A rapid DAS signal classification algorithm based on VMD and IMF power spectrum Gaussian fitting

Distributed acoustic sensing (DAS) systems utilize optical fibers as the sensing medium, offering long-distance and wide-range real-time monitoring capabilities. They are widely applied in fields such as seismic monitoring, pipeline leak detection, and railway safety monitoring. To address the challenge of traditional Power Spectral Density (PSD) analysis methods in accurately identifying effective signal frequencies under high-frequency noise interference, this paper proposes a novel spectral feature extraction method that combines variational mode decomposition (VMD) and modal power spectral density (PSD). This approach first employs VMD to decompose vibration signals, effectively filtering out noise and extracting valid signals. Subsequently, PSD is used to extract the spectral characteristics of the sub-modes, and Gaussian functions are applied to fit these spectral curves, forming a feature descriptor matrix that characterizes the vibration source. By compressing the original vibration data into this compact feature matrix, the method aims to replace the original data with the feature matrix for deep learning, enabling intelligent recognition and diagnosis of vibration signal states through training and classification. Using vibration data from coal conveyor rollers at Qinhuangdao Port as an example, the effectiveness of this method in extracting bearing spectral features has been validated. The research findings indicate that this method is suitable for vibration load response evaluation and provides a novel technical tool for intelligent monitoring in DAS systems.