Steam turbine rotor fault diagnosis method based on LSTM

Abstract

The invention discloses a steam turbine rotor fault diagnosis method based on LSTM, and belongs to the technical field of mechanical fault diagnosis. Firstly, multi-point acquisition sensors are deployed and controlled, and vibration signals of various typical turbine rotor faults are collected as a training set and a verification set. Secondly, the steam turbine rotor vibration signals are extracted from a power plant DCS system to serve as a testing set. Thirdly, the training set, the testing set and the verification set realize fusion of multi-point signal data and data enhancement throughsignal division, stacking and other operations. Fourthly, a neural network based on the LSTM is constructed, the training set and the verification set are used for completing training of the network,and finally, maintenance of a diagnostic model is achieved in cooperation with an actual diagnostic task, and finally the steam turbine rotor fault diagnosis is realized on the testing set.

Description

technical field [0001] The invention belongs to the technical field of mechanical fault diagnosis, and in particular relates to a method for diagnosing a fault of a steam turbine rotor based on LSTM. 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. In the operation of the unit, the rotor is an important component. Once a failure occurs and cannot be checked in time, it will cause unplanned shutdown due to the vibration exceeding the limit, and cause damage to the unit and casualties in severe cases. Therefore, the fault diagnosis method and experimental research of the turbogenerator rotor are of great significance for ensuring the safe operation of the turbogenerator, reducing major economic losses and avoiding catastrophic acc...

AI Technical Summary

Problems solved by technology

[0005] The purpose of the present invention is to detect and identify steam turbine rotor faults and ensure the safe operation of steam turbine generator sets. A LSTM-based steam turbine rotor fault diagnosis method is provided to solve the need for steam turbine rotor diagnosis in traditional methods with the help of experience and extremely high signals. Handling skills lead to low diagnostic efficiency, poor diagnostic accuracy, and are not conducive to industrial promotion and other issues

Images