Technical Briefs

Symbolic Dynamic Analysis of Transient Time Series for Fault Detection in Gas Turbine Engines

[+] Author and Article Information
Soumalya Sarkar

e-mail: svs5464@psu.edu

Kushal Mukherjee

Mem. ASME e-mail: kushal.mukherjee@gmail.com

Soumik Sarkar

Mem. ASME e-mail: sarkars@utrc.utc.com

Asok Ray

Fellow ASME e-mail: axr2@psu.eduDepartment of Mechanical Engineering, The Pennsylvania State University, University Park, PA 16802

1Present address: United Technologies Research Center, Cork, Ireland.

2Present address: United Technologies Research Center, East Hartford, USA.

Contributed by the Dynamic Systems Division of ASME for publication in the Journal of Dynamic Systems, Measurement, and Control. Manuscript received October 11, 2011; final manuscript received July 27, 2012; published online November 7, 2012. Assoc. Editor: Eugenio Schuster.

J. Dyn. Sys., Meas., Control 135(1), 014506 (Nov 07, 2012) (6 pages) Paper No: DS-11-1309; doi: 10.1115/1.4007699 History: Received October 11, 2011; Revised July 27, 2012

This brief paper presents a symbolic dynamics-based method for detection of incipient faults in gas turbine engines. The underlying algorithms for fault detection and classification are built upon the recently reported work on symbolic dynamic filtering. In particular, Markov model-based analysis of quasi-stationary steady-state time series is extended to analysis of transient time series during takeoff. The algorithms have been validated by simulation on the NASA Commercial Modular Aero Propulsion System Simulation (C-MAPSS) transient test-case generator.

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Grahic Jump Location
Fig. 1

Schematic diagram of the C-MAPSS engine model with sensors

Grahic Jump Location
Fig. 2

Detection in a multifault framework (ground truth: a faulty LPC)

Grahic Jump Location
Fig. 3

High-level fan fault detection (ground truth: a high-level fan fault)

Grahic Jump Location
Fig. 4

Low-level fan fault detection (ground truth: a low-level fan fault)

Grahic Jump Location
Fig. 5

ROC curves for fan fault identification with different test data lengths (λ varying between 0.01 and 2 with steps of 0.01)



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