A sensor signal processing method based on Hankel dynamic mode decomposition

Abstract Modal identification and extraction of monitoring signals are of great significance for aero-engine condition monitoring and health management. Empirical mode decomposition (EMD) method and variational mode decomposition (VMD) method have wide applications in time–frequency signal analysis, but EMD method is very sensitive to noise and sampling frequency. The accuracy of the VMD method relies on the input parameter settings. The dynamic mode decomposition (DMD) method is considered as a high-dimensional data-driven method which can predict the short-term behavior. However, the one-dimensional data decomposed by the DMD method only contains one mode. In this paper, the DMD method based on Hankel reconstruction (HDMD) is applied to the signal sequence to investigate its capability and potential as a signal processing method. First, multi-frequency, similar-frequency, and variable-frequency-variable-amplitude signals are constructed and resolved by various methods. It is shown that the HDMD method is more robust to noise, has a higher frequency resolution, and is capable of uncovering potential frequency trends compared to the EMD method and the VMD method. In addition, this paper applies the HDMD method to identify the measured signals of the aero-engine. It is shown that the HDMD method can accurately decompose the frequency modes of the core components. Compared with the EMD and VMD methods, the HDMD method can reveal the pattern of the engine frequency when it varies within a small range, which is more applicable to the analysis of the actual signals.