An oscillating combustion fault detection method based on synchronous compressive transform

Abstract

The present application relates to the technical field of signal processing and fault diagnosis, the method comprises, collecting flame chemiluminescence signal, and carrying out adaptive filtering noise reduction processing;Using improved intrinsic orthogonal decomposition method and high order mode, obtaining comprehensive combustion characteristic information;Developing real-time synchronous compression transform algorithm to carry out time-frequency analysis to data, and updating energy distribution in real time;Combining sensor data, carrying out multi-parameter joint analysis, obtaining combustion state evaluation, using multiple high-speed cameras to obtain flame image from different angles, reconstructing three-dimensional flame morphology through computer vision technology, providing fault detection information;Building fault prediction model, realizing early warning of combustion fault.The method realizes nondestructive and rapid detection of oscillating combustion fault, retains rich information of flame image, reflects overall pulsation characteristics of flame, improves detection speed and accuracy, and makes up for the shortcoming of low frequency resolution of traditional time-frequency analysis method.

Description

Technical Field

[0001] This invention relates to the fields of signal processing and fault diagnosis technology, and in particular to an oscillating combustion fault detection method based on synchronous compression transformation. Background Technology

[0002] Gas turbines, as highly efficient energy conversion devices, are widely used in power generation, ship propulsion, and mechanical power. However, the operational stability of their combustors has a decisive impact on the performance and safety of the entire system. Gas turbine combustors typically employ a lean premixed swirl combustion method. While this method reduces combustion temperature and NOx emissions, it is highly susceptible to oscillating combustion faults. These faults severely affect the operating and combustion efficiency of the gas turbine and its combined cycle, and may also cause fluctuations in critical parameters such as combustor temperature and NOx concentration, posing a threat to equipment and personnel safety. Combustion instability phenomena, such as oscillating combustion, may occur during gas turbine operation, leading to decreased equipment performance and potentially even equipment damage. Therefore, rapid detection and diagnosis of oscillating combustion faults in gas turbines are particularly important.

[0003] Traditional methods for monitoring oscillating combustion faults primarily rely on pressure sensors to perform frequency domain analysis of the combustion chamber's time-domain pressure signal. However, this method has several significant drawbacks. First, because the sensors are typically installed far from the flame, there is an error between the actual combustion chamber pressure and the sensor's measured pressure, leading to data monitoring bias and lag. Second, due to the limitations of sensor response speed, fault diagnosis and identification are often delayed, frequently only discovering the fault after significant damage has occurred, which is extremely detrimental to actual production.

[0004] Existing technologies for detecting combustion oscillation faults in gas turbines typically employ methods such as acoustic detection and pressure fluctuation analysis. While these methods can detect combustion oscillations to some extent, they have the following limitations:

[0005] Low sensitivity: Due to the influence of environmental noise and mechanical vibration, methods such as acoustic detection and pressure fluctuation analysis are not effective in detecting weak combustion oscillation signals, and are prone to false alarms or missed alarms.

[0006] Location difficulties: Existing detection technologies are unable to accurately locate the specific location of combustion oscillations, thus failing to provide accurate information for troubleshooting.

[0007] Poor timeliness: Traditional detection methods require a long time for signal acquisition and processing, making it impossible to achieve real-time monitoring and rapid diagnosis. Summary of the Invention

[0008] In view of the aforementioned existing problems, the present invention is proposed.

[0009] Therefore, this invention provides an oscillating combustion fault detection method based on synchronous compression transformation, which can reduce the time cost of fault detection, improve the accuracy of fault detection, and reduce the false alarm rate.

[0010] To address the aforementioned technical problems, this invention provides the following technical solution: an oscillating combustion fault detection method based on synchronous compression transform, comprising: acquiring flame chemiluminescence signals; performing adaptive filtering and noise reduction processing on the acquired flame chemiluminescence signals; extracting first-order mode time coefficients from flame images using an improved intrinsic orthogonal decomposition method; obtaining comprehensive combustion feature information using higher-order modes; developing a real-time synchronous compression transform algorithm to perform time-frequency analysis on the data and update energy distribution in real time to respond to minute changes in combustion state; combining sensor data to perform multi-parameter joint analysis to obtain combustion state assessment; acquiring flame images from different angles using multiple high-speed cameras; reconstructing the three-dimensional flame morphology using computer vision technology to provide fault detection information; constructing a fault prediction model to automatically complete the entire fault detection process and achieve early warning of combustion faults.

