Compressor Stall Prediction via FFT Pressure Analysis
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Solution Overview
Problem
Current methods for detecting compressor stall and surge in gas turbines lack predictive capabilities and fail to provide sufficient lead time for real-time control, leading to potential compressor damage due to undetected aerodynamic instabilities.
Innovation Solution
A system that obtains dynamic pressure and speed signals from a compressor rotor, filters them using blade passing frequency, buffers the data, and analyzes it using Fast Fourier Transform to predict stall conditions, enabling proactive control measures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If pressure variations are monitored to detect compressor stall, then real-time detection capability is improved, but predictive capability and lead time for control actions remain insufficient
Solution Approach 1:
The system performs preliminary action by analyzing pressure signal characteristics before actual stall occurs. The Fast Fourier Transform analysis identifies predictive features in the pressure signal that indicate impending stall conditions, allowing control systems to take corrective action before the stall fully develops, thus resolving the contradiction between detection capability and lead time.
Solution Approach 2:
The patent introduces an intermediary analysis layer using Fast Fourier Transform to process pressure signals. This intermediary transformation converts the raw pressure signal into frequency domain representations, enabling extraction of predictive features that are not apparent in the time domain, thereby providing advance warning of stall conditions.
2Reliability
If pressure monitoring is used to detect compressor stall, then detection capability is improved, but measurement precision is insufficient to distinguish between different causes of pressure variations
Solution Approach 1:
The patent applies segmentation by dividing the pressure signal into frequency components through Fast Fourier Transform. This segmentation allows distinct frequency signatures to be identified and attributed to different causes (combustion instability, rotating stall, surge), thereby improving measurement precision while maintaining detection capability.
Solution Approach 2:
The patent transitions from time-domain pressure measurements to frequency-domain analysis using Fast Fourier Transform. This dimensional change enables differentiation between various causes of pressure variations by their characteristic frequency signatures, thereby improving measurement precision without sacrificing detection reliability.
3Reliability
If comprehensive monitoring is implemented to detect all compressor conditions, then detection capability is improved, but device complexity increases
Solution Approach 1:
The patent applies universality by using a single Fast Fourier Transform-based analysis system to detect multiple different compressor conditions (combustion instability, rotating stall, surge) through their distinct frequency signatures. This multi-functional approach improves detection capability while avoiding the complexity of separate monitoring systems for each condition.
Data Source
AI summary
A method for monitoring a compressor comprising a rotor is presented. The method comprises obtaining a dynamic pressure signal of the rotor, obtaining a blade passing frequency of the rotor, using the blade passing frequency signal for filtering the dynamic pressure signal, buffering the filtered dynamic pressure signal over a moving window time period, and analyzing the buffered dynamic pressure signal to predict a stall condition of the compressor.