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.

Development of Fault Diagnosis and Failure Prediction Techniques for Small Gas Turbine Engines

2001 ◽  
Author(s):  
Craig R. Davison ◽  
A. M. Birk
Keyword(s):  
Fault Diagnosis ◽  
Gas Turbine ◽  
Operating Time ◽  
Turbine Engines ◽  
Time To Failure ◽  
Gas Turbine Engines ◽  
Ambient Conditions ◽  
Engine Operation ◽  
Maintenance Cycle ◽  
Prediction Techniques

A computer model of a gas turbine auxiliary power unit was produced to develop techniques for fault diagnosis and prediction of remaining life in small gas turbine engines. Due to the relatively low capital cost of small engines it is important that the techniques have both low capital and operating costs. Failing engine components were identified with fault maps, and an algorithm was developed for predicting the time to failure, based on the engine’s past operation. Simulating daily engine operation over a maintenance cycle tested the techniques for identification and prediction. The simulation included daily variations in ambient conditions, operating time, load, engine speed and operating environment, to determine the amount of degradation per day. The algorithm successfully adapted to the daily changes and corrected the operating point back to standard conditions to predict the time to failure.

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