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.
The goal of Gas Turbine Performance Diagnostics is to accurately detect, isolate and assess the changes in engine module performance, engine system malfunctions and instrumentation problems from knowledge of measured parameters taken along the engine’s gas path. Discernable shifts in engine speeds, temperatures, pressures, fuel flow, etc., provide the requisite information for determining the underlying shift in engine operation from a presumed nominal state. Historically, this type of analysis was performed through the use of a Kalman Filter or one of its derivatives to simultaneously estimate a plurality of engine faults. In the past decade, Artificial Neural Networks (ANN) have been employed as a pattern recognition device to accomplish the same task. Both methods have enjoyed a reasonable success.
The goal of gas turbine performance diagnositcs is to accurately detect, isolate, and assess the changes in engine module performance, engine system malfunctions and instrumentation problems from knowledge of measured parameters taken along the engine’s gas path. The method has been applied to a wide variety of commercial and military engines in the three decades since its inception as a diagnostic tool and has enjoyed a reasonable degree of success. During that time many methodologies and implementations of the basic concept have been investigated ranging from the statistically based methods to those employing elements from the field of artificial intelligence. The two most publicized methods involve the use of either Kalman filters or artificial neural networks (ANN) as the primary vehicle for the fault isolation process. The present paper makes a comparison of these two techniques.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.