A monitoring system and method for an automotive fastener vibration testing apparatus

Abstract

The application discloses a kind of automobile fastener vibration test equipment monitoring system and method, it is related to equipment monitoring technical field, including, using wavelength offset data stream to calculate the strain value of automobile fastener monitoring point, strain thermal map is drawn and local strain gradient is analyzed, set strain gradient threshold value, when local strain gradient exceeds strain gradient threshold value, sound-light alarm signal is sent and is transmitted to vibration table, vibration table frequency reduction protection mechanism is activated, and the operating state of vibration table is obtained;Collect vibration signal and generate vibration spectrum, construct equivalent impedance network model and analyze vibration spectrum, and identify parasitic resonance frequency point.The application effectively avoids the risk of equipment damage caused by inaccurate resonance frequency suppression, improves the accuracy of parasitic resonance frequency point identification, solves the problem of inaccurate resonance frequency suppression, and reduces unnecessary energy consumption and mechanical damage.

Description

Technical Field

[0001] This invention relates to the field of equipment monitoring technology, and in particular to a monitoring system and method for automotive fastener vibration testing equipment. Background Technology

[0002] With the automotive industry's increasing use of lightweight and high-strength materials and its demand for operation under complex conditions, vibration testing technology is playing an increasingly important role in fastener performance evaluation. Conventional vibration testing mainly relies on accelerometers and strain gauges to analyze the dynamic response of fasteners by measuring vibration signals and surface strain.

[0003] Existing monitoring methods for vibration testing equipment in automotive fasteners have many shortcomings. While resonance identification from vibration spectrum diagrams can pinpoint parasitic resonant frequencies, it lacks detailed analysis of local strain distribution on the thread surface, making it difficult to detect early signs of microcracks or fatigue damage. Although the application of photonic crystal thin films can reflect strain changes through wavelength shift, current methods mostly remain at the static testing stage, failing to effectively integrate dynamic vibration data with real-time strain monitoring. This makes it impossible to visualize the strain gradient across the entire fastener surface and to provide real-time early warning and dynamic protection against anomalies. Summary of the Invention

[0004] In view of the aforementioned existing problems, the present invention is proposed.

[0005] Therefore, the present invention provides a monitoring method for automotive fastener vibration testing equipment, which solves the problems of inaccurate resonance frequency suppression and lack of dynamic protection mechanism in existing automotive fastener vibration testing equipment monitoring methods.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0007] In a first aspect, the present invention provides a monitoring method for an automotive fastener vibration testing device, comprising: spraying a photonic crystal thin film onto an automotive fastener, collecting light wavelength distribution data on the surface of the photonic crystal thin film, and generating a wavelength offset data stream;

[0008] The strain value of the monitoring point of the car fastener is calculated by using the wavelength offset data stream, the strain thermogram is drawn and the local strain gradient is calculated, the strain gradient threshold is set, and when the local strain gradient exceeds the strain gradient threshold, an audible and visual alarm signal is issued and transmitted to the vibration table to activate the vibration table frequency reduction protection mechanism and obtain the operating status of the vibration table.

[0009] Vibration signals are collected and vibration spectrum diagrams are generated. An equivalent impedance network model is constructed and the vibration spectrum diagrams are analyzed to identify parasitic resonance frequency points.

[0010] Hydraulic dampers are used to suppress frequency band energy reflection at parasitic resonant frequency points to obtain the vibration spectrum state. A reverse waveform is generated by a piezoelectric actuator, and the reverse waveform is used to cancel the residual standing waves in the vibration spectrum state to output a stable vibration state report.

[0011] As a preferred embodiment of the monitoring method of the automotive fastener vibration testing equipment of the present invention, the generation of wavelength offset data stream refers to using a laser interferometer to collect light wavelength distribution data on the surface of a photonic crystal thin film, and numbering the light wavelength distribution data on the surface of the photonic crystal thin film in spatial order to form monitoring points;

[0012] The LabVIEW Spectrum Processing Suite was used to extract the wavelength peak values ​​of each monitoring point and organize them to form a reflectance spectrum database.

[0013] The reflectance spectral data of photonic crystal thin films were collected using an HR4000 spectrometer to form a reflectance spectral dataset. The reflectance spectral dataset was compared with the reflectance spectral database to calculate the wavelength offset and organize it into a wavelength offset data stream.

[0014] As a preferred embodiment of the monitoring method of the automotive fastener vibration testing equipment of the present invention, the calculation of the strain value of the automotive fastener monitoring point includes establishing a calibration curve, finding the wavelength offset closest to the wavelength offset data stream in the calibration curve, and converting the closest wavelength offset into the strain value of the automotive fastener monitoring point by linear interpolation.

[0015] As a preferred embodiment of the monitoring method for the automotive fastener vibration testing equipment described in this invention, the method of obtaining the operating status of the vibration table refers to organizing the strain values ​​of the automotive fastener monitoring points, forming a strain value dataset, and using a three-dimensional optical scanner to create a three-dimensional geometric coordinate file of the automotive fastener.

[0016] The 3D coordinates of the 3D geometric coordinate file are extracted using MATLAB software. The strain values ​​in the strain value dataset are then matched with the 3D coordinates to form a comprehensive dataset.

[0017] The Python interpreter is used to simplify the three-dimensional coordinates in the comprehensive dataset into two-dimensional planar coordinates, construct a two-dimensional grid, find the monitoring point that is closest to the comprehensive dataset for each grid point and assign strain values ​​to it, and generate a stress-thermal map.

[0018] Based on the stress-thermal diagram, the local strain gradient values ​​between adjacent monitoring points are calculated and compiled into a local strain gradient value table. A strain gradient threshold is set, and each local strain gradient value in the local strain gradient table is compared with the strain gradient threshold. An anomaly log file is then output.

