Citation: Bazmi, F.; Rahimi, A.
Off-Design Analysis Method for
Compressor Fouling Fault Diagnosis
of Helicopter Turboshaft Engine.
Modelling 2023, 4, 56–69. https://
doi.org/10.3390/modelling4010005
Academic Editor: Sergey
Utyuzhnikov
Received: 19 December 2022
Revised: 13 January 2023
Accepted: 24 January 2023
Published: 28 January 2023
Copyright: © 2023 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
Article
Off-Design Analysis Method for Compressor Fouling Fault
Diagnosis of Helicopter Turboshaft Engine
Farshid Bazmi and Afshin Rahimi *
Department of Mechanical, Automotive and Materials Engineering, University of Windsor, 401 Sunset Ave.,
Windsor, ON N9B 3P4, Canada
* Correspondence: arahimi@uwindsor.ca
Abstract:
Fouling, caused by the adhesion of fine materials to the blades of the compressor’s last
stages, changes the airfoil’s shape and function and the inlet flow angle on the blades. As the fouling
increases, the range of influence increases, and the mass flow rate and overall engine efficiency
reduce. Therefore, the compressor is choked at lower speeds. This study aims to simulate compressor
performance during off-design conditions due to fouling and to present an approach for modeling
faults in diagnostic and health monitoring systems. A computational fluid dynamics analysis is
carried out to evaluate the proposed method on General Electric’s T700-GE turboshaft engine, and
the performance is evaluated at different flight conditions. The results show promising outcomes
with an average accuracy of 88% that would help future turboshaft health monitoring systems.
Keywords: helicopter; turboshaft; off-design; compressor; fouling; fault diagnostics
1. Introduction
When helicopters fly in harsh environments such as cities, deserts, or saltwater seas,
pollution, sand, salt, and moisture are among the destructive factors that the engine faces.
Fouling in compressors is one of the most common problems caused by these natural and
environmental factors. The consequences of fouling can severely affect the performance of
turboshaft engines.
Fouling, formed on the compressor blades, changes the airflow trajectories and reduces
the surge range [
1
]. This additionally increases the compressor’s sensitivity to instabilities.
Therefore, the airflow reaches a choked condition at lower speeds, generally measured
in revolutions per minute (RPMs) [
2
]. With the growth and continuation of fouling, the
mass flow rate (MFR) is more affected and causes a sharp drop in the compressor pressure
ratio (CPR), reducing the output power and increasing the specific fuel consumption (SFC).
Therefore, it is one of the essential factors in off-design performance and will determine the
performance range for the engine health monitoring (EHM) system [3].
With the advent of the new generation of variable-speed engines and efforts to increase
power and reduce fuel consumption and emissions, design requirements lead engineers to
a higher-pressure ratio and more accurate control systems in the compressor. Therefore,
it is necessary to develop diagnostic systems to synthesize the engine’s off-design perfor-
mance and quickly identify fouling in the compressor from other factors that can create
similar conditions.
In gas turbine fault diagnostics, engine manufacturers have devised several methods
over the past four decades [
4
,
5
]. Table 1 lists some of the fault diagnostic methods for
turboshaft engines. The ability to use data analysis in engines and proactively monitor
fouling progression is paramount. Thus, Saravanamuttoo et al. [
6
] conducted extensive and
fundamental research on the performance of clean and fouled compressors in various types,
levels, and locations of known faults. These studies led to the modeling and sensitivity
analysis of parameters affecting fouling.
Modelling 2023, 4, 56–69. https://doi.org/10.3390/modelling4010005 https://www.mdpi.com/journal/modelling
