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

Linhai Zhu, Jinfu Liu, Yujia Ma, Weixing Zhou and Daren Yu
Additional contact information
Linhai Zhu: School of Energy Science and Engineering, Harbin Institute of Technology, Harbin 150001, China
Jinfu Liu: School of Energy Science and Engineering, Harbin Institute of Technology, Harbin 150001, China
Yujia Ma: School of Energy Science and Engineering, Harbin Institute of Technology, Harbin 150001, China
Weixing Zhou: School of Energy Science and Engineering, Harbin Institute of Technology, Harbin 150001, China
Daren Yu: School of Energy Science and Engineering, Harbin Institute of Technology, Harbin 150001, China

Energies, 2020, vol. 13, issue 18, 1-19

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.

Keywords: gas turbine; sensor fault diagnosis; machine learning; model based; Square Root Cubature Kalman Filter (SRCKF); Density-Based Spatial Clustering of Application with Noise (DBSCAN) (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/1996-1073/13/18/4976/pdf (application/pdf)
https://www.mdpi.com/1996-1073/13/18/4976/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:13:y:2020:i:18:p:4976-:d:417451

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().