[0019] Based on the anomaly log file, the early warning mechanism is activated to issue an audible and visual alarm signal. The trigger command in the anomaly log file is sent to the vibration table, and the operating status of the vibration table is obtained after observing the changes in its operating status.

[0020] As a preferred embodiment of the monitoring method for the automotive fastener vibration testing equipment of the present invention, the construction of the equivalent impedance network model refers to organizing the operating status of the vibration table into a vibration table operating status log file, wherein the vibration table operating status file includes vibration frequency and amplitude.

[0021] Use LabVIEW software to create a data input control, record the vibration frequency and amplitude as excitation units, calculate the mass of the automotive fastener, the mass of the fixture, and the stiffness value of the automotive fastener, and label them as automotive fastener mass unit, fixture mass unit, and automotive fastener stiffness unit, respectively.

[0022] Create connection controls and arrange all elements according to the order of vibration energy transmission to form an equivalent impedance network model.

[0023] As a preferred embodiment of the monitoring method of the automotive fastener vibration testing equipment of the present invention, the identification of parasitic resonance frequency points refers to using an accelerometer to collect vibration signals of automotive fasteners and fixtures and generate vibration signal files.

[0024] The vibration signal file is processed by Fourier transform to calculate the amplitude values ​​of all vibration frequencies and form a vibration spectrum.

[0025] Examine the amplitude peak in the vibration spectrum and predict the theoretical amplitude peak using the equivalent impedance network model. Set an amplitude difference threshold, calculate the difference between the amplitude peak and the theoretical amplitude peak, and compare it with the amplitude difference threshold to identify the parasitic resonance frequency point.

[0026] As a preferred embodiment of the monitoring method of the automotive fastener vibration testing equipment of the present invention, the output stable vibration state report table refers to selecting a hydraulic damper as a suppressor, calculating the viscosity coefficient of the hydraulic damper and performing suppression verification to obtain the damping adjustment result.

[0027] Based on the damping adjustment results, a signal at the non-parasitic resonance frequency point is applied to the automotive fastener, and the vibration frequency and amplitude of the automotive fastener are recorded to form a vibration spectrum state.

[0028] The residual standing wave is identified by the vibration spectrum state, and the phase of the residual standing wave is calculated. At the same time, the voltage of the reverse waveform is calculated by using a piezoelectric actuator. The voltage of the reverse waveform, the vibration spectrum state, and the phase of the residual standing wave are integrated to form the reverse waveform parameters.

[0029] A reverse waveform is generated using the reverse waveform parameters, and the residual standing wave is canceled to obtain a stable vibration state report.

[0030] In a second aspect, the present invention provides a monitoring system for an automotive fastener vibration testing device, comprising a data generation module for spraying a photonic crystal thin film onto an automotive fastener, collecting light wavelength distribution data on the surface of the photonic crystal thin film, and generating a wavelength offset data stream.

[0031] The operation module uses wavelength offset data stream to calculate the strain value of the monitoring point of the automotive fastener, draws the strain thermogram and analyzes the local strain gradient, sets the strain gradient threshold, and when the local strain gradient exceeds the strain gradient threshold, it issues an audible and visual alarm signal and transmits it to the vibration table, activates the vibration table frequency reduction protection mechanism, and obtains the operating status of the vibration table.

[0032] The frequency identification module collects vibration signals and generates vibration spectrum diagrams, constructs an equivalent impedance network model, analyzes the vibration spectrum diagrams, and identifies parasitic resonance frequency points.

[0033] The state generation module uses a hydraulic damper to suppress frequency band energy reflection at parasitic resonance frequency points to obtain the vibration spectrum state. It then uses a piezoelectric actuator to generate a reverse waveform, which is used to cancel the residual standing waves in the vibration spectrum state and output a stable vibration state report.

[0034] Thirdly, the present invention provides a computer device including a memory and a processor, wherein the memory stores a computer program, wherein when the computer program is executed by the processor, it implements any step of the monitoring method of the automotive fastener vibration testing device as described in the first aspect of the present invention.

[0035] Fourthly, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, it implements any step of the monitoring method of the automotive fastener vibration testing equipment as described in the first aspect of the present invention.

[0036] The beneficial effects of this invention are as follows: It can immediately trigger early warning and protection measures upon detecting abnormal strain gradients, effectively avoiding the risk of equipment damage caused by inaccurate resonant frequency suppression, thereby improving the safety and efficiency of testing. Simultaneously, the constructed equivalent impedance network model improves the accuracy of parasitic resonant frequency point identification, solves the problem of inaccurate resonant frequency suppression, and reduces unnecessary energy consumption and mechanical damage. Furthermore, by calculating the strain values ​​at the monitoring points of automotive fasteners, it efficiently quantifies the local strain of automotive fasteners, greatly improving the anomaly detection accuracy of the dynamic protection mechanism and compensating for the lack of refined strain analysis in existing automotive fastener vibration testing equipment monitoring methods. Attached Figure Description

[0037] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. 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.

[0038] Figure 1 Generate a flowchart for the wavelength offset data stream.

[0039] Figure 2 This is a schematic diagram of strain thermogram generation and gradient analysis.

[0040] Figure 3 The equivalent impedance network model is constructed and a parasitic resonance identification diagram is generated.

[0041] Figure 4 This is a flowchart of hydraulic damping suppression and piezoelectric waveform cancellation. Detailed Implementation

[0042] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0043] 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.

[0044] 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.

[0045] Reference Figures 1-4 This embodiment provides a monitoring method for automotive fastener vibration testing equipment, including the following steps:

[0046] S1. Spray a photonic crystal thin film onto the automotive fastener, collect the light wavelength distribution data on the surface of the photonic crystal thin film, and generate a wavelength offset data stream.

