Combination of damage feature decisions with adaptive boosting for improving the detection performance of a structural health monitoring framework: Validation on an operating wind turbine

Tsiapoki, Stavroula; Bahrami, Omid; Häckell, Moritz; Lynch, Jerome P.; Rolfes, Raimund

doi:10.1177/1475921720909379

Cited by 12 publications

(4 citation statements)

References 20 publications

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“…[11][12][13] Further, wind turbines have drawn increasing interest in the research for SHM of civil engineering structures 14 because of their extensive and remote installations, and their complex and varying dynamical properties. In particular, the focus lies on the monitoring of rotor blades [15][16][17][18] and foundations, 19 as these members are prone to damages. Regarding the former, an open-database benchmark structure was implemented by Ou et al, 20 which features several damage positions and intensities.…”

Section: Introductionmentioning

confidence: 99%

A new open‐database benchmark structure for vibration‐based Structural Health Monitoring

Wernitz

Hofmeister

Jonscher

et al. 2022

Structural Contr & Hlth

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Vibration-based Structural Health Monitoring is an ongoing field of research in many engineering disciplines. As for civil engineering, plenty of experimental structures have been erected in the past decades, both under laboratory and real-life conditions. Some of these facilities became a benchmark for different kinds of methods associated with Structural Health Monitoring such as damage analysis and Operational Modal Analysis, which led to fruitful developments in the global research community. When it comes to the continuous monitoring and assessment of the structural integrity of mechanical systems exposed to environmental and operational variability, the robustness and adaptability of the applied methods is of utmost importance. Such properties cannot be fully evaluated under laboratory conditions, which highlights the necessity of outdoor measurement campaigns. To this end, we introduce a test facility for Structural Health Monitoring comprising a lattice tower exposed to realistic conditions and featuring multiple reversible damage mechanisms. The structure located near Hanover in Northern Germany is densely equipped with sensors to capture the structural dynamics. The environmental conditions are monitored in parallel. The obtained continuous measurement data can be accessed online in an open repository. That is the foundation for benchmarks, consisting of a growing data set that enables the development, evaluation, and comparison of Structural Health Monitoring strategies and methods. In this article, we offer a documentation of the test facility and the data acquisition system. Lastly, we characterize the structural dynamics with the help of a finite element model and by analyzing several month of data.

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Section: Introductionmentioning

confidence: 99%

A new open‐database benchmark structure for vibration‐based Structural Health Monitoring

Wernitz

Hofmeister

Jonscher

et al. 2022

Structural Contr & Hlth

Self Cite

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“…Adoption of sensing is growing increasingly attractive in a wide range of civil engineering applications due to the reduction of sensor cost, the integration of wireless communication that make deployments easier, and the improvement of analytical frameworks that extract value from collected data 1 . This has made monitoring common in many field applications such as structural health monitoring (SHM) 2–5 . In SHM applications, dense sensor arrays are often needed which can drive system costs high.…”

Section: Introductionmentioning

confidence: 99%

Autonomous wireless sensor deployment with unmanned aerial vehicles for structural health monitoring applications

Zhou

Lynch

Zekkos

2022

Structural Contr & Hlth

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Summary Unmanned aerial vehicles (UAVs) equipped with onboard sensors empower end‐users to collect data within a wide range of civil engineering applications such as structural condition assessment. While UAVs have been used primarily as mobile sensing platforms to obtain imagery or other data, their mobility can also be used to deploy sensor networks. In this study, the feasibility of using an autonomous UAV to deploy wireless sensors in structural monitoring applications is proposed. While GPS‐based waypoint navigation is available for automating UAV flight operations, this approach does not provide the accuracy necessary for the precision placement of sensor payloads on structures. Computer vision‐based pose estimation is proposed to improve the accuracy of UAV localization for sensor placement. Variably sized fiducial markers integrated into a single pattern are applied to the surface of the structure and adopted as navigation and precision landing targets that identify sensor placement locations. Visual and inertial measurements are fused by means of a discrete Kalman filter to further increase the robustness of the relative position estimation algorithm that is included in the proportional‐integral‐derivative (PID) control law used for UAV landing. Outdoor experiments under realistic operational conditions are conducted to validate the proposed vision‐aided control of the UAV for sensor placement; the UAV is able to land on a predefined landing point within 10 cm. A UAV moving a wireless accelerometer to locations on a beam is used to experimentally show the validity of automating UAV sensor placement for modal analysis using reconfigurable sensor network topologies.

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“…Adaptive boosting (AdaBoost) is one of the famous algorithms to realize ensemble classifiers by assigning appropriate weights for the samples and component classifiers. In literature, AdaBoost has been widely used in image classification, 24,25 structural health monitoring, 26,27 and forecasting of energy consumption. 28,29 The determination of sample weights is a critical issue, but the weights just become larger or smaller according to the classification results in original AdaBoost framework.…”

Section: Introductionmentioning

confidence: 99%

A novel adaptive boosting algorithm with distance-based weighted least square support vector machine and filter factor for carbon fiber reinforced polymer multi-damage classification

Sheng

Liu

Söffker

2022

Structural Health Monitoring

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Adaptive boosting (AdaBoost) algorithms fuse multiple weak classifiers to generate a strong classifier by adaptively determining the fusion weights of the weak classifiers. According to incorrect or correct classification results, sample weights become larger or smaller. However, this weight update scheme neglects valuable information in the results. Moreover, an important requirement for weak classifiers is an accuracy higher than random guessing. This requirement is likely to lead to an unexpected result. This means that several generated weak classifiers with similar classification results cannot learn from each other. Consequently, the advantage of fusing multiple weak classifiers disappears. The classification and therefore distinction of different failure modes in materials is a typical task for classical nondestructive testing approaches as well as for new approaches based on machine learning methods. In the case different approaches can be applied, the main question is, which and how tuned approaches provide the best results in terms of accuracy or similar metrics. In the multi-damage classification task distinguishing different physical failure mechanisms in Carbon Fiber Reinforced Polymer (CFRP), the above two aspects complicate the application of AdaBoost algorithms. To improve the results, a novel AdaBoost with distance-based weighted least square support vector machine (WLSSVM) and filter factor is proposed. The distance-based WLSSVM is employed to increase the diversity of weak classifiers, the distances of the classification plane and samples are used to measure the classification task. The filter factor is proposed to filter out unnecessary classifiers contributing less to the final decision. The experimental results demonstrate that the improved AdaBoost schemes with distance-based WLSSVM and filter factor outperform state-of-the-art algorithms. The effects of the scheme using the new weighted update and the filter factor on the algorithm are discussed, respectively. The experimental results show that the combination of the two schemes perform better than other schemes.

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Combination of damage feature decisions with adaptive boosting for improving the detection performance of a structural health monitoring framework: Validation on an operating wind turbine

Cited by 12 publications

References 20 publications

A new open‐database benchmark structure for vibration‐based Structural Health Monitoring

A new open‐database benchmark structure for vibration‐based Structural Health Monitoring

Autonomous wireless sensor deployment with unmanned aerial vehicles for structural health monitoring applications

A novel adaptive boosting algorithm with distance-based weighted least square support vector machine and filter factor for carbon fiber reinforced polymer multi-damage classification

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