A method for measuring gas path particle concentration based on characteristic waveform recognition

Abstract

The present invention relates to a method for measuring air path particle concentration based on characteristic waveform recognition. The adopted measurement system includes an electrostatic sensor, a signal preprocessing module, a signal acquisition module, and a particle concentration calculation module. The air path particle concentration measurement method comprises the following steps: obtaining a preliminarily preprocessed electrostatic sensor signal; extracting effective waveform positions through cross-correlation matching; extracting characteristic values: taking the absolute values ​​of the peak values ​​of waveform signals that meet similar conditions in the original acquired signal and summing them as characteristic values; establishing a corresponding relationship model between characteristic values ​​and particle concentrations; and monitoring particle concentration.

Description

Technical Field

[0001] The present invention relates to the technical field of signal processing, and in particular to a real-time measurement method for air path particle concentration based on characteristic waveform recognition. Background Art

[0002] In gas flow systems, the state of the gas inside the pipeline is a key concern in scientific research and engineering applications. In particular, the particle concentration in the gas within the pipeline is crucial for assessing the operating efficiency of the gas flow system and the health of its upstream and downstream equipment. For example, in the intake and exhaust ducts of aircraft engines, monitoring particle concentration can effectively assess external environmental conditions, enabling proactive measures to address potential threats to the engine's internal environment. Furthermore, this monitoring helps detect problems such as engine blade damage caused by particle abrasion, ensuring that engine performance is not affected. During the engine design and development phase, precisely controlling the particle concentration in the experimental environment can significantly improve R&D efficiency and engine performance. Similarly, in pulverized coal conveying pipelines, monitoring particle concentration plays an important role in optimizing pipeline transport capacity and improving coal combustion efficiency. Therefore, developing and improving particle identification technology within gas flow pipelines has far-reaching practical significance for all fields involving particle concentration measurement in gas flow systems.

[0003] Real-time measurement of airflow particle concentration based on characteristic waveform recognition exploits the charge carried by particles due to friction with the airflow pipe. This method utilizes charge sensing and characteristic waveform recognition to achieve airflow particle concentration measurement. This method offers the advantages of non-contact, online measurement, high sensitivity, and low cost. Currently, research both domestically and internationally focuses on monitoring exhaust particulate matter from aircraft engine exhaust nozzles. Existing technologies primarily focus on fault diagnosis of aircraft engines and other equipment based on the charge carried by abnormal particles, but lack specific methods for measuring particle concentration. Furthermore, most existing studies use the root mean square (RMS) value (RMS) of the acquired signal as a characteristic parameter to establish a correlation with the particle concentration within the pipe. The RMS value is typically used to characterize the dispersion or fluctuation of a data set, but in particle concentration measurement, it more closely reflects the level of signal chaos within the pipe. The principle is that charged particles passing through the sensor cross section cause a sudden change in the waveform. However, actual acquired data demonstrates that, in addition to the waveform changes caused by charged particles, there is also a certain amount of noise, which is difficult to completely eliminate using conventional filtering methods. This results in the noise signal being included in the calculation of the RMS value, making the reflected signal chaos in the pipeline higher than the actual situation. Summary of the Invention

[0004] The present invention addresses the shortcomings of existing methods for measuring particle concentration in gas pipelines by providing a method for measuring particle concentration in gas pipelines based on characteristic waveform recognition, enabling real-time monitoring. This method takes into account the Gaussian-like waveform characteristic of the sensor signal when charged gas pipeline particles pass through the sensor cross-section. Using waveform cross-correlation, it accurately identifies the characteristic waveform and establishes a concentration-correlation curve, enabling real-time particle concentration monitoring in gas pipelines.

[0005] To achieve the above object, the present invention is implemented through the following technical solutions:

[0006] A method for measuring air path particle concentration based on characteristic waveform recognition, wherein the measurement system includes an electrostatic sensor, a signal preprocessing module, a signal acquisition module, and a particle concentration calculation module; the method for measuring air path particle concentration includes the following steps:

[0007] S1: Obtaining preliminary pre-processed electrostatic sensor signals;

[0008] S2: Cross-correlation matching to extract the effective waveform position, the method is:

[0009] The electrostatic sensing signal after preliminary preprocessing is collected and transmitted to the particle concentration calculation module for processing;

[0010] The formula for determining the contrast waveform used for cross-correlation comparison is as follows:

[0011]

