Steam turbine health state prediction method based on E-LSTM

Abstract

The invention provides a steam turbine health state prediction method based on E-LSTM. The method comprises steps of collecting turbine operation data from a sensor, and preprocessing the turbine operation data; feeding the preprocessed data into an LSTM network, and performing iterative training for multiple times; inputting a plurality of trained model parameters into a genetic algorithm to serve as an initial population, operating the genetic algorithm, and selecting a model parameter with an optimal effect; performing generalization performance verification on the optimal model by using more steam turbine operation data; and predicting the test data set according to the optimal model parameters, and evaluating model errors. According to the method, the accuracy of model prediction canbe improved, over-fitting can be avoided, and multivariate linear regression prediction can be realized, so that the prediction model has a better fitting effect on real data, the error of manual monitoring can be greatly reduced, the fault diagnosis efficiency can be improved, and the occurrence of faults can be informed in advance. The method can be widely applied to state management of variousfirepower and nuclear power plants and even steam turbines of ships.

Description

technical field [0001] The invention relates to a method for predicting the health state, in particular to a method for predicting the health state of a steam turbine generator in a nuclear energy and thermal power plant. Background technique [0002] According to statistics, China's annual thermal power and nuclear power generation account for nearly 80% of the total power generation, and the steam turbine generator is one of the core equipment in the thermal power and nuclear power generation system. Ensuring the safe and stable operation of turbogenerators has always been one of the most important links in the power supply system. However, in the era of Industry 4.0, the traditional sensor + manual monitoring method faces many problems such as high cost and low efficiency, and an intelligent and efficient power supply system status prediction solution is urgently needed. [0003] From the current research results, it can be seen that the traditional observation of sensor...

AI Technical Summary

Problems solved by technology

However, the expert system has the following obvious disadvantages: (1) The relationship between the rules is opaque

The logical relationship between a large number of rules may be opaque, lacking hierarchical knowledge expression

(2) Inefficient search strategy

When there are many rules, the system will run very slowly, and a large-scale rule-based expert system is not suitable for real-time applications

(3) No ability to learn

Moreover, it is difficult for humans to fully explore the internal relationship between various parameters, so that it is impossible to fully interpret the fault information

The expert system is relatively rigid, lacks real-time performance, and has no ability to learn newly discovered fault characteristics, so it is difficult to cope with complex and changeable production environments.

[0008] (3) The existing fault monitoring method is "later aware" of the imminent occurrence of the fault, and there is not enough time to deal with it when the fault is discovered

However, the way of avoiding failures by over-maintenance and early replacement has low economic benefits.

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