Anomaly Detection on Sound Data Using Dynamic Mode Decomposition

Abstract

As the evolution of sensors and computers enables collecting abundant data, methods to analyze high-dimentional data are becoming important. Dyanmic mode decompostion (DMD) is a data-driven method to extract dynamic structure from data and is attracting attention recently. In this study, we made use of DMD to analyze sound data of rotary machines with normal and abnormal states. We applied DMD to spectral distributions of the data and analyzed the dynamic structure of spectral distributions. We found that on spectral distributions of data from abnormal states, time-decaying structure is more likely to be dominant than those from normal states.