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The chapter is dedicated to the identification of vibroacoustic signals from power industry objects, with the goal of utilizing their informational resources for assessing the actual state of these objects. It discusses the peculiarities of identifying vibroacoustic signals and provides examples of the structure of conditional levels for the electrical equipment of a typical power station and the corresponding structure of conditional levels for a monitoring, identification, and diagnostics system for a specific power industry object, a typical power station. A mathematical model for the vibroacoustic signal of an electric machine’s bearing assembly is justified, represented as a linear random process—a stationary RLC-multiresonance noise. The identification of empirical distributions of vibroacoustic signals based on the Pearson curve system is discussed. Algorithmic software for statistically estimating empirical distribution laws of stationary vibroacoustic signals using smoothing curves from the Pearson curve system is provided. The final section of the fifth chapter presents a model and identification characteristics of vibroacoustic signals from power industry objects.

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Identification of Vibration Noise Signals of Electric Power Facilities

  • 章节

  • 发表日期：

    2025年

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  • Vitalii Babak,
  • Artur Zaporozhets,
  • Yurii Kuts,
  • Mykhailo Fryz,
  • Leonid Scherbak

摘要

The chapter is dedicated to the identification of vibroacoustic signals from power industry objects, with the goal of utilizing their informational resources for assessing the actual state of these objects. It discusses the peculiarities of identifying vibroacoustic signals and provides examples of the structure of conditional levels for the electrical equipment of a typical power station and the corresponding structure of conditional levels for a monitoring, identification, and diagnostics system for a specific power industry object, a typical power station. A mathematical model for the vibroacoustic signal of an electric machine’s bearing assembly is justified, represented as a linear random process—a stationary RLC-multiresonance noise. The identification of empirical distributions of vibroacoustic signals based on the Pearson curve system is discussed. Algorithmic software for statistically estimating empirical distribution laws of stationary vibroacoustic signals using smoothing curves from the Pearson curve system is provided. The final section of the fifth chapter presents a model and identification characteristics of vibroacoustic signals from power industry objects.