[0012] Among them, the parameters α, μ, and σ are unknown coefficients, and the parameter α is selected in the range of 0.2-1.0; the parameters μ and σ jointly determine the shape characteristics of the waveform. The parameter design needs to consider the sampling rate and the gas flow rate in the gas pipeline;

[0013] For a certain gas pipeline structure, the values ​​of parameters α, μ, and σ are determined through experiments, and comparative waveforms are obtained;

[0014] The absolute value of the collected original electrostatic sensor waveform signal is taken and then cross-correlated with the comparison waveform to obtain the waveform signal that meets the similarity condition in the original electrostatic sensor waveform;

[0015] S3: Feature value extraction: take the absolute value of the peak value of the waveform signal that meets the similarity condition in the original acquisition signal and sum it as the feature value;

[0016] S4: Establish a model for the relationship between characteristic values ​​and particle concentration;

[0017] S5: Particle concentration monitoring.

[0018] Furthermore, the electrostatic sensor is embedded inside the gas pipeline to obtain the electrostatic induction signal generated when the charged circuit particles pass through; the signal preprocessing module is used to receive the original sampled electrostatic sensor signal and perform preprocessing; the signal acquisition module is used to perform high-frequency sampling of the preprocessed electrostatic sensor signal; the particle concentration calculation module is used to complete the cross-correlation comparison between the high-frequency sampling signal and the characteristic waveform and the subsequent particle concentration calculation work.

[0019] Furthermore, before step S1, the definition and calculation method of the gas path particle concentration in the gas path pipeline are clarified:

[0020] In the gas pipeline, the gas path particle concentration in the pipeline is defined as the ratio of the mass of the gas path particles passing through the sensor cross section to the volume of air passing through in a certain period of time. The volume of air passing through per unit time is the product of the gas flow rate in the pipeline and the time, that is,

[0021]

[0022] For annular circulating gas pipelines, assuming that the flow velocity of the gas particles in the pipeline is consistent with the wind speed in the pipeline and is evenly distributed in the pipeline, the time t0 for a single particle to circulate once is the ratio of the total volume V0 in the pipeline to the fan flow rate Q, that is,

[0023]

[0024] In a certain time t, the total weight of the gas path particles passing through the sensor cross section is

[0025]

[0026] Where m is the mass of particles added to the air circulation pipe; the total volume of air passing through the sensor cross section within a certain time t is

[0027] V=Q·t

[0028] In a certain time t, the concentration of gas path particles passing through the sensor cross section is

[0029]

[0030] Furthermore, the method of step S1 is: for the charge signal obtained by the electrostatic sensor, the signal preprocessing module sequentially processes it through charge amplification, voltage amplification, high-pass filtering and low-pass filtering circuits; the high-pass filtering and low-pass filtering modules are used to filter out power frequency interference and high-frequency noise respectively, thereby obtaining an electrostatic sensing signal that has undergone preliminary preprocessing.

[0031] Furthermore, in step S2, the method of cross-correlation processing is as follows: by cross-correlation calculation, the comparison waveform is scanned and compared with each position of the original waveform, the similarity between each position in the original waveform and the comparison waveform is quantified, and the waveform after cross-correlation processing is obtained; by setting a threshold, the peak waveform signal that meets the similarity condition is extracted to obtain the waveform signal that meets the similarity condition in the original electrostatic sensor waveform.

[0032] Furthermore, S4: a method for establishing a corresponding relationship model between characteristic values ​​and particle concentrations is as follows: performing experiments by changing the particle concentration in the pipeline, obtaining corresponding characteristic value results under different particle concentrations, and using a linear fitting method to establish a corresponding relationship model between characteristic values ​​and particle concentrations, and completing system calibration.

[0033] In response to the requirement for real-time monitoring of particle concentration, this paper proposes an online airway particle detection method based on airway pipeline particle identification. This method has the following advantages:

[0034] (1) A calculation and analysis method for the particle concentration in the annular circulation pipeline is proposed, which solves the problem of difficult calibration and control of particle concentration during the experiment.

[0035] (2) The analysis shows that the induced charge on the electrostatic sensor and the charge of the particles in the gas pipeline present a unique single-value correspondence. On this basis, a method for preprocessing the electrostatic sensor signal is proposed to provide support for subsequent signal matching and characteristic value calibration.

[0036] (3) By cross-correlating the collected signal with the Gaussian-like signal to identify the particle characteristic waveform and further establish a characteristic relationship, it is more scientific and further improves the measurement sensitivity, better meeting the real-time monitoring performance of particle concentration required by the gas pipeline.

