Academic Journal
Performance Evaluation of Structural Health Monitoring Anomaly Data Processing Algorithms in Resource-Constrained Edge Computing.
| Τίτλος: | Performance Evaluation of Structural Health Monitoring Anomaly Data Processing Algorithms in Resource-Constrained Edge Computing. |
|---|---|
| Συγγραφείς: | Lei, Kuanjiu, Li, Yaojie, Hou, Shitong, Yan, Wenlong, Qin, Yongjun, Zuo, Feiyu |
| Πηγή: | Sensors (14248220); Jul2026, Vol. 26 Issue 14, p4658, 31p |
| Περίληψη: | Edge computing plays an important role in structural health monitoring (SHM) for transportation infrastructure because it enables local data processing and low-latency decision support. However, SHM systems encounter challenges due to data anomalies caused by sensor faults, transmission errors, or irregular structural behavior. This study evaluates the deployment performance of a SHM anomaly data-processing workflow on resource-constrained edge devices. The workflow includes data cleaning, response separation, and anomaly detection. Missing data are processed using cubic spline interpolation. Jump points and drift are corrected using the Laida criterion, and noise is reduced using wavelet threshold denoising. Response separation is then performed using the detrending method based on time windows, the 3σ criterion, or wavelet packet decomposition. Anomaly detection is then performed using autoregressive integrated moving average with explanatory variables, support vector machines, and recurrent neural networks. Simulation data and field monitoring data from bridge displacement and highway pavement strain are used to evaluate the workflow. The evaluation focuses on runtime, memory usage, central processing unit usage, and a data-processing throughput proxy. This analysis helps to understand the performance and trade-offs of algorithms on edge devices under the resource constraints typical of SHM applications. [ABSTRACT FROM AUTHOR] |
| Copyright of Sensors (14248220) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Βάση Δεδομένων: | Complementary Index |
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| Items | – Name: Title Label: Title Group: Ti Data: Performance Evaluation of Structural Health Monitoring Anomaly Data Processing Algorithms in Resource-Constrained Edge Computing. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Lei%2C+Kuanjiu%22">Lei, Kuanjiu</searchLink><br /><searchLink fieldCode="AR" term="%22Li%2C+Yaojie%22">Li, Yaojie</searchLink><br /><searchLink fieldCode="AR" term="%22Hou%2C+Shitong%22">Hou, Shitong</searchLink><br /><searchLink fieldCode="AR" term="%22Yan%2C+Wenlong%22">Yan, Wenlong</searchLink><br /><searchLink fieldCode="AR" term="%22Qin%2C+Yongjun%22">Qin, Yongjun</searchLink><br /><searchLink fieldCode="AR" term="%22Zuo%2C+Feiyu%22">Zuo, Feiyu</searchLink> – Name: TitleSource Label: Source Group: Src Data: Sensors (14248220); Jul2026, Vol. 26 Issue 14, p4658, 31p – Name: Abstract Label: Abstract Group: Ab Data: Edge computing plays an important role in structural health monitoring (SHM) for transportation infrastructure because it enables local data processing and low-latency decision support. However, SHM systems encounter challenges due to data anomalies caused by sensor faults, transmission errors, or irregular structural behavior. This study evaluates the deployment performance of a SHM anomaly data-processing workflow on resource-constrained edge devices. The workflow includes data cleaning, response separation, and anomaly detection. Missing data are processed using cubic spline interpolation. Jump points and drift are corrected using the Laida criterion, and noise is reduced using wavelet threshold denoising. Response separation is then performed using the detrending method based on time windows, the 3σ criterion, or wavelet packet decomposition. Anomaly detection is then performed using autoregressive integrated moving average with explanatory variables, support vector machines, and recurrent neural networks. Simulation data and field monitoring data from bridge displacement and highway pavement strain are used to evaluate the workflow. The evaluation focuses on runtime, memory usage, central processing unit usage, and a data-processing throughput proxy. This analysis helps to understand the performance and trade-offs of algorithms on edge devices under the resource constraints typical of SHM applications. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Sensors (14248220) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/s26144658 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 31 StartPage: 4658 Titles: – TitleFull: Performance Evaluation of Structural Health Monitoring Anomaly Data Processing Algorithms in Resource-Constrained Edge Computing. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Lei, Kuanjiu – PersonEntity: Name: NameFull: Li, Yaojie – PersonEntity: Name: NameFull: Hou, Shitong – PersonEntity: Name: NameFull: Yan, Wenlong – PersonEntity: Name: NameFull: Qin, Yongjun – PersonEntity: Name: NameFull: Zuo, Feiyu IsPartOfRelationships: – BibEntity: Dates: – D: 15 M: 07 Text: Jul2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 14248220 Numbering: – Type: volume Value: 26 – Type: issue Value: 14 Titles: – TitleFull: Sensors (14248220) Type: main |
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