Method and system for monitoring the thickness of turbine stator blades in a blast furnace gas waste heat recovery device

Abstract

The invention relates to a method and system for monitoring the thickness of a turbine stator blade of a blast furnace gas waste heat recovery device, wherein the method includes collecting turbine data, and the turbine data includes gas parameters at the inlet and outlet of the turbine and the operation of the turbine parameters and the thickness of turbine stator blades; using the autoencoder algorithm to analyze the collected turbine data, and extract the model features of turbine stator blade dust accumulation; according to the model characteristics of turbine stator blade dust accumulation and the The thickness of the stator blade of the flat machine is used to obtain the deep learning network model of the thickness of the turbine stator blade. The deep learning network model takes the turbine data as input and the turbine stator blade thickness as the output; The turbine data collected in real time is used as input, and the thickness of the turbine vane is monitored in real time. By adopting the method and system, the thickness of the turbine stationary blade can be monitored in real time, the problem of frequent failures of the turbine can be solved, the maintenance cost of the turbine can be reduced, and the power generation can be increased.

Description

technical field [0001] The invention relates to the technical field of gas energy recovery, in particular to the technical field of turbines, in particular to a method and system for monitoring the thickness of turbine stator blades of a blast furnace gas waste heat recovery device. Background technique [0002] The iron and steel industry is an important basic industry of my country's national economy and a pillar industry for the realization of new industrialization, and it is also one of the world's largest energy-consuming industries. Ironmaking is the process that consumes the most energy and resources in the steel production process, and its energy consumption accounts for about 60% of the total energy consumption of iron and steel complexes, much higher than other steel manufacturing processes. In the ironmaking process, 39% of the energy consumption is used in blast furnaces. [0003] The blast furnace gas energy recovery device mainly refers to the blast furnace to...

AI Technical Summary

Problems solved by technology

Since most vibration signal spectrum analysis is performed offline, there is no real-time online monitoring strategy for blade fouling and wear, which cannot meet the requirements of stable operation of TRT devices, and the vibration analysis system is expensive, and generally small and medium-sized TRT devices are not equipped.

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