Coal mine explosion-proof equipment dynamic detection method based on laser Doppler and pressure data

Abstract

The invention relates to the technical field of intelligent detection, and discloses a coal mine explosion-proof equipment dynamic detection method based on laser Doppler and pressure data. The method comprises the following steps: setting a measurement area and a scanning scheme according to the geometrical shape, the working environment and the measurement requirement of the explosion-proof shell, arranging a pressure sensor and a laser Doppler vibration meter at a measurement point, carrying out an explosion experiment, and collecting laser Doppler data and pressure data; the method comprises the following steps: collecting data, performing feature extraction on the collected data, respectively obtaining a vibration feature vector and a pressure feature vector, inputting the feature vectors into a deep learning model for fusion, generating a fusion feature vector, finally inputting the fusion feature vector into a comprehensive evaluation model, outputting a performance score of the explosion-proof shell, comparing the performance score with a threshold value, and judging the explosion-proof performance of the explosion-proof shell. According to the invention, high-precision and non-contact dynamic response measurement and performance evaluation can be carried out on the coal mine explosion-proof equipment under the action of explosion impact, and the efficiency and accuracy of dynamic detection of the explosion-proof equipment are improved.

Description

Technical Field

[0001] The present application relates to the field of intelligent detection technology, and in particular to a dynamic detection method for coal mine explosion-proof equipment based on laser Doppler and pressure data. Background Art

[0002] Coal accounts for more than 90% of fossil energy resources and is the most stable, economical, and self-secure energy. Coal mining has complex geological conditions and high content of flammable and explosive gases. Failure of explosion-proof equipment leading to mechanical and electrical accidents, gas explosions and other disasters are particularly prominent. Therefore, the safety of coal mine explosion-proof equipment is directly related to whether coal mine safety production work can be carried out smoothly. It is necessary to improve the explosion-proof performance evaluation capability of coal mine explosion-proof equipment to avoid safety accidents caused by gas explosions.

[0003] The current measurement and evaluation system of coal mine explosion-proof equipment is relatively basic, lacking systematic research on the evolution theory of coal mine explosion-proof equipment and the testing methods of main performance parameters. The existing theories and technologies cannot effectively solve the problem of intelligent testing and safety evaluation of coal mine explosion-proof equipment in my country. In terms of traditional testing, the strength of the explosion-proof enclosure is mainly verified through pressure resistance testing. However, the mechanical changes of coal mine explosion-proof enclosures under the action of gas explosions are a complex process. Analysis from the perspective of pressure alone cannot reveal the mechanical changes inside the shell and effectively evaluate them. And if other contact measurement methods are considered, there are problems with sensor installation and sensor detachment due to severe explosions. All of the above cannot effectively evaluate the explosion-proof enclosure of coal mines.

[0004] The introduction of laser Doppler measurement technology can effectively overcome these problems. In addition to avoiding the influence of explosion conditions, installation and other factors on traditional contact measurement methods, it has the advantages of high accuracy and fast response. More importantly, it can measure the dynamic parameters of explosion-proof shells to reveal internal laws. Therefore, the fusion of laser Doppler and pressure data can effectively solve the testing problems of coal mine explosion-proof equipment, and the performance of explosion-proof shells can be effectively evaluated by combining data processing methods. Summary of the invention

[0005] The present application provides a method for dynamic detection of coal mine explosion-proof equipment based on laser Doppler and pressure data, which can measure the surface dynamic response of the explosion-proof shell under explosion impact in real time and accurately, thereby improving the efficiency and accuracy of dynamic detection of coal mine explosion-proof equipment.

[0006] In a first aspect, the present application provides a method for dynamic detection of coal mine explosion-proof equipment based on laser Doppler and pressure data, the method comprising:

[0007] Step 1: Set the measurement area and scanning scheme according to the geometric shape of the explosion-proof housing, the working environment and the area to be measured, wherein the scanning scheme includes the number of measurement points, distribution positions and scanning paths;

[0008] Step 2: Set a pressure sensor at each measuring point, and arrange and install a laser Doppler vibrometer based on the measuring area and scanning scheme;

[0009] Step 3: Perform an explosion experiment and collect laser Doppler data and pressure data at each measuring point;

[0010] Step 4: extract features from the laser Doppler data to obtain a vibration feature vector, and extract features from the pressure data to obtain a pressure feature vector;

[0011] Step 5: Input the vibration feature vector and the pressure feature vector into the deep learning model for fusion to obtain a fused feature vector;

[0012] Step 6: Input the fused feature vector into the comprehensive evaluation model, output the performance score of the explosion-proof shell, compare the performance score with the set threshold, and determine the explosion-proof performance of the explosion-proof shell.