[0011] As a preferred embodiment of the oscillating combustion fault detection method based on synchronous compression transform described in this invention, the adaptive filtering noise reduction process includes, when performing oscillating combustion fault detection, distinguishing between noise and actual combustion oscillation signals, selecting an adaptive filter based on the steepest descent method, and combining noise estimation and signal enhancement in the filter.

[0012] The adaptive filter update rule is as follows:

[0013]

[0014] Where w(n) represents the filter weight vector, and n represents the discrete time step. Let represent the noise power estimate at time n, ∈ represent a constant, x(n) represent the input flame signal at time n, e(n) represent the error signal at time n, and α represent the forgetting factor.

[0015] As a preferred embodiment of the oscillating combustion fault detection method based on synchronous compression transform described in this invention, the improved intrinsic orthogonal decomposition method includes performing POD analysis on the flame image set after filter processing to obtain intrinsic modes and intrinsic values, and selecting the M intrinsic modes with the highest energy according to the magnitude of the intrinsic values ​​to form a new mode set, representing the most significant spatial features in the data.

[0016] For each selected mode, a time coefficient is calculated. The time coefficient represents the change of the projection of the flame image onto each intrinsic mode over time. The calculation formula is as follows:

[0017]

[0018] Among them, c i (m) represents the time coefficient, and I(m) represents the m-th flame image frame. φ represents the mean of the image set. i (x,y) represents the eigenmode of the flame image, where i is the index of the mode;

[0019] The calculated time coefficients are stored in a set:

[0020]

[0021] in, This represents a set containing time coefficients for higher-order modes, which reveals subtle fluctuations in the combustion process and early signs of failure.

[0022] As a preferred embodiment of the oscillation combustion fault detection method based on synchronous compression transform described in this invention, the development of the real-time synchronous compression transform algorithm includes: rearranging the coefficients of the time-frequency transform through a synchronous compression operator, moving the time-frequency coefficients of the signal at any point in the time-frequency plane to the center of energy position, enhancing the energy concentration of the instantaneous frequency; applying a window function on the basis of Fourier transform to divide the signal time domain; obtaining the frequency distribution under different time windows through window function sliding; and arranging these short-time spectra in time sequence to describe the time-varying law of the signal frequency components.

[0023] For a given time-varying signal The short-time Fourier transform window function is g(t), which, after STFT transformation, gives:

[0024]

[0025] Where G(t,f) represents the result of the Fourier transform, s(τ) represents the original signal, τ represents the time variable of the original signal, g(τ-t) represents the result of the Fourier transform, r represents the time variable, f represents the frequency variable, and i represents the imaginary unit.

[0026] Performing Fourier transforms on the time-varying harmonic signal and the window function signal yields the estimated instantaneous frequency in the STFT time-frequency representation, expressed as:

[0027]

[0028] Where θ(t,ω) represents the instantaneous frequency, ω represents the frequency variable, arg(G(t,f)) represents the argument, and Re represents taking the real part. This represents the partial derivative with respect to time t;

[0029] Using the instantaneous frequency estimation results described above, STFT coefficients with the same frequency are collected. Based on the properties of the Dirac function, the synchronization compression operator function ∫ is given. R δ(η-ω0(t,ω)) yields the synchronous compression transform, expressed as:

[0030] T s (t,η)=∫ R G(t,f)δ(η-θ(t,ω))dω

[0031] Among them, T s (t,η) represents the instantaneous frequency signal, δ(η-θ(t,ω)) represents the Dirac function, η represents the instantaneous frequency variable, the time-frequency coefficients are redistributed and arranged in the frequency direction, and the synchronous compression transformation process at time t is the redistribution of frequency points and the process that goes through all times.

[0032] As a preferred embodiment of the oscillating combustion fault detection method based on synchronous compression transformation described in this invention, the multi-parameter joint analysis includes analyzing the data collected by the sensor to understand its distribution and characteristics, adjusting the parameters through optimization algorithms, and finding the optimal parameter combination after multiple iterations.

[0033] The combined parameters are substituted into the multi-parameter joint analysis formula to calculate the weighted contribution of each sensor. The weights take into account the different sensitivities of the sensors to angle, velocity, and light intensity, and a normalization function is applied to ensure the comparability of data from different sensors. The specific formula is as follows:

[0034]

[0035] Where G(θ,v,I) represents the master function for multi-parameter joint analysis, θ represents the flame tilt angle, v represents the flame combustion rate, I represents the flame radiation intensity, n represents the number of sensors, and Ω i f represents the effective measurement domain of the i-th sensor. i (θ i ,v i ,I i ) represents the weighted contribution, α i Let θ represent the angle parameter of the i-th sensor. i θ represents the angle value measured by the i-th sensor. 0i β represents the reference angle value of the i-th sensor. i γ represents the adjustment coefficient for the velocity parameter of the i-th sensor. i This represents the adjustment coefficient for the light intensity parameter of the i-th sensor. Let represent the mean and standard deviation of the angle measurement value of the i-th sensor, respectively. Let represent the mean and standard deviation of the velocity measurement value of the i-th sensor, respectively.

