0
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.

FIGURES IN THIS ARTICLE
<>
Copyright © 2013 by ASME
Your Session has timed out. Please sign back in to continue.

References

Urban, L. A., 1972, “Gas Path Analysis Applied to Turbine Engine Condition Monitoring,” AIAA/SAE 8th Joint Propulsion Specialist Conference, New Orleans, LA, November 29–December 2, AIAA Paper No. 72-1082. [CrossRef]
Doel, D. L., 1994, “An Assessment of Weighted-Least-Squares-Based Gas Path Analysis,” ASME J. Eng. Gas Turbines Power, 116, pp. 366–373. [CrossRef]
Doel, D., 1994, “TEMPER—A Gas-Path Analysis Tool for Commercial Jet Engines,” ASME J. Eng. Gas Turbines Power, 116, pp. 82–89. [CrossRef]
Doel, D. L., 2003, “Interpretation of Weighted-Least-Squares Gas Path Analysis Results,” ASME J. Eng. Gas Turbines Power, 125, pp. 624–632. [CrossRef]
Stamatis, A., Mathioudakis, K., and Papailiou, K., 1990, “Adaptive Simulation of Gas Turbine Performance,” ASME J. Eng. Gas Turbines Power, 112, pp. 168–175. [CrossRef]
Stamatis, A., Mathioudakis, K., Papailiou, K., and Smith, M., 1990, “Gas Turbine Component Fault Identification by Means of Adaptive Performance Modeling,” ASME Paper No. 90-GT-376.
Sampath, S., Gulati, A., and Singh, R., 2002, “Fault Diagnostics Using Genetic Algorithm for Advanced Cycle Gas Turbine,” ASME Paper No. GT2002-30021. [CrossRef]
Sampath, S., and Singh, R., 2006, “An Integrated Fault Diagnostics Model Using Genetic Algorithm and Neural Networks,” ASME J. Eng. Gas Turbines Power, 128, pp. 49–56. [CrossRef]
Li, Y., 2008, “A Genetic Algorithm Approach to Estimate Performance Status of Gas Turbines,” ASME Paper No. GT2008-50175. [CrossRef]
Sugiyama, N., 2000, “System Identification of Jet Engines,” ASME J. Eng. Gas Turbines Power, 122, pp. 19–26. [CrossRef]
Volponi, A., DePold, H., Ganguli, R., and Daguang, C., 2003, “The Use of Kalman Filter and Neural Network Methodologies in Gas Turbine Performance Diagnostics: A Comparative Study,” ASME J. Eng. Gas Turbines Power, 125, pp. 917–924. [CrossRef]
Lu, P. J., Zhang, M. C., Hsu, T. C., and Zhang, J., 2001, “An Evaluation of Engine Faults Diagnostics Using Artificial Neural Networks,” ASME J. Eng. Gas Turbines Power, 123, pp. 340–346. [CrossRef]
Romesis, C., and Mathioudakis, K., 2003, “Setting up of a Probabilistic Neural Network for Sensor Fault Detection Including Operation With Component Faults,” ASME J. Eng. Gas Turbines Power, 125, pp. 634–641. [CrossRef]
Bettocchi, R., Pinelli, M., Spina, P., and Venturini, M., 2007, “Artificial Intelligence for the Diagnostics of Gas Turbines—Part I: Neural Network Approach,” ASME J. Eng. Gas Turbines Power, 129, pp. 711–719. [CrossRef]
Romessis, C., and Mathioudakis, K., 2006, “Bayesian Network Approach for Gas Path Fault Diagnosis,” ASME J. Eng. Gas Turbines Power, 128, pp. 64–72. [CrossRef]
Lee, Y. K., Mavris, D. N., Volovoi, V. V., Yuan, M., and Fisher, T., 2010, “A Fault Diagnosis Method for Industrial Gas Turbines Using Bayesian Data Analysis,” ASME J. Eng. Gas Turbines Power, 132, p. 041602. [CrossRef]
Eustace, R. W., 2008, “A Real-World Application of Fuzzy Logic and Influence Coefficients for Gas Turbine Performance Diagnostics,” ASME J. Eng. Gas Turbines Power, 130, p. 061601. [CrossRef]
Volponi, A., 2003, “Foundation of Gas Path Analysis (Part I and II),” Gas Turbine Condition Monitoring and Fault Diagnosis (von Karman Institute Lecture Series No. 1), von Karman Institute, Rhode-Saint-Genèse, Belgium.
Kamboukos, P., and Mathioudakis, K., 2005, “Comparison of Linear and Nonlinear Gas Turbine Performance Diagnostics,” ASME J. Eng. Gas Turbines Power, 127, pp. 49–56. [CrossRef]
Lipowsky, H., Staudacher, S., Bauer, M., and Schmidt, K. J., 2010, “Application of Bayesian Forecasting to Change Detection and Prognosis of Gas Turbine Performance,” ASME J. Eng. Gas Turbines Power, 132, p. 031602. [CrossRef]