[0013] In combination with the first aspect, in a first implementation of the first aspect of the present application, in step 1, a method for generating a scanning scheme is:

[0014] Step 11, analyzing the geometric shape and stress characteristics of the explosion-proof housing, and designing a first measurement point distribution method and a second measurement point distribution method based on the analysis results;

[0015] Step 12: Arrange virtual measuring points in the measuring area based on the layout mode corresponding to each measuring point distribution mode;

[0016] Step 13: Identify overlapping measurement points, delete the overlapping measurement points from the virtual measurement point set corresponding to the first measurement point distribution mode, and obtain a first virtual measurement point set;

[0017] Step 14: Generate a first scanning path based on the first virtual measurement point set, generate a second scanning path based on the second virtual measurement point set, merge the first scanning path and the second scanning path to generate a third scanning path, wherein the second virtual measurement point set is a virtual measurement point set corresponding to the second measurement point distribution mode;

[0018] Step 15, conduct an explosion simulation experiment, collect vibration data at each virtual measurement point, and analyze the vibration data. According to the analysis results, adjust the number and distribution of virtual measurement points, and generate a new scanning path based on the adjusted results. Repeat step 15. After multiple experimental verifications and adjustments, determine the number of measurement points, distribution positions, and scanning paths.

[0019] In combination with the first aspect, in a second implementation of the first aspect of the present application, the first measurement point distribution mode is global scanning, and the second measurement point distribution mode is local scanning, wherein the local scanning area includes a central area, an edge area, and a structural connection area, and the area shape is circular or rectangular;

[0020] The layout method corresponding to the global scan and the central area is a uniformly spaced layout, and the layout method corresponding to the edge area and the structural connection area is a dense layout.

[0021] In combination with the first aspect, in a third implementation of the first aspect of the present application, before step 4 includes: performing time synchronization and space synchronization processing on the laser Doppler data and the pressure data.

[0022] In combination with the first aspect, in a fourth implementation of the first aspect of the present application, the laser Doppler data includes vibration displacement, vibration velocity and vibration acceleration. In step 4, feature extraction is performed on the laser Doppler data to obtain a vibration feature vector:

[0023] Analyze the laser Doppler data to obtain first characteristic data of the laser Doppler data in the time dimension, and combine all the first characteristic data to generate a time domain characteristic vector, wherein the first characteristic data includes the maximum amplitude, root mean square amplitude, vibration velocity standard deviation and vibration velocity mean of the laser Doppler data;

[0024] Performing Fourier transformation on the laser Doppler data and then analyzing it to obtain second characteristic data of the laser Doppler data in the frequency dimension, and combining all the second characteristic data to generate a frequency domain characteristic vector, wherein the second characteristic data includes a main frequency, a maximum value of a power spectrum density, and a frequency bandwidth;

[0025] The time domain eigenvector and the frequency domain eigenvector are combined to generate a vibration eigenvector.

[0026] In combination with the first aspect, in a fifth implementation of the first aspect of the present application, in step 4, feature extraction is performed on the pressure data to obtain a pressure feature vector:

[0027] The pressure data is analyzed to obtain the maximum pressure, root mean square pressure, pressure change rate and pressure pulse width, and the maximum pressure, root mean square pressure, pressure change rate and pressure pulse width are combined to generate a pressure feature vector.

[0028] In combination with the first aspect, in a sixth implementation of the first aspect of the present application, step 5 includes:

[0029] Performing deep feature extraction on the vibration feature vector and the pressure feature vector respectively through a neural network to obtain a first deep feature corresponding to the vibration feature vector and a second deep feature corresponding to the pressure feature vector;

[0030] The first deep feature and the second deep feature are concatenated or weightedly fused to generate a fused feature vector.

[0031] In combination with the first aspect, in a seventh implementation of the first aspect of the present application, in step 6, a mechanical response analysis is performed on the explosion-proof housing:

[0032] The vibration displacement of each measuring point is obtained from the laser Doppler data, and the shock wave intensity of each measuring point is obtained from the pressure data. Based on the vibration displacement and the shock wave intensity, the dynamic displacement distribution of the surface of the explosion-proof shell is calculated by a numerical analysis method;

[0033] The response characteristics of the explosion-proof housing at different frequencies are obtained by combining laser Doppler data and pressure data through frequency domain analysis method.

[0034] The mechanical model is selected based on the material properties, structural form and experimental conditions of the explosion-proof shell. The pressure data is used to drive the mechanical model to obtain the mechanical response of the mechanical model. The mechanical model is then verified and corrected using laser Doppler data to determine the changing trends of stress and strain and to construct a stress-strain model.

