Combustion chamber unstable combustion state monitoring method and system based on acoustic sensor

Abstract

The present application relates to the technical field of acoustic state detection, and discloses a combustion chamber unstable combustion state monitoring method and system based on an acoustic sensor, which comprises collecting a pressure time domain signal in a combustion chamber through an acoustic pressure sensor, calculating a frequency division threshold; generating a low-frequency spectrogram lower than the frequency division threshold, generating a high-frequency spectrogram higher than the frequency division threshold, and obtaining a fused spectrogram; generating an entropy weight fused spectrogram; constructing a three-channel feature tensor and inputting it into a convolutional neural network to obtain a preliminary classification probability of the combustion state; setting a first energy variance threshold and a second energy variance threshold, calculating an energy variance, and when the energy variance is lower than the first energy variance threshold, the combustion state is stable; and when it is higher than the second energy variance threshold, it is unstable. The present application can suppress noise interference and improve the accuracy of the unstable combustion state monitoring result.

Description

Technical Field

[0001] This invention relates to the field of acoustic condition detection technology, and specifically to a method and system for monitoring unstable combustion state in a combustion chamber based on acoustic sensors. Background Technology

[0002] Thermal equipment such as aircraft engines and gas turbines face the threat of unstable combustion in their combustion chambers during operation. The pressure oscillations generated by this phenomenon can induce severe structural vibrations, leading to component fatigue damage and even catastrophic accidents. Current monitoring methods mainly fall into two categories: First, classical signal analysis, which relies on time-domain analysis of the pressure signal, such as calculating the root mean square value and peak value; or on frequency-domain analysis of the pressure signal, such as determining the dominant frequency through Fast Fourier Transform. Second, time-frequency analysis, which uses Short-Time Fourier Transform (STFT) to generate a spectrogram to visually display the distribution of signal energy over time and frequency.

[0003] However, the current monitoring methods have the following limitations: The short-time Fourier transform suffers from a time-frequency resolution contradiction: the short-time Fourier transform is constrained by the Heisenberg uncertainty principle, leading to a mutual constraint between time resolution and frequency resolution. The selection of a single window function makes it difficult to simultaneously accurately represent the slowly changing low-frequency oscillations in the early stages of unstable combustion, as well as high-frequency transient events such as deflagration. Deep learning lacks reliability: although inputting spectrograms into convolutional neural networks (CNNs) for feature extraction and pattern recognition has become a trend, it still faces the following challenges: First, feature overload: crucial weak feature information may be overwhelmed or distorted. Second, unstable decision-making: existing methods rely excessively on the classification probability of CNNs or simple fixed thresholds for diagnosis, making it difficult to adapt to complex and variable operating conditions. Furthermore, the model output is prone to misjudgment in operating conditions not covered by the training samples. Summary of the Invention

[0004] This invention provides a method and system for monitoring unstable combustion state in a combustion chamber based on acoustic sensors to solve the problem of low accuracy of unstable combustion state monitoring results in the prior art.

[0005] In a first aspect, the method for monitoring unstable combustion state in a combustion chamber based on an acoustic sensor according to the present invention includes the following steps:

[0006] The pressure time-domain signal inside the combustion chamber is acquired by an acoustic pressure sensor and divided into preset analysis periods. The spectral centroid of the signal in each analysis period is calculated as the frequency segmentation threshold for the corresponding analysis period. Short-time Fourier transforms are performed on the pressure time-domain signal using a first window function and a second window function, respectively. A low-frequency spectrogram below the frequency segmentation threshold is generated based on the transform result of the first window function, and a high-frequency spectrogram above the frequency segmentation threshold is generated based on the transform result of the second window function. The low-frequency spectrogram and the high-frequency spectrogram are spliced ​​to obtain a fused spectrogram. The spectral entropy of the fused spectrogram at each time frame is calculated to form a spectral entropy time series. The amplitude of each time frame is weighted according to the principle that the spectral entropy value is inversely proportional to the weight coefficient to generate an entropy-weighted fused spectrogram. The entropy-weighted fused spectrogram, the first-order difference map along the time axis, and the first-order difference map along the frequency axis are constructed into a three-channel feature tensor, including: using the entropy-weighted fused spectrogram as the first channel; calculating the difference between adjacent frames of the entropy-weighted fused spectrogram on the time axis to generate a first-order difference map along the time axis. The first-order difference map along the time axis is used as the second channel; the difference between adjacent frequency points on the frequency axis of the entropy-weighted fusion spectrogram is calculated to generate a first-order difference map along the frequency axis, which is used as the third channel; the first, second, and third channels are stacked in sequence to form a three-channel feature tensor, which is then input into a convolutional neural network to obtain the preliminary classification probability of the combustion state; the mean of the absolute values ​​of the first-order differences of the spectral entropy time series is calculated as the rate of change of spectral entropy, and a first energy variance threshold and a second energy variance threshold are set. The first energy variance threshold is inversely proportional to the rate of change of spectral entropy, and the second energy variance threshold is directly proportional to the rate of change of spectral entropy; the energy variance of the pressure time-domain signal within the analysis period is calculated. When the energy variance is lower than the first energy variance threshold, the combustion state is stable; when the energy variance is higher than the second energy variance threshold, the combustion state is unstable; when the energy variance is between the first and second energy variance thresholds, the combustion state is determined based on the maximum value of the preliminary classification probability.

[0007] Preferably, both the first window function and the second window function are Hanning windows.

[0008] Preferably, the duration of the first window function is 100 milliseconds, and the duration of the second window function is 25 milliseconds.

[0009] Preferably, the overlap rate of the first window function and the second window function is both 50%.

[0010] Preferably, the step of weighting the amplitude of each time frame according to the principle that the spectral entropy value is inversely proportional to the weighting coefficient to generate an entropy-weighted fused spectrogram includes: the weighting coefficient is calculated using the following formula,

[0011] Where i represents the i-th time frame of the fused spectrogram, The preset weighted adjustment coefficient, Let be the spectral entropy value of the i-th time frame; The weight coefficients for the i-th time frame;

[0012] The amplitude values ​​of each time frame are weighted using the following formula.

[0013] ;in, The weighted amplitude for the i-th time frame. The magnitude before weighting in the i-th time frame. represents the weighting coefficient for the i-th time frame.

[0014] Preferably, the first energy variance threshold is calculated using the following formula:

[0015] ;in, The first energy variance threshold, These are positive real constants calibrated based on experimental data. The rate of change of spectral entropy. To prevent tiny positive numbers with a denominator of zero, Set to 1e-6.

[0016] Preferably, the second energy variance threshold is calculated using the following formula:

[0017] ;in, This is the second energy variance threshold. The rate of change of spectral entropy. , These are positive real constants calibrated based on experimental data.

[0018] Preferably, the step of acquiring the time-domain pressure signal in the combustion chamber through an acoustic pressure sensor and dividing it into preset analysis periods includes: installing an acoustic pressure sensor on the combustion chamber wall to acquire the pressure signal, and using 1 second as an analysis period.

[0019] Secondly, the combustion chamber unstable combustion state monitoring system based on acoustic sensors of the present invention includes a memory and a processor. The memory stores computer instructions, and when the processor executes the computer instructions, it implements the above-mentioned combustion chamber unstable combustion state monitoring method based on acoustic sensors.

[0020] The beneficial effects of this invention are as follows: By employing a frequency segmentation strategy based on the centroid of the signal spectrum and combining long and short window functions to process low-frequency and high-frequency components respectively, this invention constructs a fused spectrogram that balances frequency and time resolution, revealing key time-frequency details in the combustion state evolution process. Weighting the spectrogram using spectral entropy highlights non-stationary features and suppresses noise interference. The weighted spectrogram and its first-order difference map are then constructed into a three-channel feature tensor, providing richer and more discriminative input information for the convolutional neural network. Combining the model's preliminary judgment with an energy variance interval set based on the signal's physical characteristics, and employing reliable criteria for judgment, this invention enhances the reliability of monitoring results by relying on model classification in ambiguous situations. Attached Figure Description

[0021] Figure 1 This is a flowchart illustrating the method for monitoring unstable combustion state in a combustion chamber based on acoustic sensors, provided in an embodiment of the present invention. Detailed Implementation

[0022] Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain the present invention, and should not be construed as limiting the present invention.

