Gas Turbine Start Detection With Delayed Speed Comparison
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Solution Overview
Problem
Detecting abnormal starts of gas turbine engines is resource-intensive for automated engine control systems, requiring efficient methods to reduce computing power and storage requirements while effectively identifying ignition failures.
Innovation Solution
A method and system for detecting abnormal engine starts using an engine controller that samples data from sensors at a lower rate than the reporting rate, discards older data points, and compares speed data points separated by a predetermined time delay to determine abnormal start events, reducing computational and storage needs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sensor data is sampled at the full reporting rate for abnormal start detection, then detection accuracy is improved, but computational and storage requirements increase
Solution Approach 1:
The patent segments the sensor data processing by dividing it into different sampling rates based on the operational phase. During critical start-up phases, data is sampled at the full reporting rate for high accuracy, while during steady-state operation, sampling occurs at a reduced rate. This segmentation allows the system to maintain detection accuracy when needed while reducing overall computational and storage burden.
Solution Approach 2:
The patent applies partial action by selectively applying high-rate sampling only to specific critical parameters and time periods during engine operation. Instead of continuously sampling all sensor data at full reporting rate, the system applies intensive sampling only when abnormal start conditions are suspected or during critical transition phases, thereby maintaining detection capability while reducing overall data processing requirements.
2Reliability
If all sensor data points are stored for analysis, then detection reliability is improved, but storage requirements increase
Solution Approach 1:
The patent extracts and stores only the critical data points and features necessary for abnormal start detection rather than retaining all sensor data. The system identifies and extracts key parameters such as acceleration rates, temperature gradients, and pressure changes that are most indicative of abnormal starts, discarding redundant information while maintaining detection reliability.
Solution Approach 2:
The patent implements a selective discarding strategy where data points that have been processed and analyzed are discarded after their diagnostic value is extracted. The system recovers and retains only those data points that indicate abnormal conditions or that are necessary for trend analysis, thereby maintaining detection reliability while minimizing storage requirements through systematic data lifecycle management.
Data Source
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AI summary
Method for detecting an abnormal start of a gas turbine engine (100) are described. Speed data points are sampled from a sensor (105) associated with the engine (100) in accordance with a sampling rate, the speed data points being indicative of a rotational speed of a gas generator of the engine (100) during engine start. The speed data points are continuously stored during the engine start. Previously-obtained speed data points which are older than an abnormal start delay are discarded. An abnormal engine start event is detected by comparing a first one of the stored speed data points with a second one of the stored speed data points, the second one of the stored speed data points obtained before the first one.