[0035] In combination with the first aspect, in an eighth implementation of the first aspect of the present application, the laser Doppler vibrometer is equipped with a galvanometer control system, which automatically adjusts the irradiation angle and position of the laser head and controls the movement of the laser beam according to the scanning path during the explosion experiment.

[0036] In combination with the first aspect, in a ninth implementation of the first aspect of the present application, before step 4, the laser Doppler data and the pressure data are denoised and calibrated.

[0037] Compared with the prior art, the beneficial effects of the technical solution of the present application are at least as follows:

[0038] 1. Setting multiple measurement points and scanning paths, combined with global scanning and local scanning, can fully cover all key parts of the explosion-proof shell to ensure the comprehensiveness and accuracy of the detection.

[0039] 2. It can accurately obtain dynamic parameters such as displacement, velocity and acceleration of the explosion-proof shell surface. Fusion with pressure data can provide effective experimental data for explosion-proof performance evaluation and design. By extracting features from laser Doppler data and pressure data and inputting them into the deep learning model for fusion, a more comprehensive fusion feature vector can be obtained. This data fusion method can comprehensively consider various information such as vibration and pressure, and provide more accurate explosion-proof performance evaluation.

[0040] 3. By analyzing the mechanical response of the explosion-proof shell, combining the mechanical model and experimental data, calculating the dynamic displacement distribution of its surface and the response at different frequencies, and constructing a stress-strain model, we can gain an in-depth understanding of the dynamic behavior of the explosion-proof shell under the impact of explosion, help identify the weak links of the explosion-proof shell, and make targeted improvements. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0042] Figure 1 A schematic diagram of an embodiment of a method for dynamic detection of coal mine explosion-proof equipment based on laser Doppler and pressure data in an embodiment of the present application;

[0043] Figure 2 This is a schematic diagram of another embodiment of the method for dynamic detection of coal mine explosion-proof equipment based on laser Doppler and pressure data in the embodiment of the present application;

[0044] Figure 3 This is a schematic diagram of an embodiment of global scanning in an embodiment of the present application;

[0045] Figure 4 This is a schematic diagram of an embodiment of local scanning in an embodiment of the present application. DETAILED DESCRIPTION

[0046] The embodiment of the present application provides a method for dynamic detection of coal mine explosion-proof equipment based on laser Doppler and pressure data. The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments described here can be implemented in an order other than that illustrated or described here. In addition, the terms "including" or "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0047] For ease of understanding, the specific process of the embodiment of the present application is described below. Figure 1 In the embodiment of the present application, an embodiment of a method for dynamic detection of coal mine explosion-proof equipment based on laser Doppler and pressure data includes:

[0048] Step 1: Set the measurement area and scanning scheme according to the geometric shape of the explosion-proof housing, the working environment and the area to be measured, wherein the scanning scheme includes the number of measurement points, distribution positions and scanning paths.

[0049] Specifically, before step 1, select a suitable laser Doppler vibrometer and data acquisition system to ensure that the power, frequency range, accuracy, etc. of the sensor meet the dynamic response measurement requirements of coal mine explosion-proof equipment. At the same time, in order to avoid the explosion-proof equipment from affecting the measurement data due to vibration or external force during the measurement process, it is necessary to ensure that the equipment is firmly fixed on the test platform, and use appropriate supports or clamps to firmly fix the explosion-proof equipment to avoid any possible movement or shaking.

[0050] According to the geometric shape of the explosion-proof shell, the working environment and the area to be measured, the measurement area is selected and planned. Usually, the outer surface of the explosion-proof shell is selected, especially those parts with greater force or more obvious vibration, such as the central area, edge area and structural connection parts, which may be strongly affected by external factors such as explosion shock waves and pressure waves. Generally, when an explosion occurs in the cavity, the explosion shock wave will spread around in a spherical shape with the ignition point as the center. Ideally, the ignition is centered and the force is evenly distributed on all four sides, so the side of the explosion-proof shell is selected as the measurement area.

[0051] Step 2: Set up a pressure sensor at each measurement point, and arrange and install a laser Doppler vibrometer based on the measurement area and scanning scheme.

[0052] Specifically, a pressure sensor is set at each measuring point. The arrangement of the sensor needs to ensure that it can accurately sense the pressure changes of the explosion shock wave. It is usually necessary to install it on the surface or inside the explosion-proof shell. The specific location is determined according to the distribution of the measuring points determined in step 1. Before installation, the pressure sensor is calibrated to ensure its measurement accuracy and reliability. The calibration process usually includes steps such as zero point calibration, range calibration and linearity calibration.

