Patent Number: 
Section: claims

1. A method for detecting combustion anomalies within a gas turbine engine comprising:obtaining a sampled dynamic signal that is representative of combustion conditions comprising:obtaining a signal measured by a sensor associated with a combustor of the engine; andconverting the signal obtained by the sensor to a sampled dynamic signal with an analog to digital convertor;providing a processor that:divides the sampled dynamic signal into time segments to derive a plurality of data points for each of the time segments; andtransforms the sampled dynamic signal to a form that enables detection of whether the sensed combustion conditions within the combustor are indicative of one or more combustion anomalies of interest comprising processing each time segment by:performing a wavelet transform to calculate wavelet coefficients for the data points within the processed time segment;targeting at least one region of interest within the wavelet transformed segment; andnormalizing the amplitude of the wavelet coefficients within each targeted region by a baseline signal; anddetermining whether any combustion anomalies have occurred during each of the time segments using the normalized amplitudes of the wavelet coefficients within each targeted region by comparing the normalized amplitudes of the wavelet coefficients within each target region to a predetermined threshold amplitude. 2. The method according to claim 1, wherein obtaining a signal measured by a sensor associated with a combustor of the engine comprises:receiving a sensor output signal from at least one thermoacoustic sensor where the received signal corresponds to a measure of the thermoacoustic oscillations in the combustor. 3. The method according to claim 2, receiving a signal from at least one thermoacoustic sensor comprises:receiving the sensor output signal from at least one of a dynamic pressure sensor, an accelerometer, a high temperature microphone, an optical sensor, and an ionic sensor. 4. The method according to claim 1, wherein the processor divides the sampled dynamic signal into time segments to derive a plurality of data points for each of the time segments by:dividing the sampled dynamic signal into time segments, each time segment being less than a predefined period which is required to detect the occurrence of combustion anomalies of interest. 5. The method according to claim 4, wherein dividing the sampled dynamic signal into time segments, each time segment being less than a predefined period which is required to detect the occurrence of combustion anomalies of interest comprises:dividing the sampled dynamic signal into time segments that are sufficiently small enough to respond to the detection of the occurrence of the combustion anomalies of interest. 6. The method according to claim 1, wherein performing a wavelet transform to calculate wavelet coefficients for the data points within the processed time segment comprises:computing a discrete wavelet transform based upon wavelet sub-band coding by using a series of digitally implemented cascading filter banks to decompose the sampled dynamic signal in to wavelet components. 7. The method according to claim 6, wherein targeting at least one region of interest within the wavelet transformed segment comprises:identifying at least one region of interest based upon identifying a wavelet sub-band of interest. 8. The method according to claim 1, wherein targeting at least one region of interest within the wavelet transformed segment comprises:utilizing the wavelet transform to conceptualize the dynamic sensor signal from a single dimensional, time varying signal into a multi-dimensional, time varying signal characterized in terms of scale and amplitude as a function of time; andidentifying at least one scale as a region of interest for targeting detection of combustion anomalies of interest. 9. The method according to claim 1, wherein normalizing the amplitude of the wavelet coefficients within each targeted region by a baseline signal comprises:calculating the root means square values of wavelet coefficients within the targeted regions of interest;normalizing the calculated root means square values of the wavelet coefficients by the root means square values of a corresponding time domain sensor signal for that time segment. 10. The method according to claim 1, further comprising:utilizing operational conditions of the engine to determine which type of combustion anomalies are occurring. 11. A system that detects combustion anomalies within a gas turbine engine comprising:a sensor associated with a combustor of the engine that measures a signal that is representative of combustion conditions;an analog to digital converter that converts the signal measured by the sensor to a sampled dynamic signal; anda processor that:divides the sampled dynamic signal into time segments to derive a plurality of data points for each of the time segments; andtransforms the sampled dynamic signal to a form that enables detection of whether the sensed combustion conditions within the combustor are indicative of one or more combustion anomalies of interest, wherein, for each time segment, the processor:performs a wavelet transform to calculate wavelet coefficients for the data points within the processed time segment;targets at least one region of interest within the wavelet transformed segment; andnormalizes the amplitude of the wavelet coefficients within each targeted region by a baseline signal;wherein the normalized amplitudes of the wavelet coefficients within each targeted region are used to determine whether any combustion anomalies have occurred during each of the time segments by comparing the normalized amplitudes of the wavelet coefficients within each target region to a predetermined threshold amplitude. 12. The system according to claim 11, wherein the sensor comprises at least one thermoacoustic sensor that measures thermoacoustic oscillations in the combustor, and wherein measured thermoacoustic oscillations are converted to the sampled dynamic signal by the analog to digital converter. 13. The system according to claim 12, wherein the sensor comprises at least one of a dynamic pressure sensor, an accelerometer, a high temperature microphone, an optical sensor, and an ionic sensor. 14. The system according to claim 11, wherein the time segments are less than a predefined period which is required to detect the occurrence of combustion anomalies of interest. 15. The system according to claim 14, wherein the time segments are sufficiently small enough to respond to the detection of the occurrence of the combustion anomalies of interest. 16. The system according to claim 11, wherein the wavelet transform comprises a discrete wavelet transform, the discrete wavelet transform based upon wavelet sub-band coding of digitally implemented cascading filter banks that decompose the sampled dynamic signal into wavelet components. 17. The system according to claim 16, wherein the targeted at least one region of interest within the wavelet transformed segment is based upon an identified wavelet sub-band of interest. 18. The system according to claim 11, wherein, to target the at least one region of interest within the wavelet transformed segment, the processor:utilizes the wavelet transform to conceptualize the dynamic sensor signal from a single dimensional, time varying signal into a multi-dimensional, time varying signal characterized in terms of scale and amplitude as a function of time; andidentifies at least one scale as a region of interest that targets detection of combustion anomalies of interest. 19. The system according to claim 11, wherein, to normalize the amplitude of the wavelet coefficients within each targeted region by a baseline signal, the processor:calculates the root means square values of wavelet coefficients within the targeted regions of interest; andnormalizes the calculated root means square values of the wavelet coefficients by the root means square value of a corresponding time domain sensor signal for that time segment. 20. The system according to claim 11, wherein operational conditions of the engine are utilized to determine which type of combustion anomalies are occurring.