[0036] As a preferred embodiment of the oscillating combustion fault detection method based on synchronous compression transformation described in this invention, the reconstruction of the three-dimensional flame morphology includes reconstructing the three-dimensional flame morphology using a computer vision algorithm by combining the results of multi-parameter joint analysis.

[0037] By considering the changes in flame over time using a time-dependent function, a dynamic three-dimensional flame model is obtained:

[0038] F(x,y,z,t)=∫ Ω G(θ,v,I)·h(t)dt

[0039] h(t)=sin(ωt+φ)·e -λt

[0040] Where x, y, z represent the coordinate axes in three-dimensional space, h(t) represents the flame variation function over time, Ω represents the spatial domain of integration, φ represents the phase shift, and λ represents the attenuation coefficient; the three-dimensional flame model gives the complete shape of the flame in three-dimensional space, including its variation over time.

[0041] As a preferred embodiment of the oscillating combustion fault detection method based on synchronous compression transformation described in this invention, the fault prediction model includes comparing the calculated flame state and flame shape with a normal behavior threshold. If the calculation result exceeds a preset threshold range, it is considered that there is abnormal behavior.

[0042] When the flame temperature is below 1000K or above 2500K, the abnormal behavior is identified as incomplete combustion or burner blockage. The solution is to adjust the oxygen / fuel ratio of the burner and check and clean the carbon deposits and sediment inside the burner.

[0043] When the flame oscillation frequency is below 0.5Hz or above 2Hz, and the flame shape is below 0.5 or above 1.5, the abnormal behavior is considered to be due to damage to the internal structure of the burner or unstable fuel supply. The solutions are to check the fuel supply system to maintain a stable fuel flow; check whether the burner nozzles and combustion chamber are blocked; and check whether the internal structure is damaged.

[0044] If the contrast of the flame image is below 0.6 or above 1.5, the abnormal behavior is considered to be caused by improper camera settings or the characteristics of the flame itself. The solution is to adjust the camera parameters to improve image quality, check whether the camera lens is contaminated or damaged, and check the brightness and color characteristics of the flame.

[0045] The detected abnormal behaviors and their causes are summarized as fault detection information and recorded in the database for subsequent analysis and reporting. Fault detection reports are generated regularly to monitor the health and performance of the combustion process.

[0046] Another objective of this invention is to provide an oscillating combustion fault detection system based on synchronous compression transformation, which can improve the fault detection accuracy and diagnostic speed through an oscillating combustion fault detection algorithm.

[0047] As a preferred embodiment of the oscillating combustion fault detection system based on synchronous compression transformation according to the present invention, it includes: a data acquisition module, an intrinsic orthogonal decomposition method module, a synchronous compression transformation module, a multi-parameter joint analysis module, a three-dimensional flame morphology reconstruction module, and a fault detection module.

[0048] The data acquisition module uses a high-sensitivity flame chemiluminescence sensor to accurately capture the light signals generated by chemical reactions in the flame. The sensor array is arranged at different positions of the burner to obtain the overall chemiluminescence characteristics of the flame.

[0049] The intrinsic orthogonal decomposition method module accurately predicts faults by analyzing and identifying change patterns during the combustion process.

[0050] The synchronous compression transformation module performs time-frequency analysis on sensor data through a real-time SCT algorithm, captures instantaneous changes during the combustion process, reflects subtle changes in the combustion state, and improves the sensitivity of fault detection.

[0051] The multi-parameter joint analysis module, combining the results of synchronous compression transformation, uses a multi-parameter joint analysis algorithm to evaluate the flame state;

[0052] The three-dimensional flame shape reconstruction module uses computer vision technology to reconstruct the three-dimensional flame shape from flame images acquired from different angles.

[0053] The fault detection module analyzes the results of multi-parameter joint analysis and three-dimensional flame morphology reconstruction to detect abnormal flame behavior.

[0054] A computer device includes a memory and a processor, the memory storing a computer program, characterized in that the processor, when executing the computer program, implements the steps of an oscillating combustion fault detection method based on synchronous compression transformation.