[0053] Specifically, after determining the measurement area and scanning scheme, fix the laser head of the laser Doppler in an appropriate position in front of the test area of ​​the explosion-proof equipment to ensure that the laser beam can accurately irradiate the surface of the measurement area, and set the optimal measurement distance and angle to ensure that the laser beam is vertically or approximately vertically irradiated to the measurement area. Before installation, perform necessary calibration and debugging on the laser Doppler instrument to ensure that the equipment works properly, and confirm that the measurement system can accurately capture surface vibration information through comparison tests with known standard signals.

[0054] Step 3: Conduct an explosion experiment and collect laser Doppler data and pressure data at each measurement point.

[0055] Specifically, before the formal explosion experiment, sufficient preparation is required, including checking whether all equipment connections are firm, whether sensors are working properly, whether the scanning path is correct, etc., and confirming whether all safety measures are in place, including safety distance, protective equipment, emergency plans, etc., to ensure the safety of experimenters and equipment. According to the experimental design, set up a suitable explosion source. The selection and setting of the explosion source needs to consider factors such as explosion equivalent, explosion location, and explosion direction to ensure that the explosion shock wave can effectively act on the explosion-proof shell.

[0056] While the explosion experiment is being carried out, the laser Doppler vibrometer obtains the vibration characteristic data of the explosion-proof shell (i.e., laser Doppler data) by measuring the vibration displacement, vibration velocity and vibration acceleration, and the pressure sensor measures the pressure changes of the explosion shock wave (i.e., pressure data) in real time.

[0057] Step 4: Perform feature extraction on the laser Doppler data to obtain a vibration feature vector, and perform feature extraction on the pressure data to obtain a pressure feature vector.

[0058] Laser Doppler measurement obtains vibration information on the surface of the explosion-proof shell. The vibration data of all measuring points are collected and integrated to form a complete vibration response data set, and feature extraction is further performed on the vibration response data set to obtain the vibration feature vector. The pressure sensor provides local pressure change data on the surface of the explosion-proof shell. The pressure data of all measuring points are collected and integrated to form a complete pressure change data set, and feature extraction is further performed on the pressure change data set to obtain the pressure feature vector, providing high-quality basic data for subsequent deep learning and comprehensive evaluation.

[0059] Step 5: Input the vibration feature vector and the pressure feature vector into the deep learning model for fusion to obtain a fused feature vector.

[0060] Specifically, the vibration feature vector and the pressure feature vector can be fused by splicing fusion or weighted fusion. Splicing fusion is to simply splice the vibration feature vector and the pressure feature vector to generate a new feature vector. This method is simple and direct, but it may ignore the correlation between different features; weighted fusion is to perform weighted fusion of the vibration feature vector and the pressure feature vector according to the contribution of different features to generate a new feature vector. This method can better reflect the relative importance of different features.

[0061] Step 6: Input the fused feature vector into the comprehensive evaluation model, output the performance score of the explosion-proof shell, compare the performance score with the set threshold, and determine the explosion-proof performance of the explosion-proof shell.

[0062] Specifically, the fusion features (including time domain, frequency domain features, and pressure features) are input into a deep learning model (such as a multi-layer perceptron), and the performance of the explosion-proof housing is determined based on the output performance evaluation results.

[0063] Based on the physical model and machine learning model, a comprehensive evaluation model (such as a neural network model) is constructed, and the fused feature vector is input into the comprehensive evaluation model to output a performance score S perf , ,in, is the evaluation function corresponding to the comprehensive evaluation model, F deep is the fused feature vector.

[0064] Set a threshold , judge whether the explosion-proof housing meets the design requirements based on the scoring results. For example, if , the explosion-proof performance of the explosion-proof casing is considered qualified, otherwise it is unqualified.

[0065] Figure 2 This is a schematic diagram of another embodiment of the method for dynamic detection of coal mine explosion-proof equipment based on laser Doppler and pressure data in the embodiment of the present application.

[0066] In a specific embodiment, in step 1, the method for generating the scanning scheme is:

[0067] Step 11: Analyze the geometric shape and stress characteristics of the explosion-proof housing, and design a first measurement point distribution method and a second measurement point distribution method based on the analysis results.

[0068] Step 12: Arrange virtual measurement points in the measurement area based on the layout mode corresponding to each measurement point distribution mode.

[0069] Step 13: Identify overlapping measurement points, delete the overlapping measurement points from the virtual measurement point set corresponding to the first measurement point distribution mode, and obtain a first virtual measurement point set.

[0070] Step 14: Generate a first scanning path based on the first virtual measurement point set, generate a second scanning path based on the second virtual measurement point set, merge the first scanning path and the second scanning path to generate a third scanning path, wherein the second virtual measurement point set is a virtual measurement point set corresponding to the second measurement point distribution method.

