Symbolic Dynamic Analysis of Transient Time Series for Fault Detection in Gas Turbine Engines-Reference-Cited by-同舟云学术

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

Published:2012-11-07 Issue:1 Volume:135 Page:
ISSN:0022-0434
Container-title:Journal of Dynamic Systems, Measurement, and Control
language:en
Short-container-title:

Author:

Sarkar Soumalya¹,Mukherjee Kushal²,Sarkar Soumik²,Ray Asok³

Affiliation:

1. e-mail:

2. Mem. ASME e-mail:

3. Fellow ASME e-mail: Department of Mechanical Engineering, The Pennsylvania State University, University Park, PA 16802

Abstract

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.

Publisher

ASME International

Subject

Computer Science Applications,Mechanical Engineering,Instrumentation,Information Systems,Control and Systems Engineering

Link

http://asmedigitalcollection.asme.org/dynamicsystems/article-pdf/doi/10.1115/1.4007699/6113818/ds_135_01_014506.pdf

Reference25 articles.

1. Application of Bayesian Forecasting to Change Detection and Prognosis of Gas Turbine Performance;ASME J. Eng. Gas Turbines Power,2010

2. Three-and Seven-Point Optimally Weighted Recursive Median Filters for Gas Turbine Diagnostics;Proc. Inst. Mech. Eng., Part G: J. Aerosp. Eng.,2008

3. A Gas Turbine Diagnostic Approach With Transient Measurements;Proc. Inst. Mech. Eng., Part A,2003

4. Fault Detection and Diagnosis in Gas Turbines;ASME J. Eng. Gas Turbines Power,1991

5. Wang, X., McDowell, N., Kruger, U., McCullough, G., and Irwin, G. W., 2008, “Semi-Physical Neural Network Model in Detecting Engine Transient Faults Using the Local Approach,” Proceedings of the 17th World Congress of the International Federation of Automatic Control (IFAC'08), July 6–11. 10.3182/20080706-5-KR-1001.01201

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