# MEMS-Based Vibration Acquisition for Modal Parameter Identification of Substation Frame
## 1 Introduction
Although the simplicity of tests is an advantage, the limitations of MEMS sensors in application should not be overlooked [23]. Firstly, MEMS are not suitable for long-term monitoring of structures. This is because MEMS sensors require specific carriers, such as portable devices and smartphones, and using such carriers for long-term vibration acquisition is impractical. Secondly, MEMS sensors, being micro semiconductor components, differ from specialized acquisition equipment in that they lack robust durability, which may result in time-history data containing background noise. Therefore, when using MEMS sensor data for parameter identification and analysis, it is advisable to choose identification algorithms with better robustness. Once the structural dynamic response is obtained, the modal parameters identification approach can be employed to determine the modal parameters of the structure, including modal frequencies, damping ratios, and mode shapes [24; 25; 26]. These parameters collectively reflect the structure's vibration characteristics, energy dissipation capacity, and modal deformation patterns [27; 28; 29; 30; 31; 32]. These parameters also play a significant role in structural engineering, such as in the calibration of numerical models, allowing the physical response of numerical models to better approximate the actual structures, thereby further extending research into damage identification under extreme conditions [33]. In power systems, these parameters are primarily used for wind-resistant design of structures [2; 24]. On the other hand, the common methods for structural modal parameter analysis include time-domain and frequency-domain approaches [34; 35; 36; 37; 38]. Time-domain methods directly utilize the experimental time-domain responses to identify modal parameters, with techniques such as the AutoRegressive Moving Average Model [39], the Random Decrement Technique [40; 41], the Covariance-Driven Stochastic Subspace Identification (SSI-Cov) method [42; 43; 44], and the Data-Driven Stochastic Subspace Identification method [45; 46; 47]. In contrast, frequency-domain methods transform the time-domain responses into the frequency domain usingFourier Transform for parameter analysis. Notable examples include the Peak Picking method [48] and Frequency-Domain Decomposition (FDD) [49; 50]. Amongst these methods, the SSI-Cov approach offers a robust algorithm. It is based on stochastic theory and is particularly effective for modal parameter identification. It constructs a Toeplitz matrix using the covariance matrix of the structural response signals and then extracts modal parameters through singular value decomposition [51; 52]. This method is efficient in processing time-domain response data under ambient excitation, and its parameter identification process is minimally affected by noise. The SSI-Cov method has been widely applied to identify the dynamic characteristics of various civil engineering structures, including bridges, high-rise buildings, and some critical civil infrastructures [44; 53; 54; 55]. Therefore, this study employed the SSI-Cov method to identify the modal parameters from the dynamic response time histories of the substation frame. In summary, this paper proposes an experimental method for obtaining the dynamic response of substation frames under ambient excitation using MEMS sensors for vibration data acquisition. The method involves the deployment of smartphones equipped with MEMS sensors on the target structure to capture vibration responses. Based on this data, the SSI-Cov method is employed to identify the modal parameters of the structure. The second section of this study introduces the field test using MEMS-based vibration acquisition devices, covering an overview of the structure, the principles of MEMS vibration data acquisition, and the execution of the field test.