[0071] Step 15, conduct an explosion simulation experiment, collect vibration data at each virtual measurement point, and analyze the vibration data. According to the analysis results, adjust the number and distribution of virtual measurement points, and generate a new scanning path based on the adjusted results. Repeat step 15. After multiple experimental verifications and adjustments, determine the number of measurement points, distribution positions, and scanning paths.

[0072] In a specific embodiment, the first measurement point distribution mode is global scanning, and the second measurement point distribution mode is local scanning, wherein the local scanning area includes a central area, an edge area, and a structural connection area, and the area shape is circular or rectangular;

[0073] The layout method corresponding to the global scan and the central area is a uniformly spaced layout, and the layout method corresponding to the edge area and the structural connection area is a dense layout.

[0074] Through multiple experiments, the number and distribution of virtual measurement points and the scanning path are determined to ensure the accuracy and efficiency of the scan. The distribution method can be divided into global scanning and local scanning. The global scan is the entire measurement area, so the measurement area spread on the surface of the explosion-proof shell is selected, and several virtual measurement points are planned. Then, the optimal number of measurement points is selected according to different measurement point schemes. The scheme adopts uniform spacing and dense arrangement based on vibration-sensitive areas. Figure 3 As shown, the global scan evenly distributes the measurement points in the measurement area according to a certain grid spacing. The local scanning areas are the central area and the edge area, and the area shapes are circular and rectangular. The central area is generally the main part of the explosion-proof shell, which is evenly stressed and has obvious vibration transmission. It is evenly spaced and laid out in circular and rectangular grids. Since this area is relatively symmetrical, the uniform layout helps to obtain overall vibration information. The vibration intensity in the outer edge area of ​​the explosion-proof shell in the edge area may be greater, especially when the edge effect is more significant under the action of external forces. The measurement point arrangement in this area can adopt a dense layout, that is, increase the density of measurement points in the vibration-sensitive area, and set multiple measurement points at equal intervals along the ring, especially more measurement points at the structural connection parts. As shown in Figure 4 As shown, the local scan is arranged in a rectangular grid with evenly distributed measurement points.

[0075] According to the experimental results, the number and distribution of virtual measurement points are optimized. For example, if the vibration information in a certain area is not obvious, the number of measurement points in that area can be appropriately reduced; if the vibration intensity in a certain area is large or the changes are complex, the density of measurement points in that area can be increased. Combine global scanning and local scanning to determine the optimal number and distribution of measurement points to ensure that the measurement area is fully covered and the vibration information of key parts is captured, ensuring the comprehensiveness and accuracy of the measurement data, and providing reliable data support for subsequent explosion-proof performance evaluation.

[0076] In a specific embodiment, before step 4, the process includes: performing time synchronization and space synchronization processing on the laser Doppler data and the pressure data.

[0077] Since the sampling frequency and sampling time of the laser Doppler vibrometer and the pressure sensor may be different, these data are time synchronized to ensure that the data collected by different sensors are completely aligned in time. The data can be time aligned by interpolation methods, for example, using linear interpolation, spline interpolation, etc., to interpolate the pressure data to the sampling time point of the laser Doppler vibrometer to ensure that the two types of data correspond at the same time step.

[0078] The vibration data provided by the laser Doppler vibrometer is usually the scanned area data, while the pressure data is collected by sensors at different positions. Therefore, it is necessary to use the pressure data and vibration data at the same position to ensure that the spatial distribution of the pressure data and the vibration data is consistent. This can be achieved by accurately measuring the relative position between the sensors and performing coordinate transformation.

[0079] In a specific embodiment, the laser Doppler data includes vibration displacement, vibration velocity and vibration acceleration. In step 4, feature extraction is performed on the laser Doppler data to obtain a vibration feature vector:

[0080] Analyze the laser Doppler data to obtain first characteristic data of the laser Doppler data in the time dimension, and combine all the first characteristic data to generate a time domain characteristic vector, wherein the first characteristic data includes the maximum amplitude, root mean square amplitude, vibration velocity standard deviation and vibration velocity mean of the laser Doppler data;

[0081] Performing Fourier transformation on the laser Doppler data and then analyzing it to obtain second characteristic data of the laser Doppler data in the frequency dimension, and combining all the second characteristic data to generate a frequency domain characteristic vector, wherein the second characteristic data includes a main frequency, a maximum value of a power spectrum density, and a frequency bandwidth;

[0082] The time domain eigenvector and the frequency domain eigenvector are combined to generate a vibration eigenvector.

[0083] Specifically, vibration features are extracted from the time domain and frequency domain. The time domain features are:

[0084] (1) Maximum amplitude : reflects the maximum amplitude of vibration, indicating the maximum deformation of the structure at a certain moment, ,in, is the vibration speed.

[0085] (2) Root mean square amplitude (RMS) : used to describe the energy or amplitude of vibration, ,in, is the time length of the vibration signal, is the speed signal.