[0047] Includes the following steps,

[0048] S1.1. Use high-precision spraying equipment to spray a cadmium selenide quantum dot-polymer composite film (i.e., photonic crystal film) onto the threaded surface of automotive fasteners (such as bolts or nuts). When preparing the spraying solution, mix cadmium selenide quantum dots and polymethyl methacrylate at a mass ratio (e.g., 1:10), add an appropriate amount of toluene solvent and stir evenly until a stable suspension is formed. Use a pneumatic spray gun for spraying.

[0049] The car fasteners are fixed to the rotating fixture, and the spray gun moves axially along the threaded surface to ensure uniform coverage of the photonic crystal film.

[0050] S1.2. Install the automotive fastener with the attached photonic crystal thin film into the test fixture (the test fixture is made of aluminum alloy and equipped with a precision thread clamping device). After installation, use a laser interferometer to scan the surface of the photonic crystal thin film (using a helium-neon laser as the light source). During operation, adjust the optical path of the laser interferometer so that the laser beam is perpendicular to the surface of the photonic crystal thin film. Using the axis of the threaded surface as a reference, scan point by point from one end, moving once every 1 mm, until the entire threaded area is covered.

[0051] During the scanning process, a laser interferometer records the reflection spectrum of the photonic crystal thin film in a strain-free state, with a wavelength range of 400–800 nm. After each scan, the reflection spectrum is automatically saved; for example, the reflection spectrum collected at the top of the thread might be 650 nm, which is recorded as the initial state and used to generate optical wavelength distribution data on the surface of the photonic crystal thin film.

[0052] S1.3. Transmit the light wavelength distribution data from the photonic crystal thin film surface to the computer terminal, and use the LabVIEW Spectrum Processing Suite to process the light wavelength distribution data. Specifically, the LabVIEW Spectrum Processing Suite displays multiple waveforms, each corresponding to the wavelength peak at a specific point on the threaded surface. Manually set the data in the LabVIEW Spectrum Processing Suite, using the axial starting point of the threaded surface as a reference, number the reflected spectra according to their spatial position to form monitoring points. For example, starting from the starting point, name them P1, P2, P3, etc., every 1 mm, until the entire threaded area is covered.

[0053] For each monitoring point, the LabVIEW Spectrum Processing Suite automatically identifies and extracts the wavelength peak of the reflectance spectrum. For example, the wavelength peak at point P1 is 650 nm, at point P2 it is 648 nm, and so on. After extraction, click the Data Table Generation function in LabVIEW to organize all monitoring points and their corresponding wavelength peaks into a structured table (CSV format). For example, the table contains two columns: the first column is the location number (P1, P2, etc.), and the second column is the wavelength peak (650 nm, 648 nm, etc.), forming a reflectance spectrum database.

[0054] S1.4. Connect the automotive fastener with the attached photonic crystal thin film to the test fixture of the vibration testing equipment (select an electromagnetic vibration table with a working frequency range of 5~2000 Hz), start the vibration testing equipment to simulate the dynamic load during vehicle operation (set the vibration mode to sinusoidal sweep frequency, gradually increasing the frequency from 20 Hz to 500 Hz), and select an HR4000 spectrometer and fix the optical probe of the spectrometer on a three-axis fine-tuning frame. Aim the probe lens at the photonic crystal thin film on the threaded surface of the automotive fastener, keeping the distance at about 10 mm, and adjust the angle to perpendicular incidence to reduce reflection error.

[0055] The scanning area of ​​the optical probe covers the entire threaded surface. Specifically, the probe is moved point by point along the thread axis from the start to the end by manually adjusting the triaxial fine-tuning frame, pausing every 1 millimeter. The acquisition frequency is set to 500 times per second, and the HR4000 spectrometer continuously records the reflection spectrum data of the photonic crystal thin film (for example, the wavelength peak at a certain point on the threaded surface is 650 nm in the initial state, which may become 650.2 nm after vibration), generating a complete reflection spectrum dataset.

[0056] S1.5. Perform wavelength shift detection on the reflectance spectrum dataset. Specifically, the reflectance spectrum dataset is transferred to the computer processing terminal via USB interface. The LabVIEW Spectrum Processing Suite is used to automatically compare the reflectance spectrum data of the photonic crystal thin film acquired in real time by the spectrometer with the corresponding value in the initial reflectance spectrum database. For example, the initial value of point P1 is 650 nm, and the real-time value is 650.15 nm. The difference is calculated as the wavelength shift of 0.15 nm.

[0057] If the wavelength offset is less than 0.1 nm, it is recorded as no significant change; if the wavelength offset exceeds 0.1 nm, it is marked as an effective offset caused by strain. The LabVIEW Spectrum Processing Suite organizes the wavelength offsets of all monitoring points into a real-time wavelength offset data stream in chronological order, for example, generating 500 sets of data streams per second. Each set contains a location number (such as P1, P2) and the corresponding wavelength offset (such as 0.15 nm, 0.12 nm).

[0058] S2. Calculate the strain value of the monitoring point of the automotive fastener using the wavelength offset data stream, draw the strain thermogram and calculate the local strain gradient, set the strain gradient threshold, and when the local strain gradient exceeds the strain gradient threshold, issue an audible and visual alarm signal and transmit it to the vibration table to activate the vibration table frequency reduction protection mechanism and obtain the operating status of the vibration table.

