Gas Turbine Performance Diagnostics Adaptation
Solution Overview
Problem
Traditional gas turbine engine performance diagnostics struggle to adapt to varying measurement suites and sensor failures or data dropouts, leading to gaps in analysis and the need for complex exception logic or software changes.
Innovation Solution
A method that adapts the measurement suite configuration using a measurement configuration matrix and a fault configuration matrix, based on a default engine configuration, to identify questionable parameters and adjust performance tracking calculations, ensuring robustness without requiring software changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional performance diagnostic estimation methods are used with fixed measurement suite requirements, then the diagnostic software can process complete data sets, but the system cannot adapt to sensor failures or data dropouts without complex exception logic
Solution Approach 1:
The measurement suite configuration is made dynamic through the use of a measurement suite configuration process that automatically adapts to available sensors at each time point. Instead of requiring a fixed complete measurement suite, the system dynamically adjusts which parameters are included in performance estimation based on what data is actually available, eliminating the need for complex exception handling logic.
Solution Approach 2:
The system changes the parameters included in the measurement suite based on sensor availability. The measurement suite configuration process modifies which measured parameters are included in performance estimation calculations depending on which sensors are functioning, allowing the diagnostic software to maintain reliability without requiring complex exception logic for handling missing data.
2Reliability
If the measurement suite is pre-selected for performance tracking, then the diagnostic process can proceed with complete data, but data dropouts cause gaps in analysis
Solution Approach 1:
The measurement suite is transformed from a static pre-selected set to a dynamic configuration that adjusts at each time point based on sensor availability. This allows the performance estimation process to continue without gaps by adapting to which parameters are actually available, preventing analysis interruptions caused by data dropouts.
Solution Approach 2:
The measurement suite configuration process automatically detects which sensors are available and self-adjusts the measurement suite accordingly, without requiring external intervention or manual reconfiguration. This self-adapting mechanism ensures continuous performance tracking even when sensors fail or data dropouts occur.
3Adaptability or versatility
If generic performance tracking methods are applied to different gas turbine models, then broad applicability is achieved, but adaptation to specific measurement configurations requires software changes
Solution Approach 1:
The measurement suite configuration process serves as a universal adapter layer between the generic performance estimation software and various specific measurement configurations. This single universal process handles adaptation to different gas turbine models and sensor configurations without requiring separate software implementations for each case.
Solution Approach 2:
The measurement suite configuration process acts as an intermediary between the fixed generic performance estimation algorithms and the variable measurement suites of different gas turbine models. It translates between the two, allowing the software to maintain its generic nature while adapting to specific measurement configurations without requiring software changes.
4Measurement precision
If comprehensive gas path measurements are collected for all engine types, then complete performance diagnostics can be performed, but the measurement suite becomes incompatible across different engine models
Solution Approach 1:
The measurement suite is segmented into optional parameters rather than requiring a complete fixed set. The measurement suite configuration process selects which segments (parameters) to include based on what is available for each specific engine model, allowing comprehensive diagnostics where possible while maintaining compatibility across different engine types.
Solution Approach 2:
Each parameter in the measurement suite is treated with local quality, meaning its inclusion or exclusion is determined independently based on local sensor availability rather than requiring uniform inclusion of all parameters across all engine models. This allows the system to maintain high measurement precision for available parameters while adapting to different engine configurations.
Data Source
AI summary
A method and system is described that estimates gas turbine engine module performance changes from gas path measurement data using a linear predictor-corrector estimation process. A measurement configuration matrix MC and a fault configuration FC matrix are assembled. The configuration matrices MC and FC are used to adapt a typical linear predictor-corrector type of performance estimation process to a given measurement suite and are ideally suited for linear estimation systems such as those using a Kalman filter.