[0055] A computer-readable storage medium having a computer program stored thereon, characterized in that, when the computer program is executed by a processor, it implements the steps of an oscillating combustion fault detection method based on synchronous compression transformation.

[0056] The beneficial effects of this invention are as follows: In terms of data acquisition, the invention utilizes high-speed photography technology to acquire flame images, which avoids the data lag problem caused by traditional pressure monitoring methods, enabling non-destructive and rapid detection of oscillating combustion faults; intrinsic orthogonal decomposition can reduce the dimensionality of flame images and extract features, retaining rich information of the flame images, reflecting the overall pulsation characteristics of the flame, and improving detection speed and accuracy; synchronous compression transformation performs energy rearrangement and instantaneous concentration through synchronous compression operators, making up for the shortcomings of low frequency resolution in traditional time-frequency analysis methods, and realizing accurate diagnosis and rapid detection of oscillating combustion faults. Attached Figure Description

[0057] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. Wherein:

[0058] Figure 1 This is a schematic flowchart of an oscillating combustion fault detection method based on synchronous compression transformation, provided as an embodiment of the present invention.

[0059] Figure 2 The detection result is provided by an embodiment of the present invention for an oscillating combustion fault detection method based on synchronous compression transformation.

[0060] Figure 3 The image shows the pressure signal SST results under two operating conditions: flame oscillation and stability, for an oscillation combustion fault detection method based on synchronous compression transformation, provided in an embodiment of the present invention.

[0061] Figure 4 This is a schematic diagram of the working module of an oscillating combustion fault detection system based on synchronous compression transformation, provided as an embodiment of the present invention. Detailed Implementation

[0062] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0063] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0064] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0065] This invention is described in detail with reference to the schematic diagrams. When detailing the embodiments of this invention, for ease of explanation, the cross-sectional views illustrating the device structure may be partially enlarged, not adhering to the usual scale. Furthermore, the schematic diagrams are merely examples and should not be construed as limiting the scope of protection of this invention. In actual fabrication, the three-dimensional spatial dimensions of length, width, and depth should be included.

[0066] Furthermore, in the description of this invention, it should be noted that the terms "upper," "lower," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. These terms are used solely for the convenience of describing the invention and for simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the invention. In addition, the terms "first," "second," or "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0067] Unless otherwise explicitly specified and limited, the terms "installation," "connection," and "joining" in this invention should be interpreted broadly. For example, they can refer to fixed connections, detachable connections, or integral connections; similarly, they can refer to mechanical connections, electrical connections, or direct connections, or indirect connections through an intermediate medium, or internal connections between two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0068] Example 1

[0069] Reference Figure 1 This is the first embodiment of the present invention, which provides a method for detecting oscillating combustion faults based on synchronous compression transformation, comprising:

[0070] S1: Acquire flame chemiluminescence signals and perform adaptive filtering and noise reduction processing on the acquired flame chemiluminescence signals.

[0071] Furthermore, the adaptive filtering noise reduction process includes, when performing oscillating combustion fault detection, distinguishing between noise and actual combustion oscillation signals, selecting an adaptive filter based on the steepest descent method, and combining noise estimation and signal enhancement with the filter.

[0072] The adaptive filter update rule is as follows:

[0073]

[0074] Where w(n) represents the filter weight vector, and n represents the discrete time step. Let represent the noise power estimate at time n, ∈ represent a constant, x(n) represent the input flame signal at time n, e(n) represent the error signal at time n, and α represent the forgetting factor.

[0075] S2: The improved intrinsic orthogonal decomposition method is used to extract the first-order modal time coefficients from the flame image, and the higher-order modes are used to obtain comprehensive combustion feature information.

[0076] Furthermore, the improved intrinsic orthogonal decomposition method includes performing POD analysis on the set of flame images after filter processing to obtain intrinsic modes and eigenvalues, and selecting the M eigenmodes with the highest energy according to the magnitude of the eigenvalues ​​to form a new mode set, representing the most significant spatial features in the data.

[0077] For each selected mode, a time coefficient is calculated. The time coefficient represents the change of the projection of the flame image onto each intrinsic mode over time. The calculation formula is as follows:

[0078]

[0079] Among them, c i (m) represents the time coefficient, and I(m) represents the m-th flame image frame. φ represents the mean of the image set. i (x,y) represents the eigenmode of the flame image, where i is the index of the mode;

[0080] The calculated time coefficients are stored in a set:

[0081]

[0082] in, This represents a set containing time coefficients for higher-order modes, which reveals subtle fluctuations in the combustion process and early signs of failure.