[0086] (3) Standard deviation : Indicates the volatility of the vibration signal, ,in, is the mean value of the vibration signal.

[0087] The time domain eigenvector is: ,in, is the maximum amplitude of vibration, is the root mean square amplitude of the vibration, is the standard deviation of the vibration signal, is the mean value of the vibration signal.

[0088] The frequency domain characteristics are:

[0089] (1) Main frequency : Identify the main frequency component of the vibration signal, which usually corresponds to the modal frequency of the explosion-proof housing. ,in, is the Fourier transform of the vibration signal.

[0090] (2) Power spectral density (PSD): ,in, is the Fourier transform of the vibration signal.

[0091] (3) Modal frequency: By analyzing the frequency spectrum, the natural modal frequency of the explosion-proof shell can be extracted, reflecting the natural response of the shell under excitation.

[0092] The frequency domain eigenvector is: ,in, is the main frequency of the signal, is the maximum value of the power spectral density, is the frequency bandwidth (which can be obtained by spectrum analysis).

[0093] After combining the features of the time domain and frequency domain, we get a feature vector , which represents the response characteristics of the explosion-proof housing in terms of vibration.

[0094] In a specific embodiment, in step 4, feature extraction is performed on the pressure data to obtain a pressure feature vector:

[0095] The pressure data is analyzed to obtain the maximum pressure, root mean square pressure, pressure change rate and pressure pulse width, and the maximum pressure, root mean square pressure, pressure change rate and pressure pulse width are combined to generate a pressure feature vector.

[0096] Specifically, pressure characteristics include:

[0097] (1) Maximum pressure : reflects the limit value of local pressure, which is directly related to the maximum degree of stress on the shell. ,in, It is a pressure signal.

[0098] (2) Root mean square pressure (RMS) : Indicates the average intensity of pressure, .

[0099] (3) Pressure change rate : reflects the speed of pressure change, usually used to evaluate the degree of pressure fluctuation under excitation. .

[0100] (4) Pressure pulse width W P :By calculating the pulse width of the pressure wave, the duration of the pressure fluctuation is evaluated, and a threshold is set (e.g. 10% of the maximum value of the signal), calculate the duration of the pressure signal exceeding this threshold, ;

[0101] in:

[0102] ;

[0103] ;

[0104] ;

[0105] is the maximum value of the pressure signal;

[0106] The pressure eigenvector is: ,in, is the maximum value of pressure, is the root mean square value of pressure, is the rate of change of the pressure signal, is the pulse width of the pressure signal.

[0107] In a specific embodiment, step 5 includes:

[0108] Performing deep feature extraction on the vibration feature vector and the pressure feature vector respectively through a neural network to obtain a first deep feature corresponding to the vibration feature vector and a second deep feature corresponding to the pressure feature vector;

[0109] The first deep feature and the second deep feature are concatenated or weightedly fused to generate a fused feature vector.

[0110] The deep features are extracted through the neural network, and the features of the laser Doppler data and pressure data are input into the deep learning model for fusion. Specifically, two branch networks are designed to input the laser Doppler feature vector and the pressure eigenvector , each branch network extracts deep features through a multi-layer fully connected network:

[0111]

[0112]

[0113] The two sets of deep features are concatenated or weighted to obtain the fused feature vector F deep :

[0114] ,in, is the first deep feature corresponding to the laser Doppler data, It is the second deep feature corresponding to the pressure data.

[0115] In a specific embodiment, in step 6, a mechanical response analysis is performed on the explosion-proof housing:

[0116] The vibration displacement of each measuring point is obtained from the laser Doppler data, and the shock wave intensity of each measuring point is obtained from the pressure data. Based on the vibration displacement and the shock wave intensity, the dynamic displacement distribution of the surface of the explosion-proof shell is calculated by a numerical analysis method;

[0117] The response characteristics of the explosion-proof housing at different frequencies are obtained by combining laser Doppler data and pressure data through frequency domain analysis method.

[0118] The mechanical model is selected based on the material properties, structural form and experimental conditions of the explosion-proof shell. The pressure data is used to drive the mechanical model to obtain the mechanical response of the mechanical model. The mechanical model is then verified and corrected using laser Doppler data to determine the changing trends of stress and strain and to construct a stress-strain model.

[0119] Specifically, the dynamic displacement distribution of the explosion-proof shell surface is calculated by numerical analysis based on the vibration displacement obtained in the laser Doppler data and the shock wave intensity in the pressure data. These displacement data can reveal the deformation of the explosion-proof shell surface under the action of the explosion wave, especially the local maximum displacement and stress concentration area.