[0023] like Figure 1 As shown, an embodiment of the combustion chamber unstable combustion state monitoring method based on acoustic sensors provided by the present invention includes the following steps:

[0024] S1: The time-domain pressure signal in the combustion chamber is acquired by an acoustic pressure sensor, divided into preset analysis periods, and the spectral centroid of the signal in each analysis period is calculated as the frequency segmentation threshold for the corresponding analysis period.

[0025] Specifically, an acoustic pressure sensor is installed on the combustion chamber wall to collect pressure signals, and a 1-second analysis period is used as the analysis time interval; the pressure time-domain signal within the analysis period is subjected to a fast Fourier transform to obtain its spectrum. According to the formula The spectral centroid of the signal within the analysis period is calculated, and its value is used as the frequency segmentation threshold for subsequent spectrogram segmentation. Represents a frequency point.

[0026] S2, perform short-time Fourier transform on the pressure time-domain signal using the first window function and the second window function respectively; generate a low-frequency spectrogram below the frequency segmentation threshold based on the transform result of the first window function, generate a high-frequency spectrogram above the frequency segmentation threshold based on the transform result of the second window function, and stitch the low-frequency spectrogram and the high-frequency spectrogram together to obtain a fused spectrogram.

[0027] In an optional embodiment, both the first window function and the second window function are Hanning windows; the duration of the first window function is 100 milliseconds, and the duration of the second window function is 25 milliseconds; the overlap rate of both the first window function and the second window function is 50%.

[0028] The pressure signal in the combustion chamber contains steady-state and transient components. Steady-state components change slowly, such as the continuous resonance of the equipment; transient components change rapidly, such as the impact of valve opening and closing. To effectively analyze these two types of characteristics, this invention employs two window functions of different lengths to achieve a balance between time and frequency resolution. Long window functions provide high frequency resolution but low time resolution, while short window functions offer the opposite.

[0029] For example, taking a pressure signal with a sampling rate of 1000 Hz as an example, the first window function's time length is set to 100 milliseconds, i.e., 100 sampling points. After short-time Fourier transform, it can distinguish similar frequency components such as 10 Hz and 20 Hz, which is suitable for analyzing the steady-state operating frequency of the equipment. The second window function's time length is set to 25 milliseconds, i.e., 25 sampling points. It can locate the moment of signal abrupt change at a higher time, such as a pressure pulse with a duration of only 30 milliseconds. The Hanning window is used to reduce spectral leakage and ensure that energy is concentrated in the main lobe, while the 50% overlap rate ensures a smooth transition between time frames and avoids the loss of boundary information caused by windowing.

[0030] Specifically, from the transformation result of the first window function, the portion with a frequency lower than the previously calculated frequency segmentation threshold is extracted as a low-frequency spectrogram; from the transformation result of the second window function, the portion with a frequency higher than the frequency segmentation threshold is extracted as a high-frequency spectrogram; the low-frequency spectrogram and the high-frequency spectrogram are then stitched together along the frequency axis to form a fused spectrogram with high frequency resolution in the low-frequency band and high temporal resolution in the high-frequency band.

[0031] S3. Calculate the spectral entropy of the fused spectrogram at each time frame to form a spectral entropy time series. Based on the principle that the spectral entropy value is inversely proportional to the weight coefficient, weight the amplitude of each time frame to generate an entropy-weighted fused spectrogram. Construct the entropy-weighted fused spectrogram, the first-order difference map along the time axis, and the first-order difference map along the frequency axis into a three-channel feature tensor, and input it into a convolutional neural network to obtain the preliminary classification probability of the combustion state.

[0032] Specifically, for each time frame in the fused spectrogram, the amplitude spectrum is normalized to obtain the probability distribution. Then according to the formula Calculate the spectral entropy H for all time frames, and the spectral entropy of all time frames constitutes the spectral entropy time series.

[0033] In an optional embodiment, the step of weighting the amplitude of each time frame according to the principle that the spectral entropy value is inversely proportional to the weighting coefficient to generate an entropy-weighted fused spectrogram includes: the weighting coefficient is calculated using the following formula,

[0034] Where i represents the i-th time frame of the fused spectrogram, The preset weighted adjustment coefficient, Let be the spectral entropy value of the i-th time frame; The weight coefficients for the i-th time frame;

[0035] The amplitude values ​​of each time frame are weighted using the following formula.

