Long-short term memory and gas path analysis based gas turbine fault diagnosis and prognosis

Hongyu Zhou,Jingchao Li,Yaofei Jin,Yulong Ying

doi:10.1177/16878140211037767

Hongyu Zhou, Jingchao Li + Show 2 more

Open Access

https://doi.org/10.1177/16878140211037767

Copy DOI

Abstract

At present, the main purpose of gas turbine fault prediction is to predict the performance decline trend of the whole system, but the quantitative and thorough performance health index (PHI) research of every major component is lacking. Regarding the issue above, a long-short term memory and gas path analysis (GPA) based gas turbine fault diagnosis and prognosis method is proposed, which realizes the coupling of fault diagnosis and prognosis process. The measurable gas path parameters (GPPs) and the health parameters (HP) of every main component of the goal engine are obtained through the adaptive modeling strategy and the gas path diagnosis method based on the thermodynamic model. The predictive model of the Long-Short Term Memory (LSTM) network combines the measurable GPPs and the diagnostic HPs to predict the HPs of each major component in the future. Simulation experiments show that the proposed method can effectively diagnose and predict detailed, quantified, and accurate PHIs of the main components. Among them, the maximum root mean square error (RMSE) of the diagnosed component HPs do not exceed 0.193%. The RMSE of the best prediction model is 0.232%, 0.029%, 0.069%, and 0.043% in the HP prediction results of each component, respectively.

Highlights

As a combustion engine, a gas turbine utilizes a continuing flow of hot gas as an actuating medium so as to impel the vane wheel to promotes high-speed rotation and make the chemical energy of fuel converted to useful work
This paper mainly studies a multi-dimensional time series regression (TSR) prediction method that conforms to the gas path diagnosis (GPD) information of each main component, and a long-short term memory and gas path analysis (GPA)-based gas turbine fault diagnosis and prognosis method is proposed
In order to further compare the utility of the method proposed in this paper, recurrent neural network (RNN) and Long-Short Term Memory (LSTM) neural network models that only use gas path measurable parameters as input vector are selected as the comparison algorithm

Summary

Introduction

A gas turbine utilizes a continuing flow of hot gas as an actuating medium so as to impel the vane wheel to promotes high-speed rotation and make the chemical energy of fuel converted to useful work.

Results

Conclusion