Aretakis, N., Mathioudakis, K., and Stamatis, A., 2003, “Nonlinear Engine Component Fault Diagnosis From a Limited Number of Measurements Using a Combinatorial Approach,” ASME J. Eng. Gas Turbines Power, 125, pp. 642–650. [CrossRef]
Donoho, D. L., 2006, “Compressed Sensing,” IEEE Trans. Inf. Theory, 52(4), pp. 1289–1306. [CrossRef]
Yang, A. Y., Wright, J., Ma, Y., and Sastry, S. S., 2007, “Feature Selection in Face Recognition: A Sparse Representation Perspective,” UC Berkeley Tech Report No. UCB/EECS-2007-99.
Zhang, B., Karray, F., Li, Q., and Zhang, L., 2012, “Sparse Representation Classifier for Microaneurysm Detection and Retinal Blood Vessel Extraction,” Information Sci., 200(1), pp. 78–90. [CrossRef]
Chen, S. S., Donoho, D. L., and Saunders, M. A., 1999, “Atomic Decomposition by Basis Pursuit,” SIAM J. Sci. Comput., 20(1), pp. 33–61. [CrossRef]
Tropp, J., and Gilbert, A. C., 2007, “Signal Recovery From Partial Information Via Orthogonal Matching Pursuit,” IEEE Trans. Inform. Theory, 53(12), pp. 4655–4666. [CrossRef]
Borguet, S., and Léonard, O., 2010, “A Sparse Estimation Approach to Fault Isolation,” ASME J. Eng. Gas Turbines Power, 132, p. 021601. [CrossRef]
Borguet, S., and Léonard, O., 2011, “Constrained Sparse Estimation for Improved Fault Isolation,” ASME J. Eng. Gas Turbines Power, 133, p. 121602. [CrossRef]
Ji, S., Xue, Y., and Carin, L., 2008, “Bayesian Compressive Sensing,” IEEE Trans. Signal Process, 56, pp. 2346–2356. [CrossRef]
Wipf, D., and Rao, B., 2005, “ℓ0-Norm Minimization for Basis Selection,” Advances in Neural Information Processing Systems 17, L. K. Saul, Y. Weiss, and L. Bottou, eds., MIT Press, Cambridge, MA, pp. 1513–1520.
Wipf, D., and Rao, B., 2006, “Comparing the Effects of Different Weight Distributions on Finding Sparse Representations,” Advances in Neural Information Processing Systems 18, Y. Weiss, B. Schölkopf, J. Platt, eds., MIT Press, Cambridge, MA, pp. 1521–1528.
Fuchs, J. J., 2004, “Recovery of Exact Sparse Representations in the Presence of Noise,” IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP'04), Montreal, Canada, May 17–21, Vol. 2, pp. 533–536. [CrossRef]
Fuchs, J. J., 2004, “On Sparse Representations in Arbitrary Redundant Bases,” IEEE Trans. Inf. Theory, 50, pp. 1341–1344. [CrossRef]
Tipping, M. E., 2001, “Sparse Bayesian Learning and the Relevance Vector Machine,” J. Mach. Learn. Res., 1, pp. 211–244. [CrossRef]
Jansen, M., Schulenberg, T., and Waldinger, D., 1992, “Shop Test Result of the V64.3 Gas Turbine,” ASME J. Eng. Gas Turbines Power, 114, pp. 676–681. [CrossRef]
Camporeale, S., Fortunato, B., and Mastrovito, M., 2006, “A Modular Code for Real Time Dynamic Simulation of Gas Turbines in Simulink,” ASME J. Eng. Gas Turbines Power, 128, pp. 506–517. [CrossRef]
Pinelli, M., and Spina, P., 2002, “Gas Turbine Field Performance Determination: Sources of Uncertainties,” ASME J. Eng. Gas Turbines Power, 124, pp. 155–160. [CrossRef]
Zwebek, A., and Pilidis, P., 2003, “Degradation Effects on Combined Cycle Power Plant Performance—Part I: Gas Turbine Cycle Component Degradation Effects,” ASME J. Eng. Gas Turbines Power, 125, pp. 651–657. [CrossRef]
Tipping, M. E., 2009, “SPARSEBAYES V1.1: A Baseline Matlab Implementation of ‘Sparse Bayesian’ Model Estimation.”
Wipf, D. P., and Rao, B. D., 2004, “Sparse Bayesian Learning for Basis Selection,” IEEE Trans. Signal Process., 52(8), pp. 2153–2164. [CrossRef]

Figures

Grahic Jump Location
Fig. 1

A visualization of student-t prior and a Gaussian

Grahic Jump Location
Fig. 2

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

Grahic Jump Location
Fig. 3

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

Grahic Jump Location
Fig. 4

Comparison of the SBL and BP for a bias of Pe

Grahic Jump Location
Fig. 5

Comparison of the SBL convergence result with different initial parameters

Tables

Errata

Discussions

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related Journal Articles
Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In