[0036] ;in, The weighted amplitude for the i-th time frame. The magnitude before weighting in the i-th time frame. represents the weighting coefficient for the i-th time frame.

[0037] Spectral entropy is an indicator of the uniformity of energy distribution in a signal's spectrum. When signal energy is concentrated at a few frequency points, such as pure tones or harmonics, the spectral entropy value is low, indicating a large amount of information in the signal. Conversely, when signal energy is evenly distributed across the entire frequency band, such as white noise, the spectral entropy value is high, indicating strong randomness in the signal. Utilizing this characteristic, spectral entropy is mapped to weighting coefficients using an exponential function; the higher the spectral entropy, the lower the weighting, and vice versa. By calculating the weighting coefficient for each time frame and multiplying all amplitude points within each time frame by their corresponding weighting coefficient, the entire fused spectrogram is weighted, resulting in an entropy-weighted fused spectrogram, thereby suppressing disordered components similar to white noise.

[0038] For example, suppose at a certain time i, the signal exhibits clear equipment fault characteristic frequencies, and the calculated spectral entropy H is 0.3. Then the weighting coefficient W is 0.64, and the amplitude M of this time frame is multiplied by a larger weight, thus enhancing it in the spectrogram. At another time j, the signal mainly consists of random environmental noise, and the calculated spectral entropy H is 0.9. Therefore, its weighting coefficient W is 0.26, and the amplitude M of this time frame is multiplied by a smaller weight, thus suppressing it. Entropy-weighted fusion spectrograms can automatically highlight the effective components of the signal while reducing noise interference, improving the signal-to-noise ratio of the features.

[0039] To expand the static features of the original two-dimensional spectrogram into multi-dimensional features containing change information, and to adapt to deep learning models capable of handling three-channel input, in an optional embodiment, the construction of a three-channel feature tensor from the entropy-weighted fused spectrogram, the first-order difference map along the time axis, and the first-order difference map along the frequency axis includes: using the entropy-weighted fused spectrogram as the first channel; calculating the difference between adjacent frames of the entropy-weighted fused spectrogram on the time axis to generate the first-order difference map along the time axis, which serves as the second channel; calculating the difference between adjacent frequency points of the entropy-weighted fused spectrogram on the frequency axis to generate the first-order difference map along the frequency axis, which serves as the third channel; and stacking the first, second, and third channels sequentially to form the three-channel feature tensor, the shape of which is (height, width, 3).

[0040] Entropy-weighted fusion spectrograms reflect the energy distribution at various times and frequencies, preserving core static information as the first channel. The first-order difference plot on the time axis represents the rate of change of spectral energy over time, enhancing the transient characteristics of the signal, such as sudden increases or decreases in the energy of a frequency component, which is crucial for detecting impact failure events. The first-order difference plot on the frequency axis reflects the local contour of the spectrum and the relationship between energy in adjacent frequency bands, helping to describe the harmonic structure or spectral slope, and providing important information for distinguishing the operating states of different devices. For example, a spectrogram with a height of 257 frequency points and a width of 300 time frames will generate first-order difference plots on the time axis and frequency axis of the same size, stacked into a 257*300*3 three-channel feature tensor, similar to an RGB color image. This three-channel feature tensor is then input into a pre-trained convolutional neural network containing multiple convolutional layers, pooling layers, and fully connected layers. At the output layer, the Softmax function is used to obtain preliminary classification probabilities for three states: stable, instability precursors, and unstable.

[0041] S4. Calculate the mean absolute value of the first-order difference of the spectral entropy time series as the rate of change of spectral entropy. Set a first energy variance threshold and a second energy variance threshold. The first energy variance threshold is inversely proportional to the rate of change of spectral entropy, and the second energy variance threshold is directly proportional to the rate of change of spectral entropy. Calculate the energy variance of the pressure time-domain signal within the analysis period. When the energy variance is lower than the first energy variance threshold, the combustion state is stable. When the energy variance is higher than the second energy variance threshold, the combustion state is unstable. When the energy variance is between the first energy variance threshold and the second energy variance threshold, the combustion state is determined based on the maximum value of the preliminary classification probability.

