# MEMS-Based Vibration Acquisition for Modal Parameter Identification of Substation Frame
## 1 Introduction  
Substation frames are spatial truss structures, characterized by their significant height and large spans, typically arranged in rows within power substations. In engineering practice, it is crucial for substation frames in operation to maintain stable dynamic characteristics, often represented by modal parameters. However, due to the cumulative effects of various environmental factors, such as rain, snow, strong winds, and earthquakes, these structures are susceptible to fatigue, potentially altering their dynamic characteristics [1, 2, 3, 4]. The collapse of a single-row substation frame can initiate a chain reaction, posing a serious threat to the normal operation of the substation. Therefore, conducting dynamic characteristic tests on newly constructed substation frames is essential to ascertain the modal parameters and ensure their reliability in service. From a structural dynamic perspective, field testing is the most reliable method for obtaining the dynamic response of a structure [5, 6, 7]. However, collecting dynamic responses from substation frames entails significant risks. Traditional methods of acquiring dynamic responses primarily involve deploying contact-type vibration sensors on the target structure. For instance, the 941b ultra-low-frequency vibration pickups are capable of accurately capturing structural vibrations under various load conditions [8]. Despite their accuracy, these sensors may not perform optimally for certain structures, such as tall steel frames. The need to connect vibration pickups to data acquisition devices via shielded cables introduces challenges, particularly when measurement points are located at considerable heights. Under lateral loads, such as wind, the shielded wire may swing,occasionally causing the solder joint between the shielded wire and the sensor to disconnect. As a result, it is necessary to explore a new experimental method to simplify the testing process. To address the aforementioned issues, computer vision-based vibration measurement technology has become increasingly popular [9; 10; 11; 12]. This method primarily relies on high-precision visual equipment to capture video footage of the target structure, and then employs computer vision processing techniques to extract the vibration response at specific points. However, this approach is not without challenges. Firstly, as a non-contact measurement method, it introduces the potential for errors due to vibrations from the acquisition equipment itself. Secondly, the method requires calibration at fixed positions on the structure, which may not significantly reduce the experimental complexity. Furthermore, the equipment involved is expensive, and the analysis of vibration responses is heavily dependent on the performance of the computing hardware. Consequently, for certain specialized structures, particularly those where height complicates vibration monitoring, there is a clear need for the development of a more user-friendly and cost-effective vibration acquisition method to advance structural dynamic response research [13; 14; 15; 16; 17]. At the same time, the development of micro-electromechanical systems (MEMS) technology offers a new option for vibration monitoring [18]. Firstly, MEMS technology has made it possible to miniaturize traditional sensors into tiny semiconductor components, such as MEMS sensors [19; 20]. By simulating the structure of traditional sensors, MEMS can achieve acceleration acquisition. Secondly, the development of MEMS technology has significantly reduced the cost of semiconductor components. These two factors enable the application of MEMS sensors in structural vibration monitoring, although the use of MEMS sensors still requires a carrier. Today, most mobile devices are equipped with built-in MEMS sensors, providing a good means to capture structural acceleration responses using MEMS sensors. Moreover, there are examples where MEMS sensors have been utilized to assess structural and seismic behavior, with the aim of detecting and localizing damage [21; 22].