[0083] S3: Develop a real-time synchronous compression transformation algorithm to perform time-frequency analysis on the data and update the energy allocation in real time to respond to minute changes in the combustion state.

[0084] Furthermore, the development of the real-time synchronous compression transform algorithm includes rearranging the coefficients of the time-frequency transform using a synchronous compression operator, moving the time-frequency coefficients of the signal at any point in the time-frequency plane to the center of energy position, enhancing the energy concentration of the instantaneous frequency, applying a window function on the basis of the Fourier transform to divide the signal time domain, obtaining the frequency distribution under different time windows by sliding the window function, and then arranging these short-time spectra in time sequence to describe the time-varying law of the signal frequency components;

[0085] For a given time-varying signal The short-time Fourier transform window function is g(t), which, after STFT transformation, gives:

[0086]

[0087] Where G(t,f) represents the result of the Fourier transform, s(τ) represents the original signal, τ represents the time variable of the original signal, g(τ-t) represents the result of the Fourier transform, r represents the time variable, f represents the frequency variable, and i represents the imaginary unit.

[0088] Performing Fourier transforms on the time-varying harmonic signal and the window function signal yields the estimated instantaneous frequency in the STFT time-frequency representation, expressed as:

[0089]

[0090] Where θ(t,ω) represents the instantaneous frequency, ω represents the frequency variable, arg(G(t,f)) represents the argument, and Re represents taking the real part. This represents the partial derivative with respect to time t;

[0091] Using the instantaneous frequency estimation results described above, STFT coefficients with the same frequency are collected. Based on the properties of the Dirac function, the synchronization compression operator function ∫ is given. R δ(η-ω0(t,ω)) yields the synchronous compression transform, expressed as:

[0092] T s (t,η)=∫ R G(t,f)δ(η-θ(t,ω))dω

[0093] Among them, T s (t,η) represents the instantaneous frequency signal, δ(η-θ(t,ω)) represents the Dirac function, η represents the instantaneous frequency variable, the time-frequency coefficients are redistributed and arranged in the frequency direction, and the synchronous compression transformation process at time t is the redistribution of frequency points and the process that goes through all times.

[0094] S4: Combine sensor data to perform multi-parameter joint analysis to obtain combustion status assessment. Use multiple high-speed cameras to acquire flame images from different angles, and reconstruct the three-dimensional flame morphology through computer vision technology to provide fault detection information.

[0095] Furthermore, the multi-parameter joint analysis includes analyzing the data collected by the sensors to understand the distribution and characteristics, adjusting the parameters through optimization algorithms, and finding the optimal parameter combination through multiple iterations.

[0096] The combined parameters are substituted into the multi-parameter joint analysis formula to calculate the weighted contribution of each sensor. The weights take into account the different sensitivities of the sensors to angle, velocity, and light intensity, and a normalization function is applied to ensure the comparability of data from different sensors. The specific formula is as follows:

[0097]

[0098] Where G(θ,v,I) represents the master function for multi-parameter joint analysis, θ represents the flame tilt angle, v represents the flame combustion rate, I represents the flame radiation intensity, n represents the number of sensors, and Ω i f represents the effective measurement domain of the i-th sensor. i (θ i ,v i ,I i ) represents the weighted contribution, α i Let θ represent the angle parameter of the i-th sensor. i θ represents the angle value measured by the i-th sensor. 0i β represents the reference angle value of the i-th sensor. i γ represents the adjustment coefficient for the velocity parameter of the i-th sensor. i This represents the adjustment coefficient for the light intensity parameter of the i-th sensor. Let these represent the mean and standard deviation of the angle measurement value of the i-th sensor, respectively. Let represent the mean and standard deviation of the velocity measurement value of the i-th sensor, respectively.

[0099] Furthermore, the reconstruction of the three-dimensional flame morphology includes reconstructing the three-dimensional flame morphology using computer vision algorithms by combining the results of multi-parameter joint analysis.

[0100] By considering the changes in flame over time using a time-dependent function, a dynamic three-dimensional flame model is obtained:

[0101] F(x,y,z,t)=∫ Ω G(θ,v,I)·h(t)dt

[0102] h(t)=sin(ωt+φ)·e -λt

[0103] Where x, y, z represent the coordinate axes in three-dimensional space, h(t) represents the flame variation function over time, Ω represents the spatial domain of integration, φ represents the phase shift, and λ represents the attenuation coefficient; the complete morphology of the flame in three-dimensional space, including its variation over time, is given through the three-dimensional flame model.

