scholarly journals Big Data Analytics in Online Structural Health Monitoring

2020 ◽  
Vol 7 (4) ◽  
Author(s):  
Guowei Cai ◽  
Sankaran Mahadevan
Keyword(s):  
Big Data ◽  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Data Analytics ◽  
Structural Damage ◽  
Big Data Analytics ◽  
High Volume ◽  
Heterogeneous Data ◽  
Sensor Technology ◽  
Structural Health

This manuscript explores the application of big data analytics in online structural health monitoring. As smart sensor technology is making progress and low cost online monitoring is increasingly possible, large quantities of highly heterogeneous data can be acquired during the monitoring, thus exceeding the capacity of traditional data analytics techniques. This paper investigates big data techniques to handle the highvolume data obtained in structural health monitoring. In particular, we investigate the analysis of infrared thermal images for structural damage diagnosis. We explore the MapReduce technique to parallelize the data analytics and efficiently handle the high volume, high velocity and high variety of information. In our study, MapReduce is implemented with the Spark platform, and image processing functions such as uniform filter and Sobel filter are wrapped in the mappers. The methodology is illustrated with concrete slabs, using actual experimental data with induced damage

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.

Structural Health Monitoring Data Analysis for Ageing Fixed Offshore Wind Turbine Structures

2021 ◽  
Author(s):  
Baran Yeter ◽  
Yordan Garbatov ◽  
Carlos Guedes Soares
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Structural Health Monitoring ◽  
Wind Turbine ◽  
Health Monitoring ◽  
Offshore Wind ◽  
Monitoring Data ◽  
Offshore Wind Turbine ◽  
Support Structures ◽  
Structural Health

Abstract The objective of the present study is to perform a systematic data analysis of structural health monitoring data for ageing fixed offshore wind turbine support structures. The life-cycle extension of the first offshore wind farms is under serious consideration since the support structures are still in a condition to be used further. Big data analytics and machine learning techniques can aid to extract useful information from the monitoring data collected during the service life and build models for future predictions of an optimal life-extension. To this end, it is aimed to analyse the big data provided by embedded control systems and non-destructive inspections of ageing offshore wind turbine support structures using pre-processing techniques, including denoising, detrending, and filtering to remove the noise of different nature and seasonality as well as to detect the signal-specific contents affecting the structural integrity in the time and frequency domain. The effectiveness of the Welch method is investigated in terms of dealing with noisy signals in the frequency domain. Besides, the principal component analysis is carried out to reduce the dimensionality of the data and to select the most significant features that are responsible for most of the variance in the structural health monitoring data. Moreover, nonparametric statistical methods are used to test whether the data before noise being added and the data after cleansing the added noise came from the population with the same distribution. Further, permutation (randomisation) testing is performed to predicate that the results of the nonparametric test are statistically significant. The outcome of this study provides refined evidence that enables to feed the condition monitoring data into the training of the deep neural network to be able to discriminate different structural conditions.

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