A resnet-based health status assessment method for steam turbine components

Abstract

The invention discloses a ResNet-based method for assessing the health status of steam turbine components, which includes: performing a simulation experiment on steam turbine components, and collecting vibration data through multiple measuring points; the vibration data includes fault signal data and normal working condition data; the fault signal The data is tagged; the tag information includes the fault type and remaining usable life; the fault signal data after each tag assignment is segmented and normalized to obtain a sample set; the sample set is divided according to a preset ratio to obtain a training set and verification Set; adopt the strategy of adaptively updating the network learning rate, train the pre-built multi-task learning neural network model based on ResNet through the training set, and obtain the trained evaluation model based on ResNet to the preset convergence condition; realize the steam turbine through the evaluation model Component health assessment. The invention adopts a multi-task learning mechanism, can simultaneously judge the fault type and the health degree of the steam turbine, and has high accuracy.

Description

technical field [0001] The invention belongs to the field of industrial machinery monitoring and fault diagnosis, and relates to a method for evaluating the health state of steam turbine components, in particular to a method for evaluating the health state of steam turbine components based on ResNet (Residual Neural Network, residual neural network). Background technique [0002] Turbine generator set is the key equipment for electric power production. It has the characteristics of complex structure, harsh working conditions (high temperature, high pressure, high speed), high requirements for continuous operation, etc., and is prone to failure. During the operation of the unit, its main components include rotors, blades, cylinders, etc., and rotors and blades are important components. Once a fault occurs and cannot be checked in time, it will cause unplanned shutdown due to the vibration exceeding the limit, and serious Crew damage and casualties. Steam turbine rotor failur...

AI Technical Summary

Problems solved by technology

[0005] The purpose of the present invention is to provide a ResNet-based method for assessing the health status of steam turbine components to solve the problems of low diagnostic efficiency, poor prediction accuracy, and unfavorable industrial promotion due to the need for expert experience or excessive model simplification in existing methods

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