A Coupling Diagnosis Method for Sensor Faults Detection, Isolation and Estimation of Gas Turbine Engines

Linhai Zhu,Daren Yu,Jinfu Liu,Yujia Ma,Weixing Zhou

doi:10.3390/en13184976

Abstract

In this paper a novel fault detection, isolation, and identification (FDI&E) scheme using a coupling diagnosis method with the integration of the model-based method and unsupervised learning algorithm is proposed and developed for monitoring gas turbine sensor faults, which represents an integration of Square Root Cubature Kalman Filters (SRCKF) and an improved Density-Based Spatial Clustering of Application with Noise (DBSCAN) algorithm. A detection indicator produced by SRCKF with a specific hypothesis is used for extracting sensor fault features against process and measurement noise, as well as operating conditions. Then, an improved DBSCAN is implemented based on a voting scheme to detect and isolate the faulty sensors. Finally, a residual-based fault estimation scheme is proposed to track sensor fault evolution and help to judge the types of faults. Moreover, the observability of the model involved is analyzed to verify the stable operation of the FDI&E scheme. Various experiments for single and concurrent sensor fault scenarios in a dual-spool gas turbine prototype during a whole flight mission are conducted to demonstrate the effectiveness of the proposed FDI&E scheme. Moreover, comparative studies confirm the superiority of our proposed FDI&E scheme than the existing methods in terms of promptness and robustness of the sensor FDI.

Highlights

Gas turbine (GT) engines provide power for airplanes, ships and industrial equipment, and reliable and efficient operation is crucial to their safety and performance
When the performance of the sensor degenerates, malfunctions, or fails, there will be a serious impact on the follow-up monitoring, control, or fault diagnosis systems, resulting in misdiagnosis, false alarm and even unexpected GT faults that lead to unplanned maintenance of equipment [2]
Considering the fact that the Corrected Equilibrium Manifold Expansion (CEME) model instead of the GT physics model is combined with Square Root Cubature Kalman Filters (SRCKF), this paper proposes an observability measure based on condition number to qualitatively measure the degree of system observability in order to help judge the reliability of the sensor fault detection and isolation (FDI)&E scheme

Summary

Introduction

Gas turbine (GT) engines provide power for airplanes, ships and industrial equipment, and reliable and efficient operation is crucial to their safety and performance. Various sensors have been equipped in GTs to monitor and control the safe operation of GTs. the performance of sensors is very sensitive even when the sensor is a healthy one because sensors are often installed in a very poor working environment of a GT, such as high temperature and high pressure [1]. When the performance of the sensor degenerates, malfunctions, or fails, there will be a serious impact on the follow-up monitoring, control, or fault diagnosis systems, resulting in misdiagnosis, false alarm and even unexpected GT faults that lead to unplanned maintenance of equipment [2]. A sensor fault detection, isolation, and estimation (FDI&E)

Methods

Results

Conclusion