# MEMS-Based Vibration Acquisition for Modal Parameter Identification of Substation Frame
## 5 Conclusions  
This study conducted simultaneous MEMS-based and traditional contact-based vibration acquisition on a new substation frame, capturing the dynamic response time histories. Building on this, the modal parameters of the structure under operational conditions were identified using structural dynamics analysis methods. The key conclusions drawn are as follows:  
1. A method based on MEMS technology for acquiring the dynamic response time histories of structures is proposed, and its feasibility is demonstrated through comparison with traditional vibration monitoring methods. The study found that the vibration response captured by MEMS exhibited slightly higher amplitude compared with results from traditional monitoring methods, primarily due to the MEMS system capturing more environmental noise, which increased the overall variance of the data. However, in terms of frequency-domain analysis, the differences between the two methods at the peak regions were minimal, with the main discrepancies occurring in non-modal, non-resonant regions, corresponding to amplitude differences in the time-domain response. Figure 11: Mode shapes of the substation frame: (**a**) 2.14 Hz; (**b**) 2.81 Hz; (**c**) 3.05 Hz; (**d**) 4.78 Hz. 2. The primary modes of the new substation frame were identified as follows: the first-order torsional mode at 2.14 Hz, the first-order vertical truss mode at 2.81 Hz, the second-order torsional mode at 3.05 Hz, and the first-order parallel truss mode at 4.78 Hz. 3. The modal parameter identification results indicate minimal frequency identification differences when using different datasets or identification methods, as the structural frequency is an inherent property of the structure. In contrast, the damping ratio identification results show some variability, which is attributed to differences in the data acquisition systems and the identification theories used. Overall, the identification results are relatively stable. While using MEMS sensors to collect responses in the field test offers significant convenience, it does not fully mitigate their drawbacks, particularly the unavoidable noise components in the collected data. To address the noise issue, three approaches can be considered. The first approach involves processing the collected digital signals directly, such as filtering technique. However, the choice of filtering method and its settings heavily depends on the operator's experience, introducing a degree of variability in noise reduction. The second approach involves using techniques such as the SSI-Cov algorithm, which leverage covariance matrices to diminish noise effects. The third approach is to directly model the frequency-domain information within the frequency domain. Comparatively, the latter two approaches do not require direct processing of the raw signals and impose fewer demands on the operator. In future research, these two approaches may be more suitable for handling the responses obtained from MEMS sensors. On the other hand, the SSI-Cov method assumes that the response is driven by a stationary random excitation process. For non-stationary signals, the SSI-Cov method is not appropriate, particularly when analyzing time-evolving behavior with the aim of identifying health status related to the rapid evolution of dynamic parameters. In these cases, time-frequency analysis techniques, such as the Short-Time Impulse Response Function, Curvature Evolution Method, Band Variable Filter, and Stockwell Transform, prove to be more effective. Additionally, the modal parameter identification results from this study will help researchers monitor the dynamic characteristics of substation frames throughout their life cycle. This will also better support the calibration of numerical models and the wind-resistant design of structures. These areas of research are valuable and merit further exploration in the future. Conceptualization, R.Q. ; methodology, R.Q. and M.S. ; software, M.S. ; validation, D.S. ; writing--original draft preparation, R.Q.