[0104] S5: Build a fault prediction model to automatically complete the entire fault detection process and achieve early warning of combustion faults.

[0105] Furthermore, the fault prediction model includes comparing the calculated flame state and flame shape with a normal behavior threshold. If the calculation result exceeds a preset threshold range, it is considered that there is abnormal behavior.

[0106] When the flame temperature is below 1000K or above 2500K, the abnormal behavior is identified as incomplete combustion or burner blockage. The solution is to adjust the oxygen / fuel ratio of the burner and check and clean the carbon deposits and sediment inside the burner.

[0107] When the flame oscillation frequency is below 0.5Hz or above 2Hz, and the flame shape is below 0.5 or above 1.5, the abnormal behavior is considered to be due to damage to the internal structure of the burner or unstable fuel supply. The solutions are to check the fuel supply system to maintain a stable fuel flow; check whether the burner nozzles and combustion chamber are blocked; and check whether the internal structure is damaged.

[0108] If the contrast of the flame image is below 0.6 or above 1.5, the abnormal behavior is considered to be caused by improper camera settings or the characteristics of the flame itself. The solution is to adjust the camera parameters to improve image quality, check whether the camera lens is contaminated or damaged, and check the brightness and color characteristics of the flame.

[0109] The detected abnormal behaviors and their causes are summarized as fault detection information and recorded in the database for subsequent analysis and reporting. Fault detection reports are generated regularly to monitor the health and performance of the combustion process.

[0110] Example 2

[0111] Reference Figure 2 and Figure 3 As an embodiment of the present invention, an oscillating combustion fault detection method based on synchronous compression transformation is provided. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through experiments.

[0112] Figure 2For the two verification conditions involved in the embodiments of the present invention, the pressure signals of the test bench used for verification under oscillation and stable conditions were first collected and subjected to STFT analysis. Under the oscillation condition, a clear oscillation frequency was identified around t=0.75s, but the frequency resolution was poor; while for the stable condition, no clear oscillation frequency appeared. Figure 3 The analysis results of synchronous compression transform of pressure signals under two operating conditions are presented. For the oscillating condition, the synchronous compression transform concentrates the energy of the measured signal, superimposing the spectrum within the pseudo-frequency range to concentrate the energy at the actual instantaneous frequency, improving time-frequency convergence and significantly enhancing frequency resolution. The STFT analysis results of the flame CH* light intensity signal under both operating conditions show that a significant dominant frequency response appears around t=0.2s in the oscillating condition, while no oscillating dominant frequency appears in the stable condition. Furthermore, the flame CH* light intensity signal exhibits a significant time advantage over the pressure signal in diagnostic speed. The synchronous compression transform analysis results of the flame CH* light intensity signal under the two operating conditions demonstrate that the frequency domain resolution of the time-frequency analysis results is significantly improved. The flame image was subjected to intrinsic orthogonal decomposition for data dimensionality reduction and feature extraction to obtain the first-order modal time coefficient. This coefficient was then used as the data input for STFT (Sequential Transform-Through) fault diagnosis. Using the first-order modal time coefficient of intrinsic orthogonal decomposition as the input to STFT, a significant dominant frequency response appeared at t=0.01s, greatly improving the diagnostic speed. Similar to pressure signals and CH* light intensity signals, low frequency resolution was also a problem. Through synchronous compression transform analysis, the frequency resolution of the spectrogram was greatly improved, and the frequency positioning was more accurate.

[0113] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

[0114] Example 3

[0115] The third embodiment of the present invention differs from the first two embodiments in that:

[0116] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0117] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0118] More specific examples of computer-readable media (a non-exhaustive list) include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0119] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0120] Example 4

[0121] Reference Figure 4 As an embodiment of the present invention, an oscillating combustion fault detection system based on synchronous compression transformation is provided, characterized in that it includes a data acquisition module, an intrinsic orthogonal decomposition method module, a synchronous compression transformation module, a multi-parameter joint analysis module, a three-dimensional flame morphology reconstruction module, and a fault detection module.

[0122] The data acquisition module uses a high-sensitivity flame chemiluminescence sensor to accurately capture the light signals generated by chemical reactions in the flame. The sensor array is arranged at different positions in the burner to obtain the overall chemiluminescence characteristics of the flame.

[0123] The intrinsic orthogonal decomposition method module accurately predicts faults by analyzing and identifying change patterns during the combustion process.

