초록 ▼

A system and method for detecting erosion in turbine engine blades is provided. The blade erosion detection system includes a sensor data processor and a cluster analysis mechanism. The sensor data processor receives engine sensor data, including exhaust gas temperature (EGT) data, and augments the

A system and method for detecting erosion in turbine engine blades is provided. The blade erosion detection system includes a sensor data processor and a cluster analysis mechanism. The sensor data processor receives engine sensor data, including exhaust gas temperature (EGT) data, and augments the sensor data to determine sensor data residual values and the rate of change of the sensor data residual values. The augmented sensor data is passed to the cluster analysis mechanism. The cluster analysis mechanism analyzes the augmented sensor data to determine the likelihood that compressor blade erosion has occurred. Specifically, the cluster analysis mechanism performs a 2-tuple cluster feature analysis using Gaussian density functions that provide approximations of normal and eroded blades in a turbine engine. The 2-tuple cluster feature analysis thus provides the probability that the sensor data indicates erosion has occurred in the turbine engine.

대표청구항 ▼

The invention claimed is: 1. An erosion detection system for detecting erosion in blades in a turbine engine, the erosion detection system comprising: a sensor data processor, the sensor data processor adapted to receive engine sensor data from the turbine engine and generate sensor data residuals

The invention claimed is: 1. An erosion detection system for detecting erosion in blades in a turbine engine, the erosion detection system comprising: a sensor data processor, the sensor data processor adapted to receive engine sensor data from the turbine engine and generate sensor data residuals and sensor data residual slopes from the sensor data; and a cluster analysis mechanism, the cluster analysis mechanism adapted to perform a cluster analysis on the sensor data residuals and sensor data residual slopes using a first Gaussian density function representing a good turbine blade cluster and a second Gaussian density function representing an eroded turbine blade cluster to determine a likelihood that erosion has occurred in the blades. 2. The system of claim 1 wherein the blades comprise compressor blades. 3. The system of claim 1 wherein the sensor data processor is adapted to generate sensor data residuals by comparing the sensor data to expected sensor values provided from a turbine engine model. 4. The system of claim 1 wherein the sensor data processor is adapted to generate sensor data residual slopes by performing a linear trend fit on a set of sensor data residuals. 5. The system of claim 1 wherein the sensor data comprises exhaust gas temperature data. 6. The system of claim 1 wherein the cluster analysis mechanism is adapted to perform a cluster analysis using sensor data residuals and sensor data residual slopes by using the sensor data residuals and sensor data residual slopes as 2-tuples from non-eroded blades and 2-tuples from eroded blades that are approximated using the first Gaussian density function and the second Gaussian density function. 7. The system of claim 6 wherein the first Gaussian density function and the second Gaussian density function are determined during an offline training phase using historical data. 8. The system of claim 7 wherein the first Gaussian density function and the second Gaussian density function are rotated appropriately to fit the historical data. 9. The system of claim 1 wherein the cluster analysis mechanism calculates the likelihood that the sensor data corresponds to an engine with non-eroded blades and corresponds to an engine with eroded blades. 10. The system of claim 9 wherein the cluster analysis mechanism further uses a Bayesian rule to determine the probability of eroded blades in the turbine engine. 11. A method of detecting erosion in blades in a turbine engine, the method comprising the steps of: a) receiving sensor data from the turbine engine; b) generating sensor data residuals and sensor data residual slopes from the received sensor data; and c) determining a likelihood of erosion in the blades through a cluster analysis on the sensor data residuals and sensor data residual slopes by performing a cluster analysis on the sensor data residuals and sensor data residual slopes using a first Gaussian density function representing a good turbine blade cluster and a second Gaussian density function representing an eroded turbine blade cluster. 12. The method of claim 11 wherein the blades comprise compressor blades. 13. The method of claim 11 wherein the step of generating sensor data residuals comprises comparing the sensor data to expected sensor values provided from a turbine engine model. 14. The method of claim 11 wherein the step of generating sensor data residuals and sensor data residual slopes comprises generating sensor data residual slopes by performing a linear trend fit on a set of sensor data residuals. 15. The method of claim 11 wherein the sensor data comprises exhaust gas temperature data. 16. The method of claim 11 wherein the step of determining a likelihood of erosion in the turbine blades through a cluster analysis on the sensor data residuals and sensor data residual slopes comprises using the sensor data residuals and sensor data residual slopes as 2-tuples from non-eroded blades and 2-tuples from eroded blades that are approximated using the first Gaussian density function and the second Gaussian density function. 17. The method of claim 16 further comprising the step of determining the first Gaussian density function and the second Gaussian density function during an offline training phase using historical data. 18. The method of claim 17 wherein the first Gaussian density function and the second Gaussian density function are rotated appropriately to fit the historical data. 19. The method of claim 11 wherein the step of determining a likelihood of erosion in the turbine blades through a cluster analysis on the sensor data residuals and sensor data residual slopes comprises calculating a likelihood that the sensor data corresponds to an engine with non-eroded blades and corresponds to an engine with eroded blades. 20. The method of claim 19 wherein the step of calculating a likelihood that the sensor data corresponds to an engine with non-eroded blades and corresponds to an engine with eroded blades comprises using a Bayesian rule to determine the probability of eroded blades in the turbine engine. 21. A program product comprising: a) an erosion detection program for detecting erosion in blades in a turbine engine, the erosion detection program including: a sensor data processor, the sensor data processor adapted to receive engine sensor data from the turbine engine and generate sensor data residuals and sensor data residual slopes from the sensor data; and a cluster analysis mechanism, the cluster analysis mechanism adapted to perform a cluster analysis on the sensor data residuals and sensor data residual slopes using a first Gaussian density function representing a good turbine blade cluster and a second Gaussian density function representing an eroded turbine blade cluster to determine a likelihood that erosion has occurred in the blades; and b) computer-readable signal bearing media bearing said erosion detection program. 22. The program product of claim 21 wherein the wherein the blades comprise compressor blades. 23. The program product of claim 21 wherein the sensor data processor is adapted to generate sensor data residuals by comparing the sensor data to expected sensor values provided from a turbine engine model. 24. The program product of claim 21 wherein the sensor data processor is adapted to generate sensor data residual slopes by performing a linear trend fit on a set of sensor data residuals. 25. The program product of claim 21 wherein the sensor data comprises exhaust gas temperature data. 26. The program product of claim 21 wherein the first Gaussian density function and the second Gaussian density function are determined during an offline training phase using historical data. 27. The program product of claim 26 wherein the first Gaussian density function and the second Gaussian density function are rotated appropriately to fit the historical data. 28. The program product of claim 21 wherein the cluster analysis mechanism calculates the likelihood that the sensor data corresponds to an engine with non-eroded blades and corresponds to an engine with eroded blades. 29. The program product of claim 28 wherein the cluster analysis mechanism further uses a Bayesian rule to determine the probability of eroded blades in the turbine engine.