[0059] Includes the following steps,

[0060] S2.1 Establish calibration curves (used to convert wavelength offset in the wavelength offset data stream into strain values). The specific establishment process is as follows: Prepare a set of automotive fastener samples of the same material and structure, spray a photonic crystal thin film onto the threaded surface of the automotive fastener samples, and fix it on the strain testing device. Use a high-precision strain gauge (such as a resistance strain gauge) to measure the actual strain value of the threaded surface, and simultaneously use a laser interferometer to record the wavelength peak value of the reflection spectrum of the photonic crystal thin film.

[0061] Gradually apply dynamic loads, for example, increasing from 0 Newtons to 100 Newtons in increments of 10 Newtons, and record the strain value and corresponding wavelength offset for each dynamic load. For example, when the dynamic load is 20 Newtons, the strain gauge measures a strain value of 100 N and a wavelength offset of 0.1 nm; when the dynamic load is 40 Newtons, the strain value is 200 N and the wavelength offset is 0.2 nm. Collect at least 10 sets of data through multiple measurements and organize them into a table. The first column should be the wavelength offset (e.g., 0.1 nm, 0.2 nm), and the second column should be the strain value (e.g., 100 N, 200 N). Save this table as a CSV file and import it into the LabVIEW Spectrum Processing Suite as a calibration curve.

[0062] S2.2 After establishing the calibration curve, it is necessary to calculate the strain value at the monitoring point where the photonic crystal film is located on the surface of the automotive fastener thread. The specific calculation process is as follows: The LabVIEW spectral processing suite first reads each set of wavelength offsets in the real-time wavelength offset data stream. For example, the wavelength offset at point P1 is 0.15 nm. At the same time, it finds the wavelength offset closest to 0.15 nm in the calibration curve. For example, 0.1 nm corresponds to 100 strain, and 0.2 nm corresponds to 200 strain in the calibration curve. Since 0.15 nm is between 0.1 nm and 0.2 nm, a linear interpolation method is needed to determine the specific strain value. This is divided into six steps. Taking point P1 as an example, the first step is to calculate the difference between the two known wavelength offsets in the calibration curve (i.e., the wavelength offset range), which shows that 0.2 nm minus 0.1 nm equals 0.1 nm. The second step is to calculate the increment of the wavelength offset relative to the smaller value in the calibration curve, which shows that 0.15 nm minus 0.1 nm equals 0.05 nm. The third step is to calculate the difference between the two strain values ​​in the calibration curve (i.e., the range of strain value variation). The first step is to determine the proportion of the wavelength increment at point P1 within the wavelength offset range (i.e., the proportion of the wavelength increment to the total range). The result is that 0.05 nm divided by 0.1 nm equals 0.5. The second step is to apply the proportion of the wavelength increment to the total range to the range of strain values ​​(calculating the increment of strain values). The result is that 0.5 multiplied by 100 strain equals 50 strain. The third step is to add the increment of strain values ​​to the range of strain values. The result is that 50 strain plus 100 strain equals 150 strain, which is the final strain value at point P1.

[0063] LabVIEW Spectrum Processing Suite sequentially organizes the strain values ​​of the photonic crystal thin film at the monitoring points on the threaded surface of automotive fasteners to form a strain value dataset (including monitoring point numbers), such as P1, 150 strain, P2, 120 strain.

[0064] S2.3. Create a 3D geometric coordinate file for the threaded surface of the automotive fastener. The specific creation process is as follows: Use a 3D optical scanner (e.g., GOMATOSCore) to perform a full-surface scan of the automotive fastener. Fix the automotive fastener on a rotating platform, which rotates at high speed. The 3D optical scanner collects geometric data of the threaded surface point by point. This geometric data refers to the 3D coordinates of each point on the threaded surface of the automotive fastener, in millimeters. Specifically, it consists of three values: X-axis (distance along the thread axis), Y-axis (distance along the thread radial direction), and Z-axis (distance along the thread height). For example, the coordinates of the thread start point are (0,0,0), 1 millimeter from the start point are (1,0,0), and a point at the top of the thread might be (2,0.5,0.1). The 3D optical scanner records the 3D coordinates of each point using the principle of triangulation (i.e., by measuring the angle and distance of light emitted from the 3D optical scanner, reflected off the threaded surface, and back, thus calculating the spatial position of each point on the threaded surface). The scanning range covers the entire threaded area. After acquisition, the 3D coordinates of each point are converted into an STL format file, forming a 3D geometric coordinate file for the threaded surface of the automotive fastener.

[0065] In MATLAB, a script is created to read the 3D geometric coordinates file of the threaded surface of an automotive fastener and extract the 3D coordinates of all points in the file. The coordinate positions corresponding to the monitoring point numbers in the strain value dataset are manually specified. Equally spaced points along the thread axis are selected from the STL format file; for example, the 3D coordinates of point P1 are (0,0,0), point P2 are (1,0,0), and point P3 is (2,0,0). Then, the strain values ​​in the strain value dataset are matched one by one with all the 3D coordinates. Specifically, a new table is created in the script. The first column is for the monitoring point numbers (e.g., P1, P2), the second column is for the strain values ​​(e.g., 150 strain, 120 strain), and the third column is for the corresponding 3D coordinates (e.g., 0,0,0; 1,0,0). For example, point P1 is matched as (number P1, 150 strain, coordinates 0,0,0), and point P2 is matched as (number P2, 120 strain, coordinates 1,0,0). If the number of coordinate points in the STL file exceeds the number of monitoring points, only coordinates consistent with the monitoring point location are selected, and the remaining points are ignored. If the monitoring point location is slightly off, the 3D coordinates of the nearest point are used. For example, point P3 should be (2, 0, 0). If the 3D coordinates of the nearest point in the STL file are (2.02, 0, 0), then (2.02, 0, 0) will be used. After matching is complete, the script table is saved as a comprehensive dataset.