[0124] The synchronous compression transformation module performs time-frequency analysis on sensor data through the real-time SCT algorithm, captures instantaneous changes in the combustion process, reflects subtle changes in the combustion state, and improves the sensitivity of fault detection.

[0125] The multi-parameter joint analysis module, combining the results of synchronous compression transformation, uses a multi-parameter joint analysis algorithm to evaluate the flame state.

[0126] The 3D flame shape reconstruction module uses computer vision technology to reconstruct the 3D flame shape from flame images acquired from different angles.

[0127] The fault detection module analyzes the results of multi-parameter joint analysis and three-dimensional flame morphology reconstruction to detect abnormal flame behavior.

[0128] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for detecting oscillating combustion faults based on synchronous compression transformation, characterized in that: include, The flame chemiluminescence signal is acquired, and the acquired flame chemiluminescence signal is subjected to adaptive filtering and noise reduction processing; An improved intrinsic orthogonal decomposition method is used to extract the first-order mode time coefficients from flame images, and higher-order modes are used to obtain comprehensive combustion feature information. The improved intrinsic orthogonal decomposition method includes performing POD analysis on the flame image set after filter processing to obtain intrinsic modes and eigenvalues. Based on the magnitude of the eigenvalues, the M eigenmodes with the highest energy are selected to form a new mode set, representing the most significant spatial features in the data. For each selected mode, a time coefficient is calculated. The time coefficient represents the change of the projection of the flame image onto each intrinsic mode over time. The calculation formula is as follows: in, Indicates the time coefficient. This represents the m-th flame image frame. This represents the mean of the image set. The i represents the eigenmode of the flame image, where i is the index of the mode; The calculated time coefficients are stored in a set: in, This represents a set containing time coefficients for higher-order modes, which reveals subtle fluctuations in the combustion process and early signs of failure. Develop a real-time synchronous compression transformation algorithm to perform time-frequency analysis on the data and update the energy allocation in real time to respond to minute changes in the combustion state; The development of the real-time synchronous compression transform algorithm includes rearranging the coefficients of the time-frequency transform using a synchronous compression operator, moving the time-frequency coefficients at any point in the time-frequency plane to the center of energy, enhancing the energy concentration of the instantaneous frequency, applying a window function on the basis of the Fourier transform to divide the signal time domain, obtaining the frequency distribution under different time windows by sliding the window function, and then arranging these short-time spectra in time sequence to describe the time-varying law of the signal frequency components. For a given time-varying signal The short-time Fourier transform window function is g ( t After STFT transformation, we have: in, This represents the result of the Fourier transform. Represents the original signal. The time variable representing the original signal, Represents a time variable. Performing Fourier transforms on the time-varying harmonic signal and the window function signal yields the estimated instantaneous frequency in the STFT time-frequency representation, expressed as: in, Indicates instantaneous frequency. Represents frequency variables. Indicates the argument. Indicates taking the real part, Indicates time t The partial derivatives; Using the instantaneous frequency estimation results described above, STFT coefficients with the same frequency are collected. Based on the properties of the Dirac function, a synchronization compression operator function is given. The synchronous compression transform is obtained, expressed as: in, Indicates instantaneous frequency signal, Represents the Dirac function, Representing instantaneous frequency variables, the time-frequency coefficients are redistributed and arranged in the frequency direction, and in time... t The synchronous compression transformation process of time, that is, the redistribution of frequency points and the process going through all time points; By combining sensor data and performing multi-parameter joint analysis, a combustion status assessment is obtained. Multiple high-speed cameras are used to acquire flame images from different angles, and computer vision technology is used to reconstruct the three-dimensional flame morphology to provide fault detection information. Build a fault prediction model to automatically complete the entire fault detection process and achieve early warning of combustion faults.

2. The oscillating combustion fault detection method based on synchronous compression transformation as described in claim 1, characterized in that: The adaptive filtering noise reduction process includes, when performing oscillating combustion fault detection, distinguishing between noise and actual combustion oscillation signals, selecting an adaptive filter based on the steepest descent method, and combining noise estimation and signal enhancement into the filter. The adaptive filter update rule is as follows: in, Represents the filter weight vector. Indicates the discrete time step. This represents the noise power estimate at time n. Represents a constant. This represents the input flame signal at time n. This represents the error signal at time n. This represents the forgetting factor.

