been the development of power-efficient data communication protocols. One such technology is an energy-aware pulse communication architecture that employs ultrasonic pulses through the material substrate for information forwarding. This results in limited discrete binary data that raises the need for new data analysis methods for structural health monitoring purposes. A pattern recognition framework that allows interpretation of the resulting asynchronous discrete binary data for condition and damage assessment in plate-like structures is presented in this article. The proposed pattern recognition framework is based on integration of image-based pattern recognition using anomaly detection, a pattern anomaly measure, a focal density concept, and the k-nearest neighbor algorithm. Using numerical simulations, damage indication parameters were determined from the strain response of dynamically loaded plates. Simulated test cases considering different levels of damage severity, single and multiple damage regions, loading conditions, and measurement noise were studied to evaluate the effectiveness and robustness of the strategy. Furthermore, the effect of sensor density on the proposed strategy was explored. Results demonstrate satisfactory performance and robustness of the proposed pattern recognition framework for localized damage detection in plate-like structures using limited and low-resolution discrete binary data.

Development of a Structural Health Monitoring Methodology in Reinforced Concrete Structures Using FBGs and Pattern Recognition Techniques

2019 ◽  
Author(s):  
Alejandra Amaya ◽  
Joham Alvarez-Montoya ◽  
Julián Sierra-Pérez
Keyword(s):  
Pattern Recognition ◽  
Reinforced Concrete ◽  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Structural Engineering ◽  
Relevant Information ◽  
Fiber Optic Sensor ◽  
Structure Damage ◽  
Structural Health ◽  
External Stimuli

Abstract Structural health monitoring (SHM) is a branch of structural engineering which seeks for the development of monitoring systems that provide relevant information of any alteration that may occur in an engineering structure. This work presents the implementation of an SHM methodology in a prototype structure made of reinforced concrete by using fiber Bragg gratings (FBGs), a type of fiber optic sensor capable of measuring strain and temperature changes due to external stimuli. The SHM system includes an interrogation device and signal processing algorithms which are intended to study the physical variations on the FBGs measurements in order to detect anomalies in the structure promoted by a damage occurrence. The structure prototype is a porticoed structure which contains 48 embedded sensors: 32 of them are destinated for the strain measurement and are located in both columns and beams of the structure, 16 are temperature sensors which have been embedded for thermal compensation. Strain datasets for both pristine and damaged conditions were obtained for the structure while it was excited with a mechanical shaker which induced dynamic loading conditions resembling earthquakes. By using classification algorithms based on pattern recognition, it is intended to process the datasets with the aim of reaching the first level of SHM in the structure (damage detection).

scholarly journals Big Data Analytics and Structural Health Monitoring: A Statistical Pattern Recognition-Based Approach

Sensors ◽  
2020 ◽  
Vol 20 (8) ◽  
pp. 2328 ◽  
Author(s):  
Alireza Entezami ◽  
Hassan Sarmadi ◽  
Behshid Behkamal ◽  
Stefano Mariani
Keyword(s):  
Pattern Recognition ◽  
Feature Extraction ◽  
Big Data ◽  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Statistical Pattern Recognition ◽  
Statistical Decision ◽  
Structural Health ◽  
Statistical Pattern ◽  
Arma Modeling

Recent advances in sensor technologies and data acquisition systems opened up the era of big data in the field of structural health monitoring (SHM). Data-driven methods based on statistical pattern recognition provide outstanding opportunities to implement a long-term SHM strategy, by exploiting measured vibration data. However, their main limitation, due to big data or high-dimensional features, is linked to the complex and time-consuming procedures for feature extraction and/or statistical decision-making. To cope with this issue, in this article we propose a strategy based on autoregressive moving average (ARMA) modeling for feature extraction, and on an innovative hybrid divergence-based method for feature classification. Data relevant to a cable-stayed bridge are accounted for to assess the effectiveness and efficiency of the proposed method. The results show that the offered hybrid divergence-based method, in conjunction with ARMA modeling, succeeds in detecting damage in cases strongly characterized by big data.

Strain Field Pattern Recognition for Structural Health Monitoring Applications

2020 ◽  
pp. 1-40
Author(s):  
Julián Sierra-Pérez ◽  
Joham Alvarez-Montoya
Keyword(s):  
Pattern Recognition ◽  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Strain Field ◽  
Machine Learning Techniques ◽  
Field Pattern ◽  
Strain Mapping ◽  
Engineering Structures ◽  
Structural Health ◽  
Nonlinear Pca

Strain field pattern recognition, also known as strain mapping, is a structural health monitoring approach based on strain measurements gathered through a network of sensors (i.e., strain gauges and fiber optic sensors such as FGBs or distributed sensing), data-driven modeling for feature extraction (i.e., PCA, nonlinear PCA, ANNs, etc.), and damage indices and thresholds for decision making (i.e., Q index, T2 scores, and so on). The aim is to study the correlations among strain readouts by means of machine learning techniques rooted in the artificial intelligence field in order to infer some change in the global behavior associated with a damage occurrence. Several case studies of real-world engineering structures both made of metallic and composite materials are presented including a wind turbine blade, a lattice spacecraft structure, a UAV wing section, a UAV aircraft under real flight operation, a concrete structure, and a soil profile prototype.

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