[0066] S2.4. Load the comprehensive dataset using the Python interpreter and simplify the three-dimensional coordinates in the dataset into two-dimensional planar coordinates. Specifically, extract the X-axis (along the thread axis) and Y-axis (along the thread radial direction) as the horizontal and vertical axes for plotting, ignoring the Z-axis to simplify visualization. Extract the X and Y values ​​for each monitoring point from the comprehensive dataset, and simultaneously construct a two-dimensional grid for the thread surface. The grid uses the X-axis from 0 mm to the thread length (e.g., 20 mm) as the horizontal axis and the Y-axis from -1 mm to 1 mm (i.e., covering the entire radial range of the thread) as the vertical axis. For each grid point, find the nearest monitoring point in the comprehensive dataset. For example, if grid point (1.5,0) is close to point P2 (1,0), assign a strain value of 120. Since the coordinates of grid point (1.5,0) and point P2 (1,0) are only 0.5 mm apart on the X-axis, which is less than the distance of other monitoring points, the strain value of point P2 is directly adopted. If a direct match cannot be found, take the average value of the neighboring monitoring points. For example, for grid point (1.5,0.5), take the average value of points P2 and P3 (assuming the strain value of point P3 is 180, then the strain value of point P2 is 120 plus the strain value of point P2, which equals 300, and dividing by 2 gives 150).

[0067] Configure plotting parameters in the Python interpreter to generate a stress-thermal map. Specifically, create a blank canvas, using the X and Y coordinates of the 2D grid as the horizontal and vertical axes, and the strain value as the color coding. Set a color grading rule: strain values ​​less than 100 are mapped to green, strains between 100 and 200 to yellow, and strains greater than 200 to red. For example, point P1 with a strain value of 150 corresponds to yellow. Then, draw color blocks for each grid point, iterating through all grid point coordinates, reading the corresponding strain value, determining the color, and filling it into the blank canvas; for example, grid point (0,0) is green, and (1,0) is yellow. After plotting, add color bars and indicate the strain value range (0~300 strain) to visually distinguish between high-strain and low-strain areas.

[0068] S2.5 Next, the local strain gradient between adjacent monitoring points needs to be calculated. Specifically, select adjacent monitoring point pairs along the thread axis (i.e., the X-axis) from the comprehensive dataset, such as P1 (coordinates 0,0,0, strain value 150) and P2 (coordinates 1,0,0, strain value 120). Take the difference between the strain values ​​of the two points (150 strain minus 120 strain equals 30 strain), divide it by the distance between the two points (i.e., the spatial distance between adjacent monitoring points on the thread surface of the automotive fastener, for example, the distance between P1 (coordinates 0,0,0) and P2 (coordinates 1,0,0) is 1 mm), and obtain the local strain gradient as 30 strain per millimeter. Repeat the calculation of the local strain gradient for all adjacent monitoring point pairs. For example, the local strain gradient between P2 and P3 (assuming P3 has a strain of 180, coordinates 2,0,0) is 60 strain per millimeter, and organize them into a table of local strain gradient values.

[0069] A strain gradient threshold was set. The specific setting was based on the following: multiple vibration fatigue tests were conducted on a set of bolt samples (e.g., M10 bolts) of the same material and geometry as the tested automotive fasteners. An electromagnetic vibration table was used, applying a sinusoidal sweep frequency dynamic load. During each test, the wavelength shift was recorded and converted into a strain value, and the local strain gradient at adjacent monitoring points was calculated. After each load test, the bolt surface was checked for microcracks or plastic deformation. For example, in one test, when the local strain gradient at the monitoring point reached 48 strain per millimeter, no damage was observed on the bolt surface. However, in another test, when the local strain gradient reached 52 strain per millimeter, an initial crack was observed at the bolt root under a microscope. Based on the combined results of multiple tests, it was statistically found that when the local strain gradient at the monitoring point exceeded 50 strain per millimeter (hypothetically), the probability of damage to the bolt surface increased significantly (e.g., microcracks appeared in 6 out of 8 tests). Therefore, 50 strain per millimeter was selected as the specific numerical range for the strain gradient threshold (the numerical range of the strain gradient threshold can be adjusted according to actual conditions).

[0070] Each local strain gradient value in the local strain gradient table is compared with a strain gradient threshold. For example, if the local strain gradient value between P1 and P2 is 30 strain per millimeter, which is less than 50 strain per millimeter, it is considered a normal local strain gradient. If the local strain gradient between P2 and P3 is 60 strain per millimeter, which is greater than 50 strain per millimeter, it is considered an abnormal local strain gradient. For abnormal cases, the relevant monitoring point numbers (e.g., P2, P3), strain values ​​(120 strain, 180 strain), and coordinates (1,0,0, 2,0,0) are recorded and compiled into an anomaly log file.

[0071] S2.6. Based on the anomaly log file, activate the early warning mechanism to notify the operator. Specifically, connect an audible and visual alarm (equipped with a red flashing light and a decibel buzzer) to the computer processing terminal. Using the control interface in the Python interpreter, send a start command to the audible and visual alarm, simultaneously transmitting the contents of the anomaly log file to the monitoring display screen. The operator runs the display program in the Python interpreter, presenting the anomaly monitoring point number, strain value, and coordinates in tabular form, for example, displaying P2, 120 strain, 1,0,0 and P3, 180 strain, 2,0,0, and highlighting the anomaly area on the strain heat map (e.g., the P2~P3 area is marked with a flashing red border), triggering the audible and visual alarm signal.