3. The oscillating combustion fault detection method based on synchronous compression transformation as described in claim 2, characterized in that: The multi-parameter joint analysis includes analyzing the data collected by the sensors to understand the distribution and characteristics, adjusting the parameters through optimization algorithms, and finding the optimal parameter combination through multiple iterations. The combined parameters are substituted into the multi-parameter joint analysis formula to calculate the weighted contribution of each sensor. The weights take into account the different sensitivities of the sensors to angle, velocity, and light intensity, and a normalization function is applied to ensure the comparability of data from different sensors. The specific formula is as follows: in, This represents the main function for multi-parameter joint analysis. Indicates the angle of the flame tilt. Indicates the speed of flame combustion. This indicates the flame radiation intensity, and Nom indicates the number of sensors. This represents the effective measurement domain of the k-th sensor. Indicates weighted contribution. This represents the angle parameter of the k-th sensor. This represents the angle value measured by the k-th sensor. This represents the reference angle value of the k-th sensor. This represents the adjustment coefficient for the speed parameter of the k-th sensor. This represents the adjustment coefficient for the light intensity parameter of the k-th sensor. , Let these represent the mean and standard deviation of the angle measurement value of the k-th sensor, respectively. Let represent the mean and standard deviation of the velocity measurement value of the k-th sensor, respectively.

4. The oscillating combustion fault detection method based on synchronous compression transformation as described in claim 3, characterized in that: The reconstruction of the three-dimensional flame morphology includes using computer vision algorithms to reconstruct the three-dimensional flame morphology by combining the results of multi-parameter joint analysis. By considering the changes in flame over time using a time-dependent function, a dynamic three-dimensional flame model is obtained: in, The coordinate axes represent three-dimensional space. This represents a function that describes the change of flame over time. The spatial field representing the integral, This indicates the phase offset. It represents the attenuation coefficient; the complete shape of the flame in three-dimensional space is given through a three-dimensional flame model, including its changes over time.

5. The oscillating combustion fault detection method based on synchronous compression transformation as described in claim 4, characterized in that: The fault prediction model includes comparing the calculated flame state and flame shape with a normal behavior threshold. If the calculation result exceeds the preset threshold range, it is considered that there is abnormal behavior. When the flame temperature is below 1000 K or above 2500 K, the abnormal behavior is identified as incomplete combustion or burner blockage. The solution is to adjust the oxygen / fuel ratio of the burner and check and clean the carbon deposits and sediment inside the burner. When the flame oscillation frequency is below 0.5 Hz or above 2 Hz, and the flame shape is below 0.5 or above 1.5, the abnormal behavior is considered to be due to damage to the internal structure of the burner or unstable fuel supply. The solutions are to check the fuel supply system to maintain a stable fuel flow; check whether the burner nozzles and combustion chamber are blocked; and check whether the internal structure is damaged. If the contrast of the flame image is below 0.6 or above 1.5, the abnormal behavior is considered to be caused by improper camera settings or the characteristics of the flame itself. The solution is to adjust the camera parameters to improve image quality, check whether the camera lens is contaminated or damaged, and check the brightness and color characteristics of the flame. The detected abnormal behaviors and their causes are summarized as fault detection information and recorded in the database for subsequent analysis and reporting. Fault detection reports are generated regularly to monitor the health and performance of the combustion process.

6. A system employing the oscillating combustion fault detection method based on synchronous compression transformation as described in any one of claims 1 to 5, characterized in that: It includes a data acquisition module, an intrinsic orthogonal decomposition method module, a synchronous compression transformation module, a multi-parameter joint analysis module, a three-dimensional flame morphology reconstruction module, and a fault detection module; The data acquisition module uses a high-sensitivity flame chemiluminescence sensor to accurately capture the light signals generated by chemical reactions in the flame. The sensor array is arranged at different positions of the burner to obtain the overall chemiluminescence characteristics of the flame. The intrinsic orthogonal decomposition method module accurately predicts faults by analyzing and identifying change patterns during the combustion process. The synchronous compression transformation module performs time-frequency analysis on sensor data through a real-time SCT algorithm, captures instantaneous changes during the combustion process, reflects subtle changes in the combustion state, and improves the sensitivity of fault detection. The multi-parameter joint analysis module, combining the results of synchronous compression transformation, uses a multi-parameter joint analysis algorithm to evaluate the flame state; The three-dimensional flame shape reconstruction module uses computer vision technology to reconstruct the three-dimensional flame shape from flame images acquired from different angles. The fault detection module analyzes the results of multi-parameter joint analysis and three-dimensional flame morphology reconstruction to detect abnormal flame behavior.

7. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 5.

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