Development of a wireless, self-sustaining damage detection sensor system based on chemiluminescence for structural health monitoring

2014 ◽  
Author(s):  
K. S. C. Kuang
Keyword(s):  
Structural Health Monitoring ◽  
Damage Detection ◽  
Health Monitoring ◽  
Sensor System ◽  
Structural Health

Toward data anomaly detection for automated structural health monitoring: Exploiting generative adversarial nets and autoencoders

2020 ◽  
pp. 147592172092460 ◽  
Author(s):  
Jianxiao Mao ◽  
Hao Wang ◽  
Billie F Spencer
Keyword(s):  
Structural Health Monitoring ◽  
Anomaly Detection ◽  
Damage Detection ◽  
Health Monitoring ◽  
Sensor System ◽  
Series Data ◽  
Generative Adversarial Networks ◽  
Learning Methods ◽  
Structural Health ◽  
Anomalous Data

Damage detection is one of the most important tasks for structural health monitoring of civil infrastructure. Before a damage detection algorithm can be applied, the integrity of the data must be ensured; otherwise results may be misleading or incorrect. Indeed, sensor system malfunction, which results in anomalous data (often called faulty data), is a serious problem, as the sensors usually must operate in extremely harsh environments. Identifying and eliminating anomalies in the data is crucial to ensuring that reliable monitoring results can be achieved. Because of the vast amounts of data typically collected by a structural health monitoring system, manual removal of the anomalous data is prohibitive. Machine learning methods have the potential to automate the process of data anomaly detection. Although supervised methods have been proven to be effective for detecting data anomalies, two unresolved challenges reduce the accuracy of anomaly detection: (1) the class imbalance and (2) incompleteness of anomalous patterns of training dataset. Unsupervised methods have the potential to address these challenges, but improvements are required to deal with vast amounts of monitoring data. In this article, the generative adversarial networks are combined with a widely applied unsupervised method, that is, autoencoders, to improve the performance of existing unsupervised learning methods. In addition, the time-series data are transformed to Gramian Angular Field images so that advanced computer vision methods can be included in the network. Two structural health monitoring datasets from a full-scale bridge, including examples of anomalous data caused by sensor system malfunctions, are utilized to validate the proposed methodology. Results show that the proposed methodology can successfully identify data anomalies with good accuracy and robustness, hence can overcome one of the key difficulties in achieving automated structural health monitoring.

scholarly journals Magnetostrictive Sensors for Composite Damage Detection and Wireless Structural Health Monitoring

2019 ◽  
Vol 55 (7) ◽  
pp. 1-6
Author(s):  
Zhaoyuan Leong ◽  
William Holmes ◽  
James Clarke ◽  
Akshay Padki ◽  
Simon Hayes ◽  
...  
Keyword(s):  
Structural Health Monitoring ◽  
Damage Detection ◽  
Health Monitoring ◽  
Structural Health ◽  
Composite Damage

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.

Radar-based structural health monitoring of wind turbine blades: The case of damage detection

2017 ◽  
Vol 17 (4) ◽  
pp. 815-822 ◽  
Author(s):  
Jochen Moll ◽  
Philip Arnold ◽  
Moritz Mälzer ◽  
Viktor Krozer ◽  
Dimitry Pozdniakov ◽  
...  
Keyword(s):  
Structural Health Monitoring ◽  
Damage Detection ◽  
Wind Turbine ◽  
Health Monitoring ◽  
Turbine Blades ◽  
Heterogeneous Material ◽  
Material System ◽  
Wind Turbine Blades ◽  
Millimetre Wave ◽  
Structural Health

Structural health monitoring of wind turbine blades is challenging due to its large dimensions, as well as the complex and heterogeneous material system. In this article, we will introduce a radically new structural health monitoring approach that uses permanently installed radar sensors in the microwave and millimetre-wave frequency range for remote and in-service inspection of wind turbine blades. The radar sensor is placed at the tower of the wind turbine and irradiates the electromagnetic waves in the direction of the rotating blades. Experimental results for damage detection of complex structures will be presented in a laboratory environment for the case of a 10-mm-thick glass-fibre-reinforced plastic plate, as well as a real blade-tip sample.

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.

High-performance fiber optic systems for damage detection and structural health monitoring

2004 ◽  
Author(s):  
Mark E. Seaver ◽  
Stephen T. Trickey ◽  
Jonathan M. Nichols ◽  
Linda Moniz ◽  
Lou Pecora ◽  
...  
Keyword(s):  
Structural Health Monitoring ◽  
Damage Detection ◽  
Health Monitoring ◽  
High Performance ◽  
Fiber Optic ◽  
Structural Health ◽  
High Performance Fiber ◽  
Optic Systems
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