scholarly journals Fault detection and isolation in aircraft gas turbine engines. Part 1: Underlying concept

2008 ◽  
Vol 222 (3) ◽  
pp. 307-318 ◽  
Author(s):  
S Gupta ◽  
A Ray ◽  
S Sarkar ◽  
M Yasar
Keyword(s):  
Fault Detection ◽  
Gas Turbine ◽  
Fault Detection And Isolation ◽  
Turbine Engines ◽  
Gas Turbine Engines

scholarly journals A CASE STUDY OF VIBRATION FAULT DIAGNOSIS APPLIED AT ROLLS-ROYCE T-56 TURBOPROP ENGINE

Aviation ◽  
2020 ◽  
Vol 23 (3) ◽  
pp. 78-82
Author(s):  
Christos Skliros
Keyword(s):  
Fault Diagnosis ◽  
Fault Detection ◽  
Gas Turbine ◽  
Fault Detection And Isolation ◽  
Turbine Engines ◽  
Gas Turbine Engines ◽  
Root Cause ◽  
Significant Challenge ◽  
Turboprop Engine

Gas turbine engines include a plethora of rotating modules, and each module consists of numerous components. A component’s mechanical fault can result in excessive engine vibrations. Identification of the root cause of a vibration fault is a significant challenge for both engine manufacturers and operators. This paper presents a case study of vibration fault detection and isolation applied at a Rolls-Royce T-56 turboprop engine. In this paper, the end-to-end fault diagnosis process from starting system faults to the isolation of the engine’s shaft that caused excessive vibrations is described. This work contributes to enhancing the understanding of turboprop engine behaviour under vibration conditions and highlights the merit of combing information from technical logs, maintenance manuals and engineering judgment in successful fault diagnosis.

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

2012 ◽  
Vol 135 (1) ◽  
Author(s):  
Soumalya Sarkar ◽  
Kushal Mukherjee ◽  
Soumik Sarkar ◽  
Asok Ray
Keyword(s):  
Time Series ◽  
Fault Detection ◽  
Gas Turbine ◽  
Symbolic Dynamic ◽  
Turbine Engines ◽  
Test Case ◽  
Gas Turbine Engines ◽  
Transient Time ◽  
Steady State Time ◽  
Dynamic Filtering

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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