[0072] S2.7. Convert the audible and visual alarm signal into a control signal and transmit it to the control terminal of the vibration testing equipment (i.e., the vibration table) to activate the frequency reduction protection mechanism. Specifically, through the serial communication function in the Python interpreter, the trigger command in the exception log file is sent to the vibration table in the form of a data packet. After receiving the trigger command, the vibration table automatically adjusts its operating parameters, that is, reducing the vibration frequency from the current value (assuming 500 Hz) by 10 Hz per second until it reaches 100 Hz, while gradually reducing the amplitude from 5 mm to 2 mm, decreasing by about 0.5 mm each time. During the adjustment period, the vibration table operating status is recorded once per second, generating a log file of vibration frequency and amplitude.

[0073] Operators observe the vibration table's operating status changes on the monitoring display screen. For example, after confirming that the frequency has dropped to 100 Hz, the strain thermogram is reloaded to check if the local strain gradient at the monitoring point has recovered to below 50 strain per millimeter. If it returns to normal, the frequency reduction protection is stopped and the current state is maintained; if it is still abnormal, the vibration frequency is further reduced to 50 Hz. Finally, the operating status of the vibration table is output (in log file format).

[0074] S3. Collect vibration signals and generate vibration spectrum diagrams, construct an equivalent impedance network model and analyze the vibration spectrum diagrams to identify parasitic resonance frequency points.

[0075] Includes the following steps,

[0076] S3.1 Construct an equivalent impedance network model based on the physical characteristics of automotive fasteners, fixtures, and vibration tables. Specifically, the physical properties of automotive fasteners include bolt diameter (e.g., 10 mm), thread length (e.g., 20 mm), fastener material (e.g., high-strength steel), density, and elastic modulus (e.g., 200 GPa). The physical properties of the fixture are contact surface dimensions (e.g., width 50 mm, length 50 mm, thickness 10 mm, directly measured using electronic calipers), fixture density, and fixture elastic modulus (e.g., 70 GPa). The physical property of the vibration table is contact stiffness, which is obtained through testing. The specific testing method is as follows: A Newton force (assumed to be 100 Newtons) is applied between the contact surface of the fixture and the vibration table. A laser displacement sensor is fixed to one side of the fixture and aligned with the contact surface. After applying a force of 100 Newtons, the distance the fixture moves in the direction of the Newton force (i.e., displacement distance, assumed to be 0.1 mm) is recorded. The applied 100 Newton force is divided by the measured displacement distance of 0.1 mm to obtain the contact stiffness of 1000 Newtons per millimeter.

[0077] S3.2 Select LabVIEW software as the processing software. Open the LabVIEW main page and load the vibration table running status log file (vibration frequency, amplitude). Create a data input control and record the vibration frequency and amplitude as excitation units to represent the energy input of the vibration table. Then, calculate the mass of the automotive fastener based on its physical characteristics. The specific calculation process is as follows: Since the bolt diameter is 10 mm and the thread length is 20 mm, the bolt is approximately a cylinder. Therefore, the mass of the automotive fastener is the square of the bolt diameter multiplied by the thread length and then multiplied by one-quarter of pi, which is approximately 12.3 grams (create a mass unit control in LabVIEW software, input 12.3 grams, and mark it as the automotive fastener mass unit).

[0078] The mass of the fixture is calculated based on the contact surface dimensions. The specific calculation process is as follows: multiply the width of 50 mm by the length of 50 mm, then multiply by the thickness of 10 mm to obtain the volume of the fixture as 25,000 cubic millimeters. Convert the volume of the fixture to cubic meters, that is, divide 25,000 cubic millimeters by 1,000,000,000 (1 cubic meter equals 1,000,000,000 cubic millimeters), which is 0.000025 cubic meters. Assuming the density of the fixture is 2700 kg / m³, the fixture mass can be obtained by multiplying its volume by its density (0.000025 × 2700 = 0.0675 kg). Converting the mass unit to grams, we multiply 0.0675 kg by 1000 (1 kg equals 1000 g), resulting in a mass of 67.5 g. In LabVIEW software, create another mass unit control, input the final calculated fixture mass into it, and mark it as the fixture mass unit.

[0079] Calculate the stiffness value of the automotive fastener (obtained by multiplying the cross-sectional area of ​​the automotive fastener by the elastic modulus and dividing by the thread length; assuming the cross-sectional area is 78.5 square millimeters, the stiffness value of the automotive fastener is 78.5 multiplied by 200 and then divided by 20, resulting in 785 Newtons per millimeter). Create a stiffness element control, input the stiffness value of the automotive fastener, and mark it as an automotive fastener stiffness element.

[0080] Create a connection control and arrange all elements according to the vibration energy transfer order. The excitation element is connected to the fixture mass element and the fixture stiffness element in sequence, and then connected to the automotive fastener mass element and the automotive fastener stiffness element to form an equivalent impedance network model.

[0081] S3.3 Acquire vibration signals and generate a vibration spectrum. Specifically, install accelerometers on the vibration table and fix them at the core positions of the automotive fasteners and fixtures. One accelerometer is installed on the bolt head of the automotive fastener, and the other is installed near the bolt connecting the fixture and the vibration table. After installation, connect the accelerometers to a data acquisition device (such as an NIUSB-4431 acquisition card), start the vibration table, and maintain the previous vibration table operation state, for example, a vibration frequency of 100 Hz and an amplitude of 2 mm. Acquire vibration signals from the automotive fasteners and fixtures and generate a vibration signal file (containing timestamps and acceleration values, such as 0.001 seconds, 0.5g).

