MEMS-Based Vibration Acquisition for Modal Parameter Identification of Substation Frame

As a critical component of substations, the substation frames are characterized by significant height and span, which presents substantial challenges and risks in conducting dynamic response tests using traditional sensors. To simplify these difficulties, this paper introduces an experimental method utilizing MEMS sensor-based vibration acquisition. In this approach, smartphones equipped with MEMS sensors are deployed on the target structure to collect vibration data under environmental excitation. This method was applied in a dynamic field test of a novel composite substation frame. During the test, the proposed MEMS-based vibration acquisition method was conducted in parallel with traditional ultra-low-frequency vibration acquisition methods to validate the accuracy of the MEMS data. The results demonstrated that the MEMS sensors not only simplified the testing process but also provided reliable data, offering greater advantages in testing convenience compared with traditional contact methods. The modal parameters of the substation frame, including modal frequencies, damping ratios, and mode shapes, were subsequently identified using the covariance-driven stochastic subspace identification method. The experimental methodology and findings presented in this paper offer valuable insights for structural dynamic response testing and the wind-resistant design of substation frames.

Authors

Institutions

Publication Details

Journal
Applied Sciences
Published
2024-09-12
DOI
https://doi.org/10.3390/app14188190
Citations
2
Primary Topic
Structural Health Monitoring Techniques
Type
article
Field-Weighted Citation Impact
0.52

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

MEMS-Based Vibration Acquisition for Modal Parameter Identification of Substation Frame

Yachen Wang, Xianghong Liu, Qing Sun, Ruochen Qiang et al.
2 citations
Applied Sciences
Structural Health Monitoring Techniques
0.52
article

MEMS-Based Vibration Acquisition for Modal Parameter Identification of Substation Frame

Yachen Wang, Xianghong Liu, Qing Sun, Ruochen Qiang, Ming Sheng, Dongxu Su
article en
2 citations

Abstract

As a critical component of substations, the substation frames are characterized by significant height and span, which presents substantial challenges and risks in conducting dynamic response tests using traditional sensors. To simplify these difficulties, this paper introduces an experimental method utilizing MEMS sensor-based vibration acquisition. In this approach, smartphones equipped with MEMS sensors are deployed on the target structure to collect vibration data under environmental excitation. This method was applied in a dynamic field test of a novel composite substation frame. During the test, the proposed MEMS-based vibration acquisition method was conducted in parallel with traditional ultra-low-frequency vibration acquisition methods to validate the accuracy of the MEMS data. The results demonstrated that the MEMS sensors not only simplified the testing process but also provided reliable data, offering greater advantages in testing convenience compared with traditional contact methods. The modal parameters of the substation frame, including modal frequencies, damping ratios, and mode shapes, were subsequently identified using the covariance-driven stochastic subspace identification method. The experimental methodology and findings presented in this paper offer valuable insights for structural dynamic response testing and the wind-resistant design of substation frames.

Applied SciencesVol. 14(18)
State Grid Corporation of China (China) (CN), Xi'an Jiaotong University (CN)
National Natural Science Foundation of China
Affordable and clean energy
Openalex Percentile: Top 36%
Structural Health Monitoring Techniques
0.52
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.