[0037] (4) The method of extracting the useful signal characteristics of the original waveform after identifying the gas pipeline waveform and performing linear calibration with the particle concentration in the pipeline can further improve the resolution and sensitivity of particle online monitoring. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 This is a structural block diagram of the air path particle online testing system based on air path pipeline particle identification of the present invention.

[0039] Figure 2 This is a flow chart of the on-line gas path particle testing method based on gas path pipeline particle identification of the present invention.

[0040] Figure 3 : is a section of collected signal graph, in which the dotted line is the signal curve before particles are injected, and the solid line is the signal curve after particles are injected.

[0041] Figure 4 This is a typical Gaussian-like curve for a single charge used in cross-correlation calculations.

[0042] Figure 5 For the general Figure 2 The result after taking the absolute value of the collected signal and performing cross-correlation algorithm processing.

[0043] Figure 6 This is the algorithm flow chart of the particle concentration calculation module. DETAILED DESCRIPTION

[0044] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0045] To achieve the above object, the present invention is implemented through the following technical solutions:

[0046] like Figure 1 As shown, the real-time measurement system for gas path particle concentration designed by the present invention includes an electrostatic sensor, a signal preprocessing module, a signal acquisition module, and a particle concentration calculation module.

[0047] The electrostatic sensor is embedded inside the gas pipeline and is used to obtain the electrostatic induction signal generated when charged particles pass through it; the signal preprocessing module is used to receive the original sampled electrostatic sensor signal and realize the conversion from charge signal to voltage signal, as well as amplification and noise reduction preprocessing; the signal acquisition module is used to realize high-frequency sampling of the preprocessed electrostatic sensor signal; the particle concentration calculation module is used to complete the cross-correlation comparison between the collected signal and the characteristic waveform and the subsequent particle concentration calculation.

[0048] The real-time measurement method of gas path particle concentration mainly includes the following steps:

[0049] S1: Clarify the definition and calculation method of gas path particle concentration in gas path pipelines

[0050] In the gas pipeline, the gas path particle concentration in the pipeline is defined as the ratio of the mass of the gas path particles passing through the sensor cross section to the volume of air passing through in a certain period of time. The volume of air passing through per unit time is the product of the gas flow rate in the pipeline and the time, that is,

[0051]

[0052] For annular circulating air pipelines, the same air path particle will pass through the sensor cross section multiple times per unit time. This is different from the single passing of air path particles in ordinary air path pipelines, and the method of calculating concentration also changes accordingly. Assuming that the flow velocity of air path particles in the pipeline is consistent with the wind speed in the pipeline and is evenly distributed in the pipeline, the time t0 for a single particle to circulate once is the ratio of the total volume V0 in the pipeline to the fan flow rate Q, that is,

[0053]

[0054] Then within a certain time t, the total weight of the gas path particles passing through the sensor cross section is

[0055]

[0056] Where m is the mass of particles added to the air circulation pipe. The total volume of air passing through the sensor cross section within a certain time t is

[0057] V=Q·t

[0058] Therefore, within a certain time t, the concentration of gas path particles passing through the sensor cross section is

[0059]

[0060] S2: Preliminary preprocessing of electrostatic sensor signals

[0061] Electrostatic sensors are used to capture the electrostatic induction signals generated by charged particles within gas pipelines. Based on the principle of electrostatic induction, when particles in the gas pipeline acquire an electric charge through friction or contact, these charged particles generate a constantly changing electrostatic field around the electrostatic sensor, causing the induced charge on the sensor probe to fluctuate accordingly. According to the uniqueness theorem of electrostatic fields, given the boundary conditions of the pipeline, insulator, and sensor electrodes, as well as the static spatial charge distribution of the gas pipeline system, the induced charge on the sensor's metal electrode uniquely corresponds to the charge carried by the particles. This relationship provides a theoretical basis for quantifying the charge carried by particles in the gas pipeline by monitoring the output signal of the electrostatic sensor.

[0062] The signal preprocessing module processes the charge signal collected by the electrostatic sensor using a sequence of charge amplification, voltage amplification, high-pass filtering, and low-pass filtering circuits. First, the charge amplification circuit converts and amplifies the raw charge signal into a voltage signal for subsequent processing. The voltage amplification module then further amplifies this voltage signal to enhance signal strength. The high-pass filtering and low-pass filtering modules, respectively, filter out power frequency interference and high-frequency noise, ultimately producing a pre-processed electrostatic sensor signal.

