Reconstruction of the in-plane mode shape of a rotating tire with a continuous scanning measurement using the Hilbert–Huang transform

Abstract

Generally, the dynamic characteristics (natural frequency, damping, and mode shape) of a structure can be estimated by experimental modal analysis. Among these dynamic characteristics, mode shape requires multiple measurements of the structure at different positions, which increases the experimental cost and time. Recently, the Hilbert-Huang transform (HHT) method has been introduced to extract mode-shape information from a continuous measurement, which requires vibration measurements from one position to another position continuously with a non-contact sensor. In this research study, an effort has been made to estimate the mode shapes of a rolling tire with a single measurement instead of using the conventional experimental setup (i.e., measurement of the vibration of a rolling tire at multiple positions similar to the case of a non-rotating structure), which is used to estimate the dynamic behavior of a rolling tire. For this purpose, HHT, which was used in the continuous measurement of a non-rotating structure in previous research studies, has been used for the case of a rotating system in this study. Ambiguous mode combinations can occur in this rotating system, and therefore, a method to overcome this ambiguity is proposed in this study. In addition, the specific phenomenon for a rotating system is introduced, and the effect of this phenomenon with regard to the obtained results through HHT is investigated.

EMI signal feature enhancement based on extreme energy difference and deep auto‐encoder

“…Common methods for signal processing includes: independent component analysis [1], principle component analysis (PCA) [2], wavelet transform [3], Hilbert Huang transform [4], subspace learning [5][6][7], empirical mode decomposition [5,[7][8][9][10][11], and clustering [12][13][14][15]. To all of these methods, it is always crucial to determine how to select and extract features from a signal, especially from those high-dimension signals.…”