A Comparison of Representation Learning Methods for Damage Detection With Guided Wave Structural Health Monitoring

2021 ◽  
Author(s):  
KANG YANG ◽  
Sungwon Kim ◽  
Rongting Yue ◽  
Joel B. Harley
Keyword(s):  
Structural Health Monitoring ◽  
Damage Detection ◽  
Health Monitoring ◽  
Representation Learning ◽  
Guided Wave ◽  
Learning Methods ◽  
Structural Health

An open database for benchmarking guided waves structural health monitoring algorithms on a composite full-scale outer wing demonstrator

2019 ◽  
Vol 19 (5) ◽  
pp. 1524-1541 ◽  
Author(s):  
Alessandro Marzani ◽  
Nicola Testoni ◽  
Luca De Marchi ◽  
Marco Messina ◽  
Ernesto Monaco ◽  
...  
Keyword(s):  
Structural Health Monitoring ◽  
Damage Detection ◽  
Monitoring System ◽  
Health Monitoring ◽  
Guided Wave ◽  
Full Scale ◽  
Composite Panel ◽  
Health Monitoring System ◽  
Electromechanical Impedance ◽  
Structural Health

This article reports on the creation of an open database of piezo-actuated and piezo-received guided wave signals propagating in a composite panel of a full-scale aeronautical structure. The composite panel closes the bottom part of a wingbox that, along with the leading edge, the trailing edge, and the wingtip, forms an outer wing demonstrator approximately 4.5 m long and from 1.2 to 2.3 m wide. To create the database, a structural health monitoring system, composed of a software/hardware central unit capable of controlling a network of 160 piezoelectric transducers secondarily bonded on the composite panel, has been realized. The structural health monitoring system has been designed to (1) perform electromechanical impedance measurement at each transducer, in order to check for their reliability and bonding strength, and (2) to operate an active guided wave screening for damage detection in the composite panel. Electromechanical impedance and guided wave measurements were performed at four different testing stages: before loading, before fatigue, before impacts, and after impacts. The database, freely available at http://shm.ing.unibo.it/ , can thus be used to benchmarking, on real-scale structural data, guided wave algorithms for loading, fatigue, as well as damage detection, characterization, and sizing. As an example, in this work, a delay and sum algorithm is applied on the post-impact data to illustrate how the database can be exploited.

2-D Directional Phased Array Using Piezoelectric Paint to Detect Damages in Isotropic Plates

2009 ◽  
Author(s):  
Byungseok Yoo ◽  
Darryll J. Pines ◽  
Ashish S. Purekar
Keyword(s):  
Structural Health Monitoring ◽  
Damage Detection ◽  
Health Monitoring ◽  
Ultrasonic Testing ◽  
Phased Array ◽  
Guided Wave ◽  
In Situ Monitoring ◽  
Mode Shapes ◽  
Detection Techniques ◽  
Structural Health

Research interests in structural health monitoring have increased due to in-situ monitoring of structural components to detect damage. This can secure personal safety and reduce maintenance effort for mechanical systems. Conventional damage detection techniques known as nondestructive evaluation (NDE) have been conducted to detect and locate damaged area in structures. Ultrasonic testing, using ultrasonic transducers or electromagnetic acoustic transducers, is one of the most widespread NDE techniques, based on monitoring changes in acoustic impedance. Although the ultrasonic testing has advantages such as high sensitivity to discontinuities and evaluation accuracy, it requires testing surface accessibility, close location to the damaged area, and decent skill and training of technicians. In recent years, modal analysis techniques to capture changes of mode shapes and natural frequency of structures have been investigated. However, the technique is relatively insensitive to small amount of damage such as an initial crack which can rapidly grow in structures under cyclic loadings. In addition, structural health monitoring based on guided waves has become a preferred damage detection approach due to its quick examination of large area and simple inspection mechanisms. There are many techniques used to analyze sensor signals to bring out features related to damage. A phased array coupled with the guided wave approach has been introduced to effectively analyze complicated guided wave signals. Phased array theory as a directional filtering technique is usually used in antenna applications. By using phased array signal processing, virtually steering the array to find the largest response of source, the desired signal component can be enhanced while unwanted information is eliminated.

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.

Efficient storage and processing of large guided wave data sets with random projections

2020 ◽  
pp. 147592172096019
Author(s):  
Sungwon Kim ◽  
Spencer Shiveley ◽  
Alexander CS Douglass ◽  
Yisong Zhang ◽  
Rajeev Sahay ◽  
...  
Keyword(s):  
Structural Health Monitoring ◽  
Damage Detection ◽  
Health Monitoring ◽  
Random Projection ◽  
Guided Wave ◽  
Random Projections ◽  
Data Sets ◽  
Structural Health ◽  
Efficient Storage ◽  
Value Decomposition

Over the last several decades, structural health monitoring systems have grown into increasingly diverse applications. Structural health monitoring excels with large data sets that can capture the typical variability, novel events, and undesired degradation over time. As a result, the efficient storage and processing of these large, guided wave data sets have become a key feature for successful application of structural health monitoring. This article describes a series of investigations into the use of random projection theory to significantly reduce storage burdens and improve computational complexity while not significantly affecting common damage detection strategies. Random projections are used as a lossy compression scheme that approximately retains metrics of distance or similarity between data records. Random projection compression is evaluated using a large 1,440,000 measurement data set, which was collected over 5 months in an unprotected outdoor environment. Accurate damage detection, after the compression process, is achieved through correlation analysis and singular value decomposition. The results indicate consistent detection performance with over 95% of storage compression and more than a 477 times speed improvement in computational cost for singular value decomposition–based damage detection.

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.

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