Compressor Anomaly Prediction via Permutation Entropy
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Gas turbine systems experience anomalies like stall, surge, and instability in the compressor due to wear and tear, leading to decreased efficiency and costly maintenance, as existing prediction methods are inadequate for timely recognition and prevention of these issues.
Innovation Solution
A system utilizing pressure sensors to generate high-speed time-series signals between compressor blade tips and the casing, which are processed to determine permutation entropy patterns, allowing for prediction and categorization of anomalies, enabling proactive corrective actions to minimize or avoid these issues.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional monitoring methods are used for compressor anomalies, then the system structure remains simple, but the anomaly detection precision and timeliness are insufficient
Solution Approach 1:
The patent replaces traditional mechanical monitoring methods with signal processing and permutation entropy analysis. Pressure signals are transformed into permutation entropy values that quantify signal complexity, enabling precise anomaly detection without complex mechanical sensors or invasive measurements
Solution Approach 2:
The patent transforms pressure signals into permutation entropy parameters to detect anomalies. By changing the parameter from raw pressure values to permutation entropy (which measures signal complexity), the system achieves sensitive anomaly detection while maintaining relatively simple implementation
2Measurement precision
If permutation entropy analysis is implemented for anomaly prediction, then the anomaly detection precision improves, but the computational complexity increases
Solution Approach 1:
The patent applies permutation entropy analysis selectively to pressure signals from critical compressor stages rather than processing all sensor data comprehensively. This partial application achieves effective anomaly detection while limiting computational complexity to necessary calculations only
3Reliability
If continuous monitoring of compressor pressure signals is performed, then the reliability of anomaly prediction improves, but the energy consumption increases
Solution Approach 1:
The patent implements continuous monitoring of pressure signals through the compressor system, maintaining reliable anomaly detection capability. The permutation entropy calculation processes signals continuously to provide ongoing reliability assessment while energy consumption is managed through efficient signal processing algorithms
4Loss of information
If multiple pattern categories are analyzed for anomaly classification, then the information completeness improves, but the difficulty of detecting and measuring increases
Solution Approach 1:
The patent segments anomaly detection into multiple pattern categories (normal operation, developing anomaly, critical anomaly). Each category has distinct permutation entropy characteristics, allowing comprehensive information capture while simplifying classification through clear threshold-based differentiation
Data Source
Figure 1
Figure 2
Figure 3
AI summary
A controller comprising a non-transitory computer-readable storage medium (60) storing one or more processor-executable instructions wherein the one or more instructions, when executed by a processor (58) of the controller (56), cause acts to be performed including receiving (152) signals (100) representative of pressure between respective compressor blade (80) tips and a casing (25) of a compressor (24) at one or more stages (82), generating (154) multiple patterns based on a permutation entropy window (254) and the signals, identifying (156) multiple pattern categories (404, 454) in the multiple patterns, determining (158) a permutation entropy based on the multiple patterns and the multiple pattern categories (404, 454), predicting (160) an anomaly (180, 194) in the compressor (24) based on the permutation entropy, comparing (162) the multiple pattern categories (404, 454) to determined permutations (110) of pattern categories (404, 454) when an anomaly (180, 194) is present in the compressor (24), predicting (164) a category of the anomaly (180, 194) based on the comparison of the multiple pattern categories (404, 454) to the determined permutation (110) of pattern categories (404, 454) and determining and executing (166) a corrective action.