[0082] S3.4. Using DIAdem software as the processing software, select the Fourier Transform tool in DIAdem to perform Fourier Transform processing on the vibration signal file. Specifically, select the acceleration values ​​(e.g., 0.5g, 0.6g) in the vibration signal file and segment the time series: divide the acceleration values ​​into segments, for example, read the acceleration values ​​within a 10-second timestamp, totaling 10,000 points, and divide them into 10 1-second segments, each segment containing 1,000 acceleration values ​​(e.g., the first segment covers acceleration values ​​from 0.001 seconds to 1 second (0.5g, 0.6g), the second segment covers acceleration values ​​from 1.001 seconds to 2 seconds, and so on). Then apply the Hanning window. For each segment, select the window function tool in DIAdem software and apply the Hanning window. DIAdem software automatically generates 1,000 weight values, with the first and last point weight values ​​close to 0 (e.g., 0.01), and the 500th point weight close to 1 (e.g., 0.99). Multiply the acceleration value at each point by the corresponding weight value. For example, 0.5g at point 1 is multiplied by 0.01 to get 0.005g, and 0.6g at point 500 is multiplied by 0.99 to get 0.594g, thus forming windowed data segments.

[0083] Perform a Fourier transform on each windowed data segment to calculate the amplitude at each vibration frequency. Specifically, for each vibration frequency (e.g., 150 Hz), calculate the contributions of the sine and cosine components. This involves iterating through 1000 acceleration values ​​(e.g., 0.005g, 0.594g), multiplying each acceleration value by the corresponding sine value at 150 Hz (sin(2π×150×t), where t is time, e.g., 0.001 seconds) and cosine value (cos(2π×150×t)), and summing them up. For example, the first value, 0.005g, multiplied by sin(2π×150×0.001), yields 0.002g. Summing all 1000 values ​​gives the total sine component (e.g., 0.1g). The cosine component is calculated similarly, summing up to 0.05g. Then, take the square of the sum of the sine components (i.e., 0.01) and add the square of the sum of the cosine components (i.e., 0.0025), sum to get 0.0125, take the square root to get 0.112g, multiply by 2 and divide by 1000 (1000 acceleration values), and get the amplitude value of the vibration frequency, which is approximately 0.224g.

[0084] Integrate the amplitude values ​​of all vibration frequencies and calculate the average spectrum of the amplitude values. For each frequency (e.g., 150 Hz), extract the amplitude values ​​of multiple segments (e.g., 10), such as 0.223g, 0.225g, 0.224g, etc. Add the 10 amplitude values ​​together (e.g., 0.223 + 0.225 + ... = 2.23g, where 2.23g is an assumption), divide by 10, and obtain the average amplitude value of 0.223g at the 150 Hz vibration frequency. Repeat the calculation process to calculate the average spectrum of all amplitude values ​​and generate a vibration spectrum diagram, for example, 0.224g at 150 Hz and 0.180g at 200 Hz.

[0085] S3.5. Use the equivalent impedance network model to analyze the resonant frequency in the vibration spectrum. Specifically, check the amplitude peak value in the vibration spectrum one by one (the amplitude peak value refers to the highest point in the vibration spectrum, that is, the maximum amplitude value, for example, the maximum amplitude value is 0.8g at a vibration frequency of 170 Hz, and the maximum amplitude value is 0.7g at a vibration frequency of 200 Hz). Taking a vibration frequency of 150 Hz as an example, use the equivalent impedance network model to predict the theoretical amplitude peak value. The specific process is as follows: First, calculate the natural frequency by dividing the stiffness value of the car fastener (785 Newtons per millimeter) by the mass of the car fastener (12.3 grams, i.e., 0.0123 kilograms), take the square root to get 8000, and then divide by 2π (approximately 6.283) to obtain approximately 1273 Hz. Then determine the basic amplitude (i.e., the excitation acceleration, which can be understood as the acceleration generated by the vibration table during vibration, obtained according to the operating state of the vibration table, such as a vibration frequency of 100 Hz and an amplitude of 2 millimeters, then the expression for the excitation acceleration is:

[0086] ;

[0087] in, Indicates acceleration due to excitation. This indicates the vibration frequency, i.e., 100 Hz. This indicates the amplitude, which is 2 millimeters, approximately equal to 789 meters per second squared, which is 80.5g in grams.

[0088] Considering the actual output limitations of the vibration table and the attenuation of the fixture, the excitation acceleration is changed to 0.9g. Since the damping ratio (the rate at which vibration energy is dissipated due to damping, ranging from 0 to 1; in vibration testing of automotive fasteners and fixtures, the damping ratio measures the rate at which the vibration amplitude decreases over time. For example, a damping ratio of 0.02 means that the vibration amplitude decays slowly, indicating slight damping, suitable for the low-frequency vibration response of high-strength steel and aluminum alloy materials; therefore, setting the damping ratio to 0.02) will reduce the peak amplitude, the attenuation ratio of the peak amplitude needs to be calculated, i.e., 1 - 0.02 = 0.98. Multiplying the attenuation ratio by the base amplitude of 0.9g yields a theoretical peak amplitude of approximately 0.75g.

[0089] S3.6. Set the amplitude difference threshold. The setting is based on preparing a set of bolt samples with the same material and geometry as the tested automotive fasteners, and using the same fixture. Apply sinusoidal sweep vibrations from 20 to 500 Hz, and use an accelerometer to collect the vibration signals, generating a vibration spectrum and recording the peak amplitude. Then, estimate the theoretical peak amplitude using an equivalent impedance network model. Compare the theoretical peak amplitude with the actual peak amplitude from multiple tests, and statistically analyze the difference distribution. For example, at 150 Hz, the differences from 10 tests are 0.03g, 0.05g, 0.06g, etc., with an average of approximately 0.04g. At 200 Hz, the differences are 0.3g, 0.35g, etc., with an average of approximately 0.4g. When high amplitude occurs at frequencies with a difference less than 0.1g (e.g., 150 Hz), microscopic observation of the thread surface will reveal tiny fatigue marks, indicating a risk of resonance. When the difference is greater than 0.1g at frequencies (e.g., 200 Hz), there is no obvious damage, and it is considered normal vibration. Multiple sets of tests were conducted, and a difference of 0.1g was set as the dividing point (the specific value of the amplitude difference threshold can also be dynamically adjusted according to the actual situation).