[0042] Specifically, the spectral entropy time series is first-differenced, and the average of its absolute values ​​is taken to obtain the rate of change of spectral entropy. In an optional embodiment, the first energy variance threshold is calculated using the following formula:

[0043] ;in, The first energy variance threshold, These are positive real constants calibrated based on experimental data. The rate of change of spectral entropy. To prevent tiny positive numbers with a denominator of zero, Set to 1e-6.

[0044] The second energy variance threshold is calculated using the following formula.

[0045] ;in, This is the second energy variance threshold. The rate of change of spectral entropy. , These are positive real constants calibrated based on experimental data.

[0046] Specifically, the rate of change of spectral entropy R reflects the degree of change in signal stationarity; the value of R increases when the signal transitions from a stationary noise segment to a non-stationary effective signal segment. Assuming calibration based on a large amount of data... It is 5. It is 20. The value is 0.5. When the signal is in a stable background noise environment, the rate of change of spectral entropy R is very small, for example, 0.05, at which point the first energy variance threshold is reached. The value is calculated to be 100, the second energy variance threshold. The calculated value is 1.5. When a stress event occurs, the signal complexity increases, and the rate of change of spectral entropy R increases to 2.0, at which point the first energy variance threshold... It will dynamically decrease to 2.5, while the second energy variance threshold This will increase to 40.5. The 1-second pressure time-domain signal is divided into 10 segments of 0.1 seconds each. The sum of squares of the signal within each segment is calculated to obtain its energy, resulting in a sequence containing 10 energy values. The variance of this energy sequence is calculated; this is the energy variance. The energy variance is then compared with a first energy variance threshold. Second energy variance threshold Comparison: If the energy variance is less than If the energy variance is greater than 1, then the combustion state is directly determined to be stable; If the combustion state is considered unstable, then the energy variance is considered unstable. and Between these states, examine the probability values ​​of the three states output by the convolutional neural network and select the state with the highest probability value as the monitoring result.

[0047] The implementation principle of the combustion chamber unstable combustion state monitoring method based on acoustic sensors in this invention is as follows: This invention successfully resolves the resolution contradiction in time-frequency analysis by employing a frequency segmentation strategy based on the centroid of the signal spectrum. A long window function is used to process low-frequency components, and a short window function is combined to process high-frequency components, ultimately constructing a fused spectrogram that balances high frequency and high time resolution, thereby revealing key time-frequency details in the combustion state evolution process more comprehensively. In terms of feature extraction, this invention uses spectral entropy to weight the spectrogram, effectively highlighting non-stationary features and suppressing noise interference. Then, the weighted spectrogram and its first-order difference graphs of the time and frequency axes are constructed into a three-channel feature tensor, providing richer and more discriminative input information for the convolutional neural network. Furthermore, by combining the energy variance interval of the signal with a dynamic threshold set by the rate of change of spectral entropy, a reliable criterion judgment logic is designed, significantly enhancing the reliability and anti-false-judgment capability of the monitoring results.

[0048] An embodiment of the combustion chamber unstable combustion state monitoring system based on acoustic sensors provided by the present invention includes a memory and a processor. The memory stores computer instructions, and when the processor executes the computer instructions, it implements the combustion chamber unstable combustion state monitoring method based on acoustic sensors in the above embodiment.

