The authors have developed model-based and data-driven techniques aimed at providing a more reliable health assessment of gas turbine engine accessory components, which have contributed to a significant number of events that compromise mission success and equipment availability in military aircraft. As part of this approach, a physical model is used to derive parameters indicative of component-specific faults. Statistical fault classifiers and evolutionary prognostics methods are then used to track these parameters and identify the most likely health state and time to failure for each component. This assessment is fused with the results of independent data-driven routines, which are also used to analyze dynamic signal response and detect faults that would be difficult to incorporate into physical models. The developed approach was demonstrated using an experimental setup representative of aircraft fuel and lubrication systems. Pump leakage, pump gear damage, and valve blockage were seeded on the setup, and the developed routines were trained with high-bandwidth experimental data. The approach produced wide separation between baseline and faulted cases, yielding negligible missed detection rates for moderate faults and reasonable missed detection rates for an incipient valve blockage fault. The demonstration produced a quantifiable estimate of achievable performance using the hybrid techniques.
Health Assessment of Shield Equipment Cutterhead Based on t-SNE Data-driven Model