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

A Fault Diagnosis Method for Industrial Gas Turbines Using Bayesian Data Analysis

[+] Author and Article Information
Young K. Lee1

School of Aerospace Engineering, Georgia Institute of Technology, Atlanta, GA 30332-0150young.lee@aerospace.gatech.edu

Dimitri N. Mavris

School of Aerospace Engineering, Georgia Institute of Technology, Atlanta, GA 30332-0150dmavris@ae.gatech.edu

Vitali V. Volovoi

School of Aerospace Engineering, Georgia Institute of Technology, Atlanta, GA 30332-0150vvolovoi@ae.gatech.edu

Ming Yuan

School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA 30332-0205myuan@isye.gatech.edu

Ted Fisher

Service Engineering,  GE Energy, Atlanta, GA 30339-8402ted.fisher@ge.com

1

Corresponding author.

J. Eng. Gas Turbines Power 132(4), 041602 (Jan 15, 2010) (6 pages) doi:10.1115/1.3204508 History: Received March 04, 2009; Revised June 02, 2009; Published January 15, 2010; Online January 15, 2010

This paper presents an offline fault diagnosis method for industrial gas turbines in a steady-state. Fault diagnosis plays an important role in the efforts for gas turbine owners to shift from preventive maintenance to predictive maintenance, and consequently to reduce the maintenance cost. Ever since its birth, numerous techniques have been researched in this field, yet none of them is completely better than the others and perfectly solves the problem. Fault diagnosis is a challenging problem because there are numerous fault situations that can possibly happen to a gas turbine, and multiple faults may occur in multiple components of the gas turbine simultaneously. An algorithm tailored to one fault situation may not perform well in other fault situations. A general algorithm that performs well in overall fault situations tends to compromise its accuracy in the individual fault situation. In addition to the issue of generality versus accuracy, another challenging aspect of fault diagnosis is that, data used in diagnosis contain errors. The data is comprised of measurements obtained from gas turbines. Measurements contain random errors and often systematic errors like sensor biases as well. In this paper, to maintain the generality and the accuracy together, multiple Bayesian models tailored to various fault situations are implemented in one hierarchical model. The fault situations include single faults occurring in a component, and multiple faults occurring in more than one component. In addition to faults occurring in the components of a gas turbine, sensor biases are explicitly included in the multiple models so that the magnitude of a bias, if any, can be estimated as well. Results from these multiple Bayesian models are averaged according to how much each model is supported by data. Gibbs sampling is used for the calculation of the Bayesian models. The presented method is applied to fault diagnosis of a gas turbine that is equipped with a faulty compressor and a biased fuel flow sensor. The presented method successfully diagnoses the magnitudes of the compressor fault and the fuel flow sensor bias with limited amount of data. It is also shown that averaging multiple models gives rise to more accurate and less uncertain results than using a single general model. By averaging multiple models, based on various fault situations, fault diagnosis can be general yet accurate.

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Copyright © 2010 by American Society of Mechanical Engineers
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Figures

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Figure 1

“Spike and slab” prior

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Figure 2

Graphical model of the current formulation

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Figure 3

Network consisting of the health parameters, sensor biases, and measurements

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Figure 4

Posterior of the compressor efficiency parameter with various numbers of data points (vertical lines: true values)

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Figure 5

Gibbs samples in the XCE-BCDT coordinate

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Figure 6

Posteriors of the compressor flow parameter and the fuel flow sensor bias (vertical lines: true values)

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Figure 7

Model posterior distribution

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Figure 8

Comparison of the true, full, and Bayesian averaged models (vertical lines: true values)

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