Buy article PDF
The purchased file will be sent to you
via email after the payment is completed.
US$ 35
|
Smart Structures and Systems Volume 28, Number 2, August 2021 , pages 275-287 DOI: https://doi.org/10.12989/sss.2021.28.2.275 |
|
|
|
Real-time structural health monitoring system based on streaming data |
||
Qilin Zhang, Siyuan Sun, Bin Yang, Roland Wüchner, Licheng Pan and Haitao Zhu
|
||
| Abstract | ||
| In this paper, a novel real-time structural health monitoring (SHM) system based on streaming data is proposed. In contrast to a traditional SHM system, the proposed system implements a series of optimizations for data transmission and processing to reduce the latency and better satisfy the real-time requirement. The concept of the watermark in the streaming system is adopted to address the problem of when to trigger the time window calculation under the real-time requirement. Moreover, a well-designed parallel mechanism is used to satisfy the multistage computation requirement in the parallel data stream. A case study in which the proposed system is applied to the Shanghai Tower is presented. The peak picking method is used as an example in the test environment to track the latency of each main operation under different parallelism schemes. The results show that computing in parallel effectively reduces the latency and provides a reference for integrating the random decrement technique (RDT), stochastic subspace identification (SSI), or other more complex but effective algorithms in parallel into the system in the future. The total latency under the test environment from data generation to data transmission to the web server is approximately only 200-400 ms, which indicates the excellent real-time performance of the system. | ||
| Key Words | ||
| data stream; latency; computing; real-time; structural health monitoring; parallel | ||
| Address | ||
| (1) Qilin Zhang, Siyuan Sun, Bin Yang, Licheng Pan, Haitao Zhu: College of Civil Engineering, Tongji University, Shanghai, China; (2) Roland Wüchner: Technical University of Munich, Arcisstr. 21, D-80333 Munchen, Germany. | ||