A Correction for the Paper "Symplectic geometry mode decomposition and its application to rotating machinery compound fault diagnosis"

Fuente: arXiv
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Auteurs principaux: Zhang, Hong-Yan, Liu, Haoting, Lin, Rui-Jia, Zhou, Yu
Format: Preprint
Publié: 2025
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author Zhang, Hong-Yan
Liu, Haoting
Lin, Rui-Jia
Zhou, Yu
author_facet Zhang, Hong-Yan
Liu, Haoting
Lin, Rui-Jia
Zhou, Yu
contents The symplectic geometry mode decomposition (SGMD) is a powerful method for decomposing time series, which is based on the diagonal averaging principle (DAP) inherited from the singular spectrum analysis (SSA). Although the authors of SGMD method generalized the form of the trajectory matrix in SSA, the DAP is not updated simultaneously. In this work, we pointed out the limitations of the SGMD method and fixed the bugs with the pulling back theorem for computing the given component of time series from the corresponding component of trajectory matrix.
format Preprint
id arxiv_https___arxiv_org_abs_2508_20990
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle A Correction for the Paper "Symplectic geometry mode decomposition and its application to rotating machinery compound fault diagnosis"
Zhang, Hong-Yan
Liu, Haoting
Lin, Rui-Jia
Zhou, Yu
Signal Processing
The symplectic geometry mode decomposition (SGMD) is a powerful method for decomposing time series, which is based on the diagonal averaging principle (DAP) inherited from the singular spectrum analysis (SSA). Although the authors of SGMD method generalized the form of the trajectory matrix in SSA, the DAP is not updated simultaneously. In this work, we pointed out the limitations of the SGMD method and fixed the bugs with the pulling back theorem for computing the given component of time series from the corresponding component of trajectory matrix.
title A Correction for the Paper "Symplectic geometry mode decomposition and its application to rotating machinery compound fault diagnosis"
topic Signal Processing
url https://arxiv.org/abs/2508.20990