Digital twin simulation and intelligent diagnosis training method for power plant equipment failure

Abstract

The application provides a power plant equipment fault digital twin simulation and intelligent diagnosis training method, and relates to the technical field of digital twins. The power plant equipment fault digital twin simulation and intelligent diagnosis training method comprises the following steps: S1: collecting multi-source operation data of a target equipment in a power plant, wherein the multi-source operation data at least comprises vibration data, temperature data, pressure data and acoustic data, and performing time synchronization and data preprocessing on the multi-source operation data; and S2: constructing a digital twin basic model of the equipment based on the preprocessed multi-source operation data. Through unified collection of multi-source data such as vibration, temperature, pressure and acoustics, time alignment and data cleaning processing are implemented, so that different source data form consistent input basis under the same time reference, and collaborative modeling is performed in combination with physical mechanism expression and data-driven expression, so that the equipment operation state has mechanism explainability and data self-adaptability.

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