[0049] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. A method for monitoring unstable combustion state in a combustion chamber based on acoustic sensors, characterized in that, The process includes the following steps: acquiring time-domain pressure signals within the combustion chamber using an acoustic pressure sensor, dividing the analysis into preset time periods, and calculating the spectral centroid of the signal within each time period as the frequency segmentation threshold for that time period; performing short-time Fourier transforms on the pressure time-domain signals using a first window function and a second window function, respectively; generating a low-frequency spectrogram below the frequency segmentation threshold based on the transform result of the first window function, generating a high-frequency spectrogram above the frequency segmentation threshold based on the transform result of the second window function, and stitching the low-frequency spectrogram and the high-frequency spectrogram together to obtain a fused spectrogram. The spectral entropy of the fused spectrogram at each time frame is calculated to form a spectral entropy time series. The amplitude of each time frame is weighted according to the principle that the spectral entropy value is inversely proportional to the weight coefficient to generate an entropy-weighted fused spectrogram. A three-channel feature tensor is constructed by using the entropy-weighted fused spectrogram, the first-order difference map along the time axis, and the first-order difference map along the frequency axis. This includes: using the entropy-weighted fused spectrogram as the first channel; calculating the difference between adjacent frames of the entropy-weighted fused spectrogram on the time axis to generate the first-order difference map along the time axis, which serves as the second channel; calculating the difference between adjacent frequency points of the entropy-weighted fused spectrogram on the frequency axis to generate the first-order difference map along the frequency axis, which serves as the third channel; stacking the first, second, and third channels sequentially to form the three-channel feature tensor; and inputting the three-channel feature tensor into a convolutional neural network. The initial classification probability of combustion state is obtained; the mean absolute value of the first difference of the spectral entropy time series is calculated as the spectral entropy change rate; a first energy variance threshold and a second energy variance threshold are set, the first energy variance threshold being inversely proportional to the spectral entropy change rate and the second energy variance threshold being directly proportional to the spectral entropy change rate; the energy variance of the pressure time domain signal within the analysis period is calculated, when the energy variance is lower than the first energy variance threshold, the combustion state is stable; when the energy variance is higher than the second energy variance threshold, the combustion state is unstable; when the energy variance is between the first energy variance threshold and the second energy variance threshold, the combustion state is determined according to the maximum value of the initial classification probability.

2. The method for monitoring unstable combustion state in a combustion chamber based on acoustic sensors according to claim 1, characterized in that, Both the first and second window functions are Hanning windows.

3. The method for monitoring unstable combustion state in a combustion chamber based on acoustic sensors according to claim 2, characterized in that, The duration of the first window function is 100 milliseconds, and the duration of the second window function is 25 milliseconds.

4. The method for monitoring unstable combustion state in a combustion chamber based on acoustic sensors according to claim 3, characterized in that, The overlap rate of the first window function and the second window function is both 50%.

5. The method for monitoring unstable combustion state in a combustion chamber based on an acoustic sensor according to claim 1, characterized in that, The step of weighting the amplitude of each time frame according to the principle that the spectral entropy value is inversely proportional to the weighting coefficient to generate an entropy-weighted fused spectrogram includes: The weighting coefficient is calculated using the following formula. Where i represents the i-th time frame of the fused spectrogram, The preset weighted adjustment coefficient, Let be the spectral entropy value of the i-th time frame; The weight coefficients for the i-th time frame; The amplitude values ​​of each time frame are weighted using the following formula. ;in, The weighted amplitude for the i-th time frame. The magnitude before weighting in the i-th time frame. represents the weighting coefficient for the i-th time frame.

6. The method for monitoring unstable combustion state in a combustion chamber based on an acoustic sensor according to claim 1, characterized in that, The first energy variance threshold is calculated using the following formula. ;in, The first energy variance threshold, These are positive real constants calibrated based on experimental data. The rate of change of spectral entropy. To prevent tiny positive numbers with a denominator of zero, Set to 1e-6.

7. The method for monitoring unstable combustion state in a combustion chamber based on an acoustic sensor according to claim 6, characterized in that, The second energy variance threshold is calculated using the following formula: ;in, This is the second energy variance threshold. The rate of change of spectral entropy. , These are positive real constants calibrated based on experimental data.

8. The method for monitoring unstable combustion state in a combustion chamber based on an acoustic sensor according to claim 1, characterized in that, The process of acquiring time-domain pressure signals within the combustion chamber via an acoustic pressure sensor and dividing the data into preset analysis time periods includes: An acoustic pressure sensor is installed on the combustion chamber wall to collect pressure signals, and an analysis period of 1 second is used.

9. A combustion chamber unstable combustion state monitoring system based on acoustic sensors, characterized in that, It includes a memory and a processor. The memory stores computer instructions. When the processor executes the computer instructions, it implements the method for monitoring unstable combustion state in a combustion chamber based on an acoustic sensor as described in any one of claims 1-8.

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