[0063] S3: Cross-correlation matching to extract valid waveform positions

[0064] The signal acquisition module is used to collect the electrostatic sensor signal processed by the signal preprocessing module and transmit it to the particle concentration calculation module to analyze the collected signal waveform. Figure 3 : is a section of collected signal graph, in which the dotted line is the signal curve before particles are injected, and the solid line is the signal curve after particles are injected.

[0065] The results show that compared with the signal without particle injection, the signal generated by charged particles exhibits a significant Gaussian distribution characteristic. Therefore, the useful signal generated by charged particles can be extracted from the signal through feature recognition methods and then analyzed.

[0066] By analyzing the signal collection results of repeated experiments and combining the waveform information such as the amplitude and Gaussian distribution characteristics of the induced signal generated by the charged particles, the waveform formula for cross-correlation comparison was finally determined as follows:

[0067]

[0068] Based on the experimental results, the final parameters were determined to be α = 0.3, μ = 0.4, and σ = 1.5 under this experimental environment. The parameter α determines the amplitude of the comparison waveform. After the system circuit is amplified, this parameter is selected within the range of 0.2-1.0 when applied to different working conditions. The parameters μ and σ jointly determine the shape characteristics of the waveform. Parameter design needs to consider the influence of the system sampling rate and the gas flow rate in the gas pipeline, and depends on the specific situation.

[0069] The waveforms used for cross-correlation comparison in this experiment are as follows Figure 4 As shown in the figure, the collected signal is cross-correlated with the waveform curve. Through the cross-correlation calculation, the comparison waveform is scanned and compared with each position of the original waveform. The similarity between each position in the original waveform and the waveform used for cross-correlation comparison is quantified to obtain the waveform after cross-correlation processing. Therefore, in the result of cross-correlation processing, each maximum value can reflect the similarity between each spike signal in the original data and the comparison waveform. Then, by setting a threshold method, spike waveform signals that meet the high similarity condition can be extracted. Figure 5 For the general Figure 3 After collecting the absolute value of the signal, Figure 4 The result after the cross-correlation algorithm is applied to the contrast signal. When the threshold is set to 0.2, the number of maximum values ​​that can be obtained is 6, which corresponds to Figure 4 The number of spike waveforms that meet similar conditions in the original acquired signal is 6.

[0070] S4: Feature extraction

[0071] After completing the cross-correlation matching and extracting the effective waveform in step S3, the waveform signal that meets the similarity condition in the original acquisition signal is obtained. At the same time, according to the theoretical analysis of the pre-processing part of the annular electrostatic sensor signal, it can be known that the amplitude of the electrostatic induction signal waveform is proportional to the charge of the gas path particles passing through the pipeline, and can also be considered to be proportional to the particle concentration in the pipeline. Therefore, the peak value of the waveform signal that meets the similarity condition in the original acquisition signal is extracted, and the absolute value is summed as the characteristic value. For example, Figure 3After cross-correlation comparison, there are 6 peak signals that meet the similarity condition. The peak signals of these 6 peaks are extracted, their absolute values ​​are taken and summed up, which is used as the characteristic value of this segment of signal. The characteristic value can be used as the basis for judging the total amount of charged particles in the air path passing through the cross-section of the electrostatic sensor within a period of time, and can also reflect the concentration of particles passing through the electrostatic sensor during this period of time.

[0072] S5: Establish a model for the relationship between eigenvalues ​​and particle concentrations

[0073] On the basis of step S4, the particle concentration in the pipeline is continuously changed to obtain the corresponding characteristic value results under different particle concentrations, and the linear fitting method is used to establish the peak sum as the corresponding relationship model between the characteristic value and the particle concentration.

[0074] S6: Calibration result inspection

[0075] Based on the corresponding relationship model between the characteristic value and the particle concentration established in step S5, the calibration results are tested. Without changing the external environment, within the particle measurement range, the particle concentrations of another M values ​​(generally M ≥ 5) are detected by controlling the mass of the injected particles. The measured values ​​and actual values ​​of the M particle concentrations are obtained, and the error between the measured values ​​and the actual values ​​is compared. If the error meets the requirements, proceed to the next step; otherwise, return to step S5 and recalibrate.

[0076] S7: Complete system calibration and realize real-time monitoring of particle concentration.

[0077] The present invention is not limited to the embodiments described above. The above description of the specific embodiments is intended to illustrate and explain the technical solutions of the present invention. The above specific embodiments are merely illustrative and not restrictive. Those skilled in the art, guided by the present invention, may make various modifications without departing from the spirit of the present invention and the scope of the claims.

