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The complexity of modern energy and power systems poses unprecedented challenges to signal processing technology [1]. In fields such as mechanical equipment monitoring, underwater acoustic detection, fluid dynamics analysis, and power system operation and maintenance, the signals collected by sensors often show significant non-stationarity, nonlinearity, and strong noise interference characteristics [2–7]. For example, the vibration signals of rotating machinery often contain transient shock components [2, 3]. For the power grid, the transient current is mixed with the high-frequency harmonics and random disturbances [4, 5]. For fluid machinery, the fluctuations of fluid pressure show serious nonlinearity and non-stationarity [6, 7]. If these complex signals cannot be effectively identified [8], it will directly affect the accuracy of status judgment and the correctness of operation and maintenance decisions [9–11].

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Introduction

  • 章节

  • 发表日期：

    2025年

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  • Yuning Zhang,
  • Chenxin Yang,
  • Peng Luo,
  • Heng Zhang

摘要

The complexity of modern energy and power systems poses unprecedented challenges to signal processing technology [1]. In fields such as mechanical equipment monitoring, underwater acoustic detection, fluid dynamics analysis, and power system operation and maintenance, the signals collected by sensors often show significant non-stationarity, nonlinearity, and strong noise interference characteristics [2–7]. For example, the vibration signals of rotating machinery often contain transient shock components [2, 3]. For the power grid, the transient current is mixed with the high-frequency harmonics and random disturbances [4, 5]. For fluid machinery, the fluctuations of fluid pressure show serious nonlinearity and non-stationarity [6, 7]. If these complex signals cannot be effectively identified [8], it will directly affect the accuracy of status judgment and the correctness of operation and maintenance decisions [9–11].