Gas turbine anomaly detection method based on NARX network-box diagram and normal mode extraction

Abstract

The invention discloses a gas turbine anomaly detection method based on an NARX network-box line graph and constant mode extraction, and the method comprises the steps: training an NARX neural networkthrough the data of a training set, and obtaining an exhaust temperature prediction value of training data and a trained NARX neural network model; calculating a residual error between the exhaust temperature prediction value and the corresponding exhaust temperature true value, and inputting the residual error into an improved box line graph algorithm to obtain a residual error detection threshold value; calculating a residual error between a turbine exhaust temperature value predicted by a model obtained by inputting to-be-detected data into a trained NARX neural network model and an actualturbine exhaust temperature value and judging whether the residual error is within a residual error detection threshold value or not. According to the method, the problem that in the prior art, abnormity detection of the gas turbine cannot be achieved under the condition that only a large amount of normal historical data exists is solved, online detection can be achieved, and the method has important significance in safe and reliable operation of the gas turbine.

Description

technical field [0001] The invention belongs to the field of gas turbine detection and control, and proposes a gas turbine anomaly detection method based on NARX network-box diagram and normal mode extraction. Background technique [0002] Gas turbine is a very important power machine, which is widely used in aviation power, ship power, mechanical drive, power system and other fields. Gas turbines work in an environment of high temperature, high pressure, and high-speed rotation for a long time, and the thermal load and mechanical load are large, so they are prone to failure. Gas turbine failures often result in high maintenance costs and huge economic losses. On-line anomaly detection of gas turbine is of great significance to improve the operation safety and reliability of gas turbine. [0003] In the document "Gas turbine fault diagnosis using probabilistic neural networks (gas turbine fault diagnosis based on probabilistic neural network)" published by Loboda I, Robles...

AI Technical Summary

Problems solved by technology

[0004] The purpose of this invention is to propose a gas turbine anomaly detection method based on NARX network-box plot and normal mode extraction, and to establish a gas turbine engine using massive normal historical data of gas turbine units The normal mode model realizes the online anomaly detection of gas turbine units without having fault samples and normal samples at the same time, which solves the problem of anomaly detection of gas turbines that cannot be detected in the existing technology only with massive normal historical data

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