Innovative technology for continuous fatigue crack condition monitoring of bridges: long-term electrochemical fatigue sensor (LTEFS)

Miceli, Marybeth; Moshier, Monty A.; Nelson, Levi; Brinkerhoff, Ryan

doi:10.1784/insi.2016.58.7.351

Cited by 2 publications

(1 citation statement)

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“…Flexibility and durability are concerns for the EFS, which uses an electrolyte that eventually dries out. Furthermore, the EFS uses a purely empirical algorithm to detect fatigue crack initiation [27,28], which may only apply for the specific situations in which it has been evaluated. Finally, mounted at an existing crack tip, the electrochemical fatigue crack sensor is small and designed to detect crack initiation; it is not suitable for monitoring crack propagation.…”

Section: State-of-the-art In Metal Fatigue Crack Sensingmentioning

confidence: 99%

Performance Evaluation of a Carbon Nanotube Sensor for Fatigue Crack Monitoring of Metal Structures

Ahmed

Schumacher

Thostenson

et al. 2020

Sensors

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This article describes research that investigated the ability of a carbon nanotube (CNT) sensor to detect and monitor fatigue crack initiation and propagation in metal structures. The sensor consists of a nonwoven carrier fabric with a thin film of CNT that is bonded to the surface of a structure using an epoxy adhesive. The carrier fabric enables the sensor to be easily applied over large areas with complex geometries. Furthermore, the distributed nature of the sensor improves the probability of detecting crack initiation and enables monitoring of crack propagation over time. Piezoresistivity of the sensor enables strains to be monitored in real time and the sensor, which is designed to fragment as fatigue cracks propagate, directly measures crack growth through permanent changes in resistance. The following laboratory tests were conducted to evaluate the performance of the sensor: (1) continuous crack propagation monitoring, (2) potential false positive evaluation under near-threshold crack propagation conditions, and (3) crack re-initiation detection at a crack-stop hole, which is a commonly used technique to arrest fatigue cracks. Real-time sensor measurements and post-mortem fractography show that a distinguishable resistance change of the sensor occurs due to fatigue crack propagation that can be quantitatively related to crack length. The sensor does not show false positive responses when the crack does not propagate, which is a drawback of many other fatigue sensors. The sensor is also shown to be remarkably sensitive to detecting crack re-initiation.

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Section: State-of-the-art In Metal Fatigue Crack Sensingmentioning

confidence: 99%

Performance Evaluation of a Carbon Nanotube Sensor for Fatigue Crack Monitoring of Metal Structures

Ahmed

Schumacher

Thostenson

et al. 2020

Sensors

View full text Add to dashboard Cite

show abstract

Anomaly detection of bridge health monitoring data based on KNN algorithm

Zhen

Zhu

Fang

et al. 2020

IFS

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Pattern recognition technology is applied to bridge health monitoring to solve abnormalities in bridge health monitoring data. Testing is of great significance. For abnormal data detection, this paper proposes a single variable pattern anomaly detection method based on KNN distance and a multivariate time series anomaly detection method based on the covariance matrix and singular value decomposition. This method first performs compression and segmentation on the original data sequence based on important points to obtain multiple time subsequences, then calculates the pattern distance between each time subsequence according to the similarity measure of the time series, and finally selects the abnormal mode according to the KNN method. In this paper, the reliability of the method is verified through experiments. The experimental results in this paper show that the 5/7/9 / 11-nearest neighbors point to a specific number of nodes. Combined with the original time series diagram corresponding to the time zone view, in this paragraph in the time, the value of the temperature sensor No. 6 stays at 32.5 degrees Celsius for up to one month. The detection algorithm controls the number of MTS subsequences through sliding windows and sliding intervals. The execution time is not large, and the value of K is different. Although the calculated results are different, most of the most obvious abnormal sequences can be detected. The results of this paper provide a certain reference value for the study of abnormal detection of bridge health monitoring data.

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Innovative technology for continuous fatigue crack condition monitoring of bridges: long-term electrochemical fatigue sensor (LTEFS)

Cited by 2 publications

References 0 publications

Performance Evaluation of a Carbon Nanotube Sensor for Fatigue Crack Monitoring of Metal Structures

Performance Evaluation of a Carbon Nanotube Sensor for Fatigue Crack Monitoring of Metal Structures

Anomaly detection of bridge health monitoring data based on KNN algorithm

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