Wavelet Analysis for Gas Turbine Combustion Anomaly Detection
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Solution Overview
Problem
Combustion anomalies such as flame flashback in combustion engines can cause damage and destruction of components, and existing detection methods are inadequate for timely and accurate identification.
Innovation Solution
A method and system utilizing wavelet analysis of dynamic sensor signal information to detect combustion anomalies, involving sampling, wavelet transformation, normalization, and comparison to thresholds to identify anomalies in real-time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If wavelet analysis and real-time signal processing are implemented to detect combustion anomalies, then detection accuracy and timeliness are improved, but device complexity and computational requirements increase
Solution Approach 1:
The combustion signal is divided into multiple time segments, and wavelet analysis is applied to each segment individually. This segmentation allows the complex signal processing to be broken down into manageable portions, improving detection accuracy while distributing the computational load across multiple smaller processing units rather than requiring a single complex system.
Solution Approach 2:
The patent transforms the one-dimensional time-domain signal into a two-dimensional time-scale representation through wavelet analysis. This dimensional transformation enables better visualization and detection of combustion anomalies by revealing patterns that are not apparent in the original time-domain signal, thereby improving measurement precision without requiring proportional increases in system complexity.
2Loss of time
If real-time signal processing is performed to enable early detection of combustion anomalies, then response time is improved, but computational energy consumption increases
Solution Approach 1:
The system performs preliminary signal processing by dividing the continuous signal into discrete time segments before applying wavelet analysis. This preliminary segmentation allows the system to process only the most relevant portions of the signal in real-time, reducing unnecessary computational energy consumption while maintaining fast anomaly detection response.
Solution Approach 2:
The wavelet analysis is applied locally to specific time segments rather than processing the entire signal continuously. This localized approach allows the system to concentrate computational resources on periods where anomalies are most likely to occur, improving real-time detection response while minimizing overall energy consumption by avoiding unnecessary processing of normal operating conditions.
Data Source
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AI summary
The detection of combustion anomalies within a gas turbine engine is provided. A sensor (60) associated with a combustor of the engine measures a signal that is representative of combustion conditions. A sampled dynamic signal is divided into time segments to derive a plurality of data points. The sampled dynamic signal is transformed to a form that enables detection of whether the sensed combustion conditions within the combustor are indicative of any combustion anomalies of interest. A wavelet transform is performed to calculate wavelet coefficients for the data points and at least one region of interest is targeted. The amplitude of each wavelet coefficient within each targeted region is normalized by a baseline signal. The normalized amplitudes of the wavelet coefficients are used to determine whether any combustion anomalies have occurred by comparing the normalized amplitudes of the wavelet coefficients within each target region to a predetermined threshold amplitude.