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Contextual normalization applied to aircraft gas turbine engine diagnosis

Turney, Peter and Halasz, Michael (1993) Contextual normalization applied to aircraft gas turbine engine diagnosis. [Journal (Paginated)]

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Abstract

Diagnosing faults in aircraft gas turbine engines is a complex problem. It involves several tasks, including rapid and accurate interpretation of patterns in engine sensor data. We have investigated contextual normalization for the development of a software tool to help engine repair technicians with interpretation of sensor data. Contextual normalization is a new strategy for employing machine learning. It handles variation in data that is due to contextual factors, rather than the health of the engine. It does this by normalizing the data in a context-sensitive manner. This learning strategy was developed and tested using 242 observations of an aircraft gas turbine engine in a test cell, where each observation consists of roughly 12,000 numbers, gathered over a 12 second interval. There were eight classes of observations: seven deliberately implanted classes of faults and a healthy class. We compared two approaches to implementing our learning strategy: linear regression and instance-based learning. We have three main results. (1) For the given problem, instance-based learning works better than linear regression. (2) For this problem, contextual normalization works better than other common forms of normalization. (3) The algorithms described here can be the basis for a useful software tool for assisting technicians with the interpretation of sensor data.

Item Type:Journal (Paginated)
Keywords:machine learning, engine diagnosis, machinery condition monitoring, normalization, robust classification.
Subjects:Computer Science > Artificial Intelligence
Computer Science > Machine Learning
Computer Science > Statistical Models
ID Code:1864
Deposited By:Turney, Peter
Deposited On:11 Nov 2001
Last Modified:11 Mar 2011 08:54

References in Article

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1. M. Halasz, P. Davidson, S. Abu-Hakima, and S. Phan, “JETA: A knowledge-based approach

to aircraft gas turbine engine maintenance,” Journal of Applied Intelligence, vol. 2, pp. 25-46,

1992.

2. D. Kibler, D.W. Aha, and M.K. Albert, “Instance-based prediction of real-valued attributes,”

Computational Intelligence, vol. 5, pp. 51-57, 1989.

3. D.W. Aha, D. Kibler, and M.K. Albert, “Instance-based learning algorithms,” Machine

Learning, vol. 6, pp. 37-66, 1991.

4. B.V. Dasarathy, Nearest Neighbor Pattern Classification Techniques, edited collection, IEEE

Press: Los Alamitos, CA, 1991.

5. N.R. Draper and H. Smith, Applied Regression Analysis, second edition, John Wiley & Sons:

New York, NY, 1981.

6. D.A.S. Fraser, Probability and Statistics: Theory and Applications, Duxbury Press: North

Scituate, MA, 1976.

7. Precision Visuals, Inc., PV-WAVE 3.0 Technical Reference Manual: Workstation Analysis and

Visualization Environment, Precision Visuals: Boulder, CO, 1990.

8. G.S. Dell, “Positive feedback in hierarchical connectionist models: Applications to language

production,” in Connectionist Models and Their Implications, edited by D. Waltz and J.A.

Feldman, Ablex Publishing: Princeton, NJ, pp. 97-117, 1988.

9. R.M. Golden, “A developmental neural model of visual word perception,” in Connectionist

Models and Their Implications, edited by D. Waltz and J.A. Feldman, Ablex Publishing:

Princeton, NJ, pp. 119-154, 1988.

10. W.E. Dietz, E.L. Kiech, and M. Ali, “Jet and rocket engine fault diagnosis in real time,”

Journal of Neural Network Computing, pp. 5-18, 1989.

11. D.B. Malkoff, “A neural network for real-time signal processing,” Advances in Neural Infor-mation

Processing Systems 2, edited by D.S. Touretzky, Morgan Kaufmann: San Mateo, CA,

pp. 248-255, 1990.

12. G.J. Montgomery, “Abductive Diagnostics,” A Collection of Technical Papers: AIAA

Computers in Aerospace VII Conference, Monterey, CA, vol. 1, pp. 267-275, 1989.

13. A.J. Katz, M.T. Gately, and D.R. Collins, “Robust classifiers without robust features,” Neural

Computation, vol. 2, pp. 472-479, 1990.

14. J.R. Quinlan, “Induction of decision trees,” Machine Learning, vol. 1, pp. 81-106, 1986.

15. D.E. Goldberg, Genetic Algorithms in Search, Optimization, and Machine Learning,

Addison-Wesley: Reading, MA, 1989.

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