Traditional data collection from sensors produce a lot of data, which lead to constant power consumption and require more storage space. This study proposes an algorithm for a data acquisition and processing method based on Fourier transform (DFT), which extracts dominant frequency components using harmonic analysis (HA) to identify frequency peaks. This algorithm allows sensors to activate only when an event occurs, while preserving critical information for detecting defects, such as those in the surface structures of buildings and ensuring accuracy for further predictions.

翻译：传统传感器数据采集产生大量数据，导致持续功耗并需要更多存储空间。本研究提出一种基于离散傅里叶变换（DFT）的数据采集与处理方法，通过谐波分析（HA）提取主导频率成分以识别频率峰值。该算法使传感器仅在事件发生时激活，同时保留用于检测缺陷（如建筑物表面结构缺陷）的关键信息，并确保后续预测的准确性。