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

Sparse Bayesian Learning for Gas Path Diagnostics

[+] Author and Article Information
Shangming Liu

e-mail: liushm@mail.tsinghua.edu.cn

Hongde Jiang

Key Laboratory for Thermal Science
and Power Engineer of Ministry of Education,
Tsinghua University,
100084 Beijing, China

Daren Yu

School of Energy Science and Engineering,
Harbin Institute of Technology,
150001 Heilongjiang, China
e-mail: yudaren@hit.edu.cn

Contributed by the Education Committee of ASME for publication in the Journal of Engineering for Gas Turbines and Power. Manuscript received September 13, 2012; final manuscript received February 1, 2013; published online June 12, 2013. Assoc. Editor: Allan Volponi.

J. Eng. Gas Turbines Power 135(7), 071601 (Jun 12, 2013) (8 pages) Paper No: GTP-12-1357; doi: 10.1115/1.4023608 History: Received September 13, 2012; Revised February 01, 2013

A gas path diagnostic method based on sparse Bayesian learning is presented. Most gas path diagnostic problems present the case where there are fewer measurements than health parameters. In addition, the measurement readings can be faulty themselves and need to be determined, which further increases the number of unknown variables. The number of unknown variables exceeds the number of measurements in gas path diagnostics, making the estimation problem underdetermined. For gradual deterioration, it is common to apply a weighted-least-square algorithm to estimate the component health parameters at the same time sensor errors are being determined. However, this algorithm may underestimate the real problem and attribute parts of it to other component faults for accidental single fault events. The accidental single fault events impact at most one or two component(s). This translates mathematically into the search for a sparse solution. In this paper, we proposed a new gas path diagnostic method based on sparse Bayesian learning favoring sparse solutions for accidental single fault events. The sparse Bayesian learning algorithm is applied to a heavy-duty gas turbine considering component faults and sensor biases to demonstrate its capability and improved performance in gas path diagnostics.

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Figures

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Fig. 1

A visualization of student-t prior and a Gaussian

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Fig. 2

The convergence process of σ2, α1, and α3 for a case of compressor fault

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Fig. 3

The value of J of SBL and BP for bias of Pe

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Fig. 4

Comparison of the SBL and BP for a bias of Pe

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Fig. 5

Comparison of the SBL convergence result with different initial parameters

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