Intelligent methodology for turbine engine diagnosis and health management

Author(s):  
Richard Hans Mgaya ◽  
Charles D. McCurry ◽  
Saleh Zein-Sabatto
Author(s):  
Allan J. Volponi

Engine diagnostic practices are as old as the gas turbine itself. Monitoring and analysis methods have progressed in sophistication over the past 6 decades as the gas turbine evolved in form and complexity. While much of what will be presented here may equally apply to both stationary power plants and aero-engines, the emphasis will be on aero propulsion. Beginning with primarily empirical methods centering around monitoring the mechanical integrity of the machine, the evolution of engine diagnostics has benefited from advances in sensing, electronic monitoring devices, increased fidelity in engine modeling and analytical methods. The primary motivation in this development is, not surprisingly, cost. The ever increasing cost of fuel, engine prices, spare parts, maintenance and overhaul, all contribute to the cost of an engine over its entire life cycle. Diagnostics can be viewed as a means to mitigate risk in decisions that impact operational integrity. This can have a profound impact on safety, such as In-Flight Shut Downs (IFSD) for aero applications, (outages for land based applications) and economic impact caused by Unscheduled Engine Removals (UERs), part life, maintenance and overhaul and the overall logistics of maintaining an aircraft fleet or power generation plants. This paper will review some of the methods used in the preceding decades to address these issues, their evolution to current practices and some future trends. While several different monitoring and diagnostic systems will be addressed, the emphasis in this paper will be centered on those dealing with the aero-thermodynamic performance of the engine.


Author(s):  
Allan J. Volponi

Engine diagnostic practices are as old as the gas turbine itself. Monitoring and analysis methods have progressed in sophistication over the past six decades as the gas turbine evolved in form and complexity. While much of what will be presented here may equally apply to both stationary power plants and aeroengines, the emphasis will be on aeropropulsion. Beginning with primarily empirical methods centered on monitoring the mechanical integrity of the machine, the evolution of engine diagnostics has benefited from advances in sensing, electronic monitoring devices, increased fidelity in engine modeling, and analytical methods. The primary motivation in this development is, not surprisingly, cost. The ever increasing cost of fuel, engine prices, spare parts, maintenance, and overhaul all contribute to the cost of an engine over its entire life cycle. Diagnostics can be viewed as a means to mitigate risk in decisions that impact operational integrity. This can have a profound impact on safety, such as in-flight shutdowns (IFSD) for aero applications, (outages for land-based applications) and economic impact caused by unscheduled engine removals (UERs), part life, maintenance and overhaul, and the overall logistics of maintaining an aircraft fleet or power generation plants. This paper will review some of the methods used in the preceding decades to address these issues, their evolution to current practices, and some future trends. While several different monitoring and diagnostic systems will be addressed, the emphasis in this paper will be centered on those dealing with the aerothermodynamic performance of the engine.


Author(s):  
L Wang ◽  
Y G Li ◽  
M F Abdul Ghafir ◽  
A Swingler

Gas turbine engine health management has become more and more important because of its ability to optimize the total gas turbine operation. Gas path fault classification is one of the most important techniques in gas turbine engine health management. In this article, a Rough Set-based gas turbine fault classification approach is introduced to enhance gas turbine engine health management by taking its advantages in selecting appropriate measurements for fault classification and dealing with uncertainties caused by measurement noise. In the approach, a Rough Set-based knowledge discovery tool is used to find the knowledge hidden in fault samples, and transfer the knowledge into rules representing the logical relationship between the faults and the fault signatures. Such rules can then be used by the Rough Set diagnostic approach to classify faults. Enhanced fault signatures, represented by the measurement deviations and their ranking pattern in terms of their magnitude, are used to make the diagnostic approach more effective. The Rough Set-based diagnostic approach was applied to a model two-spool turbofan gas turbine engine for the classification of single- and dual-component faults. The results show that such Rough Set-based diagnostic approach is able to classify complex-component faults accurately in the presence of measurement noise.


Author(s):  
Link Jaw ◽  
Yu-tsung (Jim) Wang ◽  
Richard Friend

Health management of a machine, such as a gas turbine engine, offers the potential benefits of efficient operations planning and the reduced cost of ownership. It requires a tight integration of major health management functions, such as trending, failure identification, forecasting, life prediction, operations and maintenance planning. This paper introduces a suite of plug-in tools that enhance the condition monitoring and health management capabilities of operational (or legacy) systems. One of these systems is the U. S. Air Force Comprehensive Engine Trending and Diagnostics System (CETADS), which has been used as the baseline for the development of the tools. These tools are collectively called the Intelligent Condition-based Engine/Equipment Management System (ICEMS). These tools are configured as software modules, which can be incorporated into an operational health management system individually or as a group. ICEMS modules implement the advanced algorithms containing artificial intelligence, statistical, model-based analysis techniques, and RCM practices. Although these modules have been developed and tested using data from the Pratt & Whitney F100-PW-220 engine in service at Luke Air Force Base, the modules are also generalized to cover many generic machines (or equipment).


Author(s):  
S. J. Hudak ◽  
B. R. Lanning ◽  
G. M. Light ◽  
K. S. Chan ◽  
J. A. Moryl ◽  
...  

The development and implementation of an integrated health management system has the potential to significantly enhance the reliability and readiness of high-value assets, while concurrently decreasing sustainment costs. A key aspect of this approach is on-board sensing to provide continual feedback on the evolving damage state at the material and component level. This paper summarizes the development and status of an embedded, thin-film, wireless, sensor for detecting and monitoring material damage state (i.e., cracking) in critical turbine engine components at elevated temperature. The potential benefits of on-board detection and monitoring of defects, as compared to periodic depot inspections, were previously assessed using probabilistic simulations. These results provided target sensitivities for the development of the thin-film sensor. The status of the sensor system is summarized including its ability to generate elastic waves and detect/monitor fatigue cracks in engineering materials at temperatures to 500°F (260°C). Crack detection sensitivities with and without load application are compared, as well as those for wired versus wireless signal transmission.


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