Claims

1. A method for measuring air path particle concentration based on characteristic waveform recognition. The measurement system employed includes an electrostatic sensor, a signal preprocessing module, a signal acquisition module, and a particle concentration calculation module. The method comprises the following steps: S1: Obtaining preliminary pre-processed electrostatic sensor signals; S2: Cross-correlation matching to extract the effective waveform position, the method is: The electrostatic sensing signal after preliminary preprocessing is collected and transmitted to the particle concentration calculation module for processing; The formula for determining the contrast waveform used for cross-correlation comparison is as follows: Among them, the parameters α, μ, and σ are unknown coefficients, and the parameter α is selected in the range of 0.2-1.0; the parameters μ and σ jointly determine the shape characteristics of the waveform. The parameter design needs to consider the sampling rate and the gas flow rate in the gas pipeline; For a certain gas pipeline structure, the values ​​of parameters α, μ, and σ are determined through experiments, and comparative waveforms are obtained; The absolute value of the collected original electrostatic sensor waveform signal is taken and then cross-correlated with the comparison waveform to obtain the waveform signal that meets the similarity condition in the original electrostatic sensor waveform; S3: Feature value extraction: take the absolute value of the peak value of the waveform signal that meets the similarity condition in the original acquisition signal and sum it as the feature value; S4: Establish a model for the relationship between characteristic values ​​and particle concentration; S5: Particle concentration monitoring.

2. The method for measuring air path particle concentration based on characteristic waveform recognition according to claim 1, characterized in that: The electrostatic sensor is embedded in the gas pipeline to obtain the electrostatic induction signal generated when charged particles pass through. The signal preprocessing module is used to receive the original sampled electrostatic sensor signal and implement preprocessing; the signal acquisition module is used to implement high-frequency sampling of the preprocessed electrostatic sensor signal; The particle concentration calculation module is used to complete the cross-correlation comparison between the high-frequency sampling signal and the characteristic waveform and the subsequent particle concentration calculation.

3. The method for measuring air path particle concentration based on characteristic waveform recognition according to claim 1, characterized in that: Before step S1, the definition and calculation method of the gas path particle concentration in the gas path pipeline should be clarified: In the gas pipeline, the gas path particle concentration in the pipeline is defined as the ratio of the mass of the gas path particles passing through the sensor cross section to the volume of air passing through in a certain period of time. The volume of air passing through per unit time is the product of the gas flow rate in the pipeline and the time, that is, For annular circulating gas pipelines, assuming that the flow velocity of the gas particles in the pipeline is consistent with the wind speed in the pipeline and is evenly distributed in the pipeline, the time t0 for a single particle to circulate once is the ratio of the total volume V0 in the pipeline to the fan flow rate Q, that is, In a certain time t, the total weight of the gas path particles passing through the sensor cross section is Where m is the mass of particles added to the air circulation pipe; the total volume of air passing through the sensor cross section within a certain time t is V = Q t In a certain time t, the concentration of gas path particles passing through the sensor cross section is 4. The method for measuring air path particle concentration based on characteristic waveform recognition according to claim 1, characterized in that: The method of step S1 is as follows: for the charge signal obtained by the electrostatic sensor, the signal preprocessing module sequentially processes it through charge amplification, voltage amplification, high-pass filtering and low-pass filtering circuits; The high-pass filter and low-pass filter modules are used to filter out power frequency interference and high-frequency noise respectively, so as to obtain the electrostatic sensing signal after preliminary preprocessing.

5. The method for measuring air path particle concentration based on characteristic waveform recognition according to claim 1, characterized in that: In step S2, the cross-correlation processing method is as follows: by performing cross-correlation calculation, the comparison waveform is scanned and compared with each position of the original waveform, and the similarity between each position in the original waveform and the comparison waveform is quantified to obtain a waveform after cross-correlation processing; By setting a threshold value, the peak waveform signal that meets the similarity condition is extracted, and the waveform signal that meets the similarity condition in the original electrostatic sensor waveform is obtained.

6. The method for measuring air path particle concentration based on characteristic waveform recognition according to claim 5, characterized in that: The threshold is set to 0.

2.

7. The method for measuring air path particle concentration based on characteristic waveform recognition according to claim 1, characterized in that: S4: The method for establishing a corresponding relationship model between characteristic values ​​and particle concentrations is: changing the particle concentration in the pipeline to conduct experiments, obtaining the corresponding characteristic value results under different particle concentrations, using the linear fitting method to establish a corresponding relationship model between characteristic values ​​and particle concentrations, and complete system calibration.

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