Steam turbine component health state assessment method based on ResNet

Abstract

The invention discloses a steam turbine component health state assessment method based on ResNet, which comprises the following steps: carrying out a simulation experiment of a steam turbine component, and carrying out vibration data acquisition through a plurality of measuring points, wherein the vibration data comprises fault signal data and normal working condition data; carrying out label assignment on the fault signal data, wherein the label information comprises a fault type and residual available life; segmenting the fault signal data assigned by each label and carrying out normalization processing to obtain a sample set; dividing the sample set according to a preset proportion to obtain a training set and a verification set; training a pre-constructed ResNet-based multi-task learning neural network model through the training set by adopting a strategy of adaptively updating the network learning rate to reach a preset convergence condition, and obtaining a trained ResNet-based evaluation model; steam turbine part health state assessment is achieved through the assessment model. A multi-task learning mechanism is adopted, the fault type and the health degree of the steam turbine can be judged at the same time, and the accuracy is high.

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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