Soft Computing Feature Extraction for Health Monitoring of Rotorcraft Structures

2007 ◽  
Author(s):  
P. J. Escamilla-Ambrosio ◽  
N. Lieven
Keyword(s):  
Feature Extraction ◽  
Soft Computing ◽  
Health Monitoring

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 Feature Extraction and Application of Bio-Signal

2019 ◽  
Vol 8 (6S2) ◽  
pp. 960-964
Keyword(s):  
Feature Extraction ◽  
Deep Learning ◽  
Side Effects ◽  
Health Monitoring ◽  
Medical Diagnosis ◽  
Biomedical Applications ◽  
Electric Signal ◽  
Time Signal ◽  
Biological Source ◽  
Structure Calculations

Biosignals have turned into a significant pointer for medical diagnosis and consequent treatment, yet in addition uninvolved health monitoring. Extracting important highlights from biosignals can help individuals comprehend the human useful state, with the goal that up and coming unsafe side effects or disease can be lightened or stayed away from. There are two fundamental methodologies ordinarily used to get valuable highlights from biosignals, which are hand-engineering and deep learning. Most of the examination in this field centers around hand- engineering highlights, which require space explicit specialists to structure calculations to remove important highlights. In the most recent years, a few investigations have utilized profound figuring out how to biologically take in highlights from crude biosignals to make include extraction calculations less reliant on people. Biosignals give correspondence among biosystems and are our essential wellspring of data on their conduct. Translation and change of signal are significant subjects of this content. Biosignals, similar to all signal, must be conveyed by some type of vitality. Biosignals can be estimated straightforwardly from their biological source, however frequently outer vitality is utilized to gauge the cooperation between the physiological framework and outside vitality. Estimating a biosignal involves changing over it to an electric signal utilizing a device known as a biotransducer. The resultant analog signal is frequently changed over to an advanced (discrete-time) signal for preparing in a PC. These investigations have likewise shown promising outcomes in an assortment of biosignal applications. In this overview, we audit various kinds of biosignals and the principle ways to deal with concentrate highlights from the signal with regards to biomedical applications.

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.

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