Gas-Turbine Engine Testing TRIZ Cases

Predicted future-period data builds a trend-aligned unit space for Mahalanobis plant monitoring, improving anomaly detection accuracy.

By simulating multiple rotary components with one circumferential mass, rotary disc balancing becomes faster and less error-prone.

Machine-learned processing of high-frequency compressor pressure signals enables faster, more accurate stall margin control in turbine engines.

Targeted radiation cures sealant beads at airfoil cooling holes, reducing material use and residual sealant while supporting maintenance.

Reduced order mistuning modeling predicts how blade blending changes IBR vibration, enabling safer repair limits and lower sustainment cost.

A trained classifier compares actual and modeled aircraft system outputs to detect operational differences earlier and cut revision time.

Filtered high-rate sensor signatures and machine learning help distinguish engine faults from degraded sensors and interference, reducing false positives.

High-rate sensor data is pre-filtered by fault signatures, then analyzed with machine learning to cut false positives in gas turbine monitoring.

Eddy current sensing tracks flange distance changes in a flexible coupling to calculate axial shaft load without contact or major retrofit.

Batch-made test pieces reveal mechanical properties so additive manufactured components can be assigned life-defining values with less testing.

Optical pit-field analysis links corrosion geometry and stress concentration to predict turbine engine residual life and trigger timely repair.

Independent usage data is cross-checked at the engine controller to validate component fatigue reporting and avoid premature maintenance.

Optical pit mapping captures full corrosion patterns and stress concentration changes to schedule remediation without removing equipment.

Using the sensing electrode as an active braze bonds ceramic parts, reducing thermal stress and internal capacitance in engine probes.

Spectral analysis of sensor signals predicts limit cycle amplitude and instability proximity in turbulent flow devices before oscillations grow.

Engine-history-based blend recommendations let damaged compressor airfoils be repaired in-engine while preserving structural integrity and reducing downtime.

Impact inspection and threshold-based repair help ceramic matrix composite turbine parts stay flightworthy after debris damage.

Moving averages of squared Mahalanobis distances suppress measurement noise and improve abnormality detection sensitivity in target devices.

A bendable, rotatable tubular assembly with a steering cable reaches confined turbomachine regions for in-situ inspection and repair.

Tracks fuel pump speed and fuel-actuated actuator position to predict wear-driven leakage and support on-condition replacement.

A movable fluid pipe section lets a turbomachine pylon dock safely, avoiding connection damage while accommodating vibration and thermal expansion.

Sensor pressure and vibration signals are converted into audible turbine sound, helping operators catch flutter, stall, or debris in real time.

Damaging pressure rise-drop cycles are converted into accumulated fatigue ratios to predict aircraft hydraulic life and trigger maintenance alerts.

Actual flight temperature, pressure, and speed data improve gas turbine component damage assessment and avoid premature replacement.

Pressure-load tracking maps hydraulic pressure swings to a damage model, helping estimate remaining aircraft apparatus life and plan maintenance.

A trained classifier compares expected and actual aircraft system outputs to flag operational differences earlier and cut development time.