[0120] By using frequency domain analysis, combined with vibration data and pressure data, the response of the shell at different frequencies can be analyzed, especially in the specific frequency band of the explosion wave, the displacement of the shell may vibrate with a large amplitude. By fusing vibration data and pressure data, the areas with the largest displacement on the surface of the explosion-proof shell can be identified. These areas are usually key areas that are strongly impacted by the explosion wave and may be potential areas for cracks, damage or fatigue.

[0121] Fusion data can help build a more accurate stress-strain model. The distribution of stress and strain fields can be calculated on the surface and inside of the explosion-proof shell. The explosion wave information in the pressure data is used to drive the mechanical model of the shell, and the vibration data is used to determine the changing trend of stress and strain. Combining the analysis of vibration data and pressure data, stress concentration areas can be accurately identified. Especially under strong explosion impact, local areas of the explosion-proof shell may have large stress concentrations, and these areas are the parts of the shell that are most prone to structural damage or fatigue.

[0122] Through a detailed mechanical response analysis process, laser Doppler data and pressure data are organically combined to deeply analyze the dynamic behavior of the explosion-proof shell under explosion impact, and a corresponding mechanical model is constructed. This analysis method can not only provide detailed mechanical performance parameters of the explosion-proof shell, but also provide a scientific basis for the design and optimization of explosion-proof equipment, thereby effectively improving the safety and reliability of coal mine explosion-proof equipment.

[0123] In a specific embodiment, the laser Doppler vibrometer is equipped with a galvanometer control system, which automatically adjusts the irradiation angle and position of the laser head and controls the movement of the laser beam according to the scanning path during the explosion experiment.

[0124] Specifically, the laser head automatically adjusts the irradiation angle and position of the laser head through the galvanometer control system. The galvanometer controls the movement of the laser beam according to the scanning path, responds quickly and accurately projects the laser beam to the position of the predetermined measurement point, and realizes accurate non-contact measurement of each virtual measurement point and real-time collection of vibration data. This method only requires a single pressure test on the explosion-proof shell to obtain the vibration data of all points on a single side. The vibration data of all measurement points are collected and integrated into the data acquisition system to form a complete vibration response data set.

[0125] In a specific embodiment, before step 4, the laser Doppler data and the pressure data are denoised and calibrated.

[0126] Specifically, due to factors such as vibration and electromagnetic interference in the experimental environment, the laser Doppler data may contain noise, and filtering techniques (such as low-pass filtering, band-pass filtering, wavelet denoising, etc.) need to be used to remove the noise and retain useful vibration signals. Pressure data may also be affected by environmental noise and need to be denoised accordingly. Similar filtering techniques can be used to remove noise and retain the true pressure change signal.

[0127] Calibrate the laser Doppler data to ensure the accuracy of the measured vibration displacement, velocity and acceleration. The calibration process may include zero point calibration, sensitivity calibration and linearity calibration, etc. Calibrate the pressure data to ensure the accuracy of the measured pressure value. The calibration process may include zero point calibration, range calibration and linearity calibration, etc.

[0128] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described systems, systems and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0129] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk and other media that can store program codes.

[0130] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A dynamic detection method for coal mine explosion-proof equipment based on laser Doppler and pressure data, characterized in that: The method comprises: Step 1: setting a measurement area and a scanning scheme according to the geometric shape of the explosion-proof housing, the working environment and the area to be measured, wherein the scanning scheme includes the number of measurement points, distribution positions and scanning paths; Step 2: Setting a pressure sensor at each measuring point, and arranging and installing a laser Doppler vibrometer based on the measuring area and the scanning scheme; Step 3, performing an explosion experiment, and simultaneously collecting laser Doppler data and pressure data at each of the measuring points; Step 4: extracting features from the laser Doppler data to obtain a vibration feature vector, and extracting features from the pressure data to obtain a pressure feature vector; Step 5: Input the vibration feature vector and the pressure feature vector into a deep learning model for fusion to obtain a fused feature vector; Step 6: Input the fused feature vector into a comprehensive evaluation model, output a performance score of the explosion-proof housing, compare the performance score with a set threshold, and determine the explosion-proof performance of the explosion-proof housing.

