Using Diagnostics and Prognostics to Minimize the Cost of Ownership of Gas Turbines

Abstract

In general, health management technologies observe features associated with anomalous system behavior and relate these features to useful information about the system’s condition. In the case of prognostics, this information is then related to the expected condition at some future time. The ability to estimate the time to conditional or to mechanical failure is of great benefit in health management systems. Inherently probabilistic in nature, prognostics can be applied to system/component failure modes governed by material condition and by functional loss. Like diagnostic algorithms, prognostic algorithms tend to be generic in design but specific in application. Today, elements of turbine gas generator condition based maintenance, module and part life analysis, and soft removal times play essential roles in sustaining safe operations and effective equipment maintenance. When intelligently combined with value chain analysis they provide the decision support system needed to undertake the maintenance actions which minimize total cost of ownership. The methodologies and mathematical constructs for performing optimization require the system designer to clearly define a useful cost or objective function, which when minimized mathematically produces the parametric design combination that we call optimized. In the specific cases where parametric constraints exist, our optimized system typically will be found along those boundary conditions.