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Research Papers: Gas Turbines: Controls, Diagnostics, and Instrumentation

Nonlinear Fault Diagnosis of Jet Engines by Using a Multiple Model-Based Approach

[+] Author and Article Information
E. Naderi

Department of Electrical and Computer Engineering,  Concordia University, Montreal, PQ, H3G 1M8, Canadae_naderi@ece.concordia.ca

N. Meskin

Department of Electrical Engineering,  Qatar University, Doha, Qatarnader.meskin@qu.edu.qa

K. Khorasani

Department of Electrical and Computer Engineering,  Concordia University, Montreal, PQ, H3G 1M8, Canadakash@ece.concordia.ca

J. Eng. Gas Turbines Power 134(1), 011602 (Nov 04, 2011) (10 pages) doi:10.1115/1.4004152 History: Received April 13, 2011; Revised April 15, 2011; Published November 04, 2011; Online November 04, 2011

In this paper, a nonlinear fault detection and isolation (FDI) scheme that is based on the concept of multiple model approach is proposed for jet engines. A modular and a hierarchical architecture is proposed which enables the detection and isolation of both single as well as concurrent permanent faults in the engine. A set of nonlinear models of the jet engine in which compressor and turbine maps are used for performance calculations corresponding to various operating modes of the engine (namely, healthy and different fault modes) is obtained. Using the multiple model approach, the probabilities corresponding to the engine modes of operation are first generated. The current operating mode of the system is then detected based on evaluating the maximum probability criteria. The performance of our proposed multiple model FDI scheme is evaluated by implementing both the extended Kalman filter and the unscented Kalman filter as detection filters. Simulation results presented demonstrate the effectiveness of our proposed multiple model FDI algorithm for both structural and actuator faults in the jet engine.

Copyright © 2012 by American Society of Mechanical Engineers
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Figures

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Figure 4

The mode probabilities corresponding to the injected 2% decrease in the compressor efficiency that is applied at t = 5 s (mode 3): (a) the UKF is used in the MM-based FDI scheme, and (b) the EKF is used in the MM-based FDI scheme

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Figure 5

The output measurements corresponding to the injected 2% decrease in the compressor efficiency that is applied at t = 5 s (mode 3)

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Figure 6

The mode probabilities corresponding to the injected 3% decrease (between the 2% and 5% modeled severities) in the turbine efficiency that is applied at t = 5 s (mode 5): (a) the UKF is used in the MM-based FDI scheme, and (b) the EKF is used in the MM-based FDI scheme

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Figure 7

The detection time for each mode of a fault that is applied at t = 5 s as a function of the fault severity: (a) the UKF is used in the MM-based FDI scheme, and (b) the EKF is used in the MM-based FDI scheme

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Figure 8

The mode probabilities corresponding to the gradual injection of a 3% decrease (between the 2% and 5% modeled severities) in the turbine efficiency that starts at t = 15 s (mode 5): (a) the UKF is used in the MM-based FDI scheme, and (b) the EKF is used in the MM-based FDI scheme

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Figure 9

The mode probabilities corresponding to the injected 2% decrease in the compressor efficiency that is applied at t = 5 s (mode 3) followed by an injection of a 2% decrease in the compressor mass flow rate (mode 2 in the second level) that is applied at t = 30 s. (a) The fault detection and isolation by the first level of filters using the UKF in the MM-based scheme. (b) The fault detection and isolation by the first level of filters using the EKF in the MM-based scheme. (c) The fault detection and isolation by the second level of filters using the UKF in the MM-based scheme. (d) The fault detection and isolation by the second level of filters using the EKF in the MM-based scheme.

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Figure 10

The output measurements corresponding to the injected 2% decrease in the compressor efficiency that is applied at t = 5 s (mode 3) followed by an injection of a 2% decrease in the compressor mass flow rate (mode 2 in the second level) that is applied at t = 30 s

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Figure 11

The mode probabilities corresponding to the injected 2% decrease in the turbine mass flow rate that is applied at t = 5 s (mode 4) while the ambient temperature is varying. (a) The UKF is used in the MM-based FDI scheme, and (b) the EKF is used in the MM-based FDI scheme.

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Figure 12

The output measurements corresponding to the injected 2% decrease in the turbine mass flow rate that is applied at t = 5 s (mode 4) while the ambient temperature is varying

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Figure 13

The output measurements corresponding to the injected 2% decrease in the turbine mass flow rate that is applied at t = 5 s (mode 4) while the PLA is varying

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Figure 14

The mode probabilities corresponding to the injected 2% decrease in the turbine mass flow rate that is applied at t = 5 s (mode 4) while the PLA is varying. (a) The UKF is used in the MM-based FDI scheme, and (b) the EKF is used in the MM-based FDI scheme.

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Figure 15

The detection time for each mode of a fault that is applied at t = 5 s as a function of the noise power factor. The empty places indicate the unsuccessful detection or isolation of the corresponding fault. (a) The UKF is used in the MM-based FDI scheme, and (b) the EKF is used in the MM-based FDI scheme.

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Figure 16

The detection time for each mode of a fault that is applied at t = 5 s as a function of the number of the measurements or sensors that are employed. (a) The UKF is used in the MM-based FDI scheme, and (b) the EKF is used in the MM-based FDI scheme.

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Figure 1

General architecture of our proposed MM-based FDI scheme

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Figure 2

Information flow diagram in a modular modeling of the jet engine dynamics

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Figure 3

Steady state series at PLAs ranging from 0.4 to 1 on the compressor performance map

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