2. The method for dynamic detection of coal mine explosion-proof equipment based on laser Doppler and pressure data according to claim 1 is characterized in that: In step 1, the method for generating the scanning scheme is: Step 11, analyzing the geometric shape and stress characteristics of the explosion-proof housing, and designing a first measurement point distribution method and a second measurement point distribution method based on the analysis results; Step 12: arranging virtual measuring points in the measuring area based on the layout mode corresponding to each measuring point distribution mode; Step 13: Identify overlapping measurement points, delete the overlapping measurement points from the virtual measurement point set corresponding to the first measurement point distribution mode, and obtain a first virtual measurement point set; Step 14: Generate a first scanning path based on the first virtual measurement point set, generate a second scanning path based on the second virtual measurement point set, and merge the first scanning path and the second scanning path to generate a third scanning path, wherein the second virtual measurement point set is a virtual measurement point set corresponding to the second measurement point distribution mode; Step 15, conduct an explosion simulation experiment, collect vibration data at each virtual measurement point, and analyze the vibration data, adjust the number and distribution of the virtual measurement points according to the analysis results, and generate a new scanning path based on the adjusted results, repeat step 15, and after multiple experimental verifications and adjustments, determine the number of measurement points, the distribution positions and the scanning path.

3. The method for dynamic detection of coal mine explosion-proof equipment based on laser Doppler and pressure data according to claim 2 is characterized in that: The first measurement point distribution mode is global scanning, and the second measurement point distribution mode is local scanning, wherein the local scanning area includes a central area, an edge area and a structural connection area, and the area shape is circular or rectangular; The layout mode corresponding to the global scan and the central area is a uniformly spaced layout, and the layout mode corresponding to the edge area and the structural connection area is a dense layout.

4. The method for dynamic detection of coal mine explosion-proof equipment based on laser Doppler and pressure data according to claim 1 is characterized in that: The method before step 4 includes: performing time synchronization and space synchronization processing on the laser Doppler data and the pressure data.

5. The method for dynamic detection of coal mine explosion-proof equipment based on laser Doppler and pressure data according to claim 1, characterized in that: The laser Doppler data includes vibration displacement, vibration velocity and vibration acceleration. In step 4, the vibration feature vector obtained by feature extraction of the laser Doppler data is: Analyze the laser Doppler data to obtain first feature data of the laser Doppler data in the time dimension, and combine all the first feature data to generate a time domain feature vector, wherein the first feature data includes the maximum amplitude, root mean square amplitude, vibration velocity standard deviation and vibration velocity mean of the laser Doppler data; Performing Fourier transformation on the laser Doppler data and then analyzing it to obtain second characteristic data of the laser Doppler data in the frequency dimension, and combining all the second characteristic data to generate a frequency domain characteristic vector, wherein the second characteristic data includes a main frequency, a maximum value of a power spectrum density, and a frequency bandwidth; The time domain feature vector and the frequency domain feature vector are combined to generate the vibration feature vector.

6. The method for dynamic detection of coal mine explosion-proof equipment based on laser Doppler and pressure data according to claim 1 is characterized in that: In step 4, the pressure feature vector is obtained by extracting features from the pressure data: The pressure data is analyzed to obtain the maximum pressure, root mean square pressure, pressure change rate and pressure pulse width, and the maximum pressure, the root mean square pressure, the pressure change rate and the pressure pulse width are combined to generate the pressure feature vector.

7. The method for dynamic detection of coal mine explosion-proof equipment based on laser Doppler and pressure data according to claim 1, characterized in that: The step 5 comprises: Performing deep feature extraction on the vibration feature vector and the pressure feature vector respectively through a neural network to obtain a first deep feature corresponding to the vibration feature vector and a second deep feature corresponding to the pressure feature vector; The first deep feature and the second deep feature are concatenated or weightedly fused to generate the fused feature vector.

8. The method for dynamic detection of coal mine explosion-proof equipment based on laser Doppler and pressure data according to claim 1, characterized in that: In step 6, a mechanical response analysis is performed on the explosion-proof housing: Acquire the vibration displacement of each of the measuring points from the laser Doppler data, and acquire the shock wave intensity of each of the measuring points from the pressure data, and calculate the dynamic displacement distribution of the surface of the explosion-proof housing by a numerical analysis method based on the vibration displacement and the shock wave intensity; By using a frequency domain analysis method, combining the laser Doppler data and the pressure data, the response characteristics of the explosion-proof housing at different frequencies are obtained; A mechanical model is selected based on the material properties, structural form and experimental conditions of the explosion-proof shell, the pressure data is used to drive the mechanical model, the mechanical response of the mechanical model is obtained, and then the laser Doppler data is used to verify and correct the mechanical model, determine the changing trend of stress and strain, and construct a stress-strain model.

9. The method for dynamic detection of coal mine explosion-proof equipment based on laser Doppler and pressure data according to claim 1, characterized in that: The laser Doppler vibrometer is equipped with a galvanometer control system, which automatically adjusts the irradiation angle and position of the laser head and controls the laser beam to move according to the scanning path during the explosion experiment.

10. The method for dynamic detection of coal mine explosion-proof equipment based on laser Doppler and pressure data according to claim 1, characterized in that: Prior to step 4, the laser Doppler data and the pressure data are denoised and calibrated.

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