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A strong tracking filter based multiple model approach for gas turbine fault diagnosis

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Abstract

In this paper, a nonlinear Fault detection and isolation (FDI) based on an improved Multiple model (MM) approach was proposed for the gas turbine engine. A bank of Strong tracking extended Kalman filters (STEKFs) was designed that enables robustness to model uncertainty and overcomes the shortcoming of the MM approach. The Jacobian matrix used in the filters was deduced by using the non-equilibrium analytic linearization method to improve the traditional method. Hierarchical fault detection and isolation architecture based on evaluating the maximum probability criteria were developed for both single and multiple faults. In addition, a nonlinear mode set automatic generation method that enables automatic generation of the modes of each level in the hierarchical architecture was also presented. Fault detection and isolation of a two-shaft marine gas turbine was studied in a simulation environment using the proposed STEKF-based MM approach and compared with the results of the traditional Extended Kalman filter (EKF) based MM approach. The results showed that the proposed approach not only has the advantages of the EKF-based MM approach but also robustness to the model uncertainty and overcomes the shortcomings of the MM approach.

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Authors and Affiliations

  1. College of Power and Energy Engineering, Harbin Engineering University, Harbin, 150001, China

    Qingcai Yang, Shuying Li & Yunpeng Cao

Authors
  1. Qingcai Yang
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  2. Shuying Li
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  3. Yunpeng Cao
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Corresponding author

Correspondence to Yunpeng Cao.

Additional information

Recommended by Associate Editor Tong Seop Kim

Qingcai Yang received his B.S. degree in Harbin Engineering University in 2013. He is currently a graduate student for Ph.D. degree of Power and Energy Engineering at Harbin Engineering University. Mr. Yang’s research interests are in the area of gas turbine performance simulation, gas turbine health estimation and gas path fault diagnosis.

Shuying Li received the B.S., M.S. and Ph.D. degrees in marine engineering, all from Harbin Engineering University in 1986, 1992 and 2000. She is a Professor of College of Power and Energy Engineering at Harbin Engineering University, China. Her research interests are in the areas of marine power plant performance and its control strategy, gas turbine engine fault diagnosis, advance measurement and testing.

Yunpeng Cao received the B.S. degree in Thermal Power Engineering from Hebei University of Technology in 2002, and the M.S., Ph.D. degrees in Marine Engineering, all from Harbin Engineering University in 2005, 2011. He is a Lecture of College of Power and Energy Engineering at Harbin Engineering University, China. His research interests are in the areas of marine power plant fault diagnosis and safety validation.

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Yang, Q., Li, S. & Cao, Y. A strong tracking filter based multiple model approach for gas turbine fault diagnosis. J Mech Sci Technol 32, 465–479 (2018). https://doi.org/10.1007/s12206-017-1248-0

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  • DOI: https://doi.org/10.1007/s12206-017-1248-0

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