[0090] Calculate the difference between the theoretical peak amplitude and the actual peak amplitude. When the difference is less than the amplitude difference threshold, the vibration frequency is marked as the parasitic resonance frequency point (for example, for a vibration frequency of 150 Hz, the theoretical peak amplitude is 0.75g, and the actual peak amplitude is 0.8g, then the difference is 0.05g, which is less than the amplitude difference threshold, so the vibration frequency of 150 Hz is marked as the parasitic resonance frequency point). When the difference is greater than the amplitude difference threshold, the vibration frequency is marked as the normal vibration frequency.

[0091] S4. Use a hydraulic damper to suppress frequency band energy reflection at parasitic resonance frequency points to obtain the vibration spectrum state, and use a piezoelectric actuator to generate a reverse waveform. Use the reverse waveform to cancel the residual standing wave in the vibration spectrum state and output a stable vibration state report.

[0092] Includes the following steps,

[0093] S4.1 Select a hydraulic damper as the suppressor and calculate its viscosity coefficient. Specifically, the calculation consists of the following steps (taking a vibration frequency of 150 Hz as an example): First, convert the actual peak amplitude into an acceleration value, i.e., 0.8g multiplied by 9.8 (gravitational acceleration), to obtain 7.84 m / s²; Second, estimate the vibration energy at the parasitic resonance frequency point, which is equal to half the mass of the car fastener multiplied by the square of the acceleration, i.e., 0.0123 × (7.84). 2 ×0.5 (kinetic energy fixed coefficient) ≈ 0.000378 Joules; the third step is to determine the energy absorbed by the hydraulic damper. Since it is necessary to suppress frequency band energy reflection, the hydraulic damper needs to absorb twice the vibration energy, i.e., 0.000378 × 2 = 0.000756 Joules; the fourth step is to estimate the piston speed of the hydraulic damper. The piston speed is the amplitude of the vibration table (2 mm, i.e., 0.002 m) multiplied by 2π and then multiplied by the vibration frequency, i.e., 0.002 × 2π × 150 ≈ 0.9425 m / s, take an approximation of 0.01 m / s (considering actual attenuation); the fifth step is to calculate the damping force of the hydraulic damper, that is, the energy absorbed by the hydraulic damper divided by the distance the piston moves (assumed to be 0.01 m), then the damping force is 0.000756 ÷ 0.01 = 0.0756 Newtons; the sixth step is to divide the damping force by the piston speed to obtain the final viscosity coefficient, which is 0.0756 ÷ 0.01 = 7.56 Newtons per second per meter.

[0094] S4.2 The viscosity coefficient of the hydraulic damper is adjusted and suppressed for verification. Specifically, the control panel of the hydraulic damper is manually opened, and the solenoid valve adjustment knob (indicated as 0~100% opening; opening refers to the degree of opening of the solenoid valve of the hydraulic damper, expressed as a percentage, ranging from 0%, i.e., completely closed, to 100%, used to control the flow rate of hydraulic oil in the damper circuit, thereby adjusting the fluid viscous resistance and affecting the viscosity coefficient; for example, an opening of about 50% means that the solenoid valve is partially open and the hydraulic oil flow is moderate) is read. The viscosity coefficient is 7 for a vibration frequency of 150 Hz. 56 N / s per meter. According to the damper manual (which refers to the written technical documentation provided by Vickers for servo hydraulic dampers, containing the technical specifications, operating instructions, and performance parameters of the hydraulic damper. The manual records a table relating the solenoid valve opening to the output viscosity coefficient of the hydraulic damper. For example, the manual states that 50% opening corresponds to approximately 8 N / s per meter, and 35% opening corresponds to approximately 6 N / s per meter), since 50% opening corresponds to approximately 8 N / s per meter, adjusting the solenoid valve knob to around 49% (slightly below 50%) will yield approximately 7.56 N / s per meter.

[0095] Start the vibration table and apply test signals one by one (set the vibration frequency to 150 Hz and the amplitude to 2 mm). Use an accelerometer to measure the amplitude of the automotive fasteners, read the sensor display and record the amplitude decrease, for example, from 0.8g to 0.4g, indicating that the reflected energy at 150 Hz has been absorbed by the hydraulic damper. Compile all adjustment results and name the results a table called "Damping Adjustment Results," containing three columns: vibration frequency, viscosity coefficient, and adjusted amplitude, for example, 150Hz, 7.56 N·s / m, 0.4g.

[0096] S4.3. Based on the damping adjustment results, generate the vibration spectrum state. Specifically, restart the vibration table, apply a signal at a non-parasitic resonance frequency point, such as 100 Hz (amplitude 2 mm), use an accelerometer to measure the amplitude of the automotive fastener and record the displayed value of the accelerometer, such as 0.1 g. Then apply a vibration frequency of 300 Hz and record the amplitude of the automotive fastener again, such as 0.08 g. Compile all the amplitudes into a table named the Vibration Spectrum State Table, which includes the vibration frequency and amplitude, such as 100 Hz, 0.1 g; 150 Hz, 0.4 g; 200 Hz, 0.3 g; 300 Hz, 0.08 g (if the amplitude at a certain parasitic resonance frequency point is still high, such as 0.6 g at 150 Hz, then fine-tune the viscosity coefficient, for example, increase it to 8 N / s per meter, and remeasure the amplitude).

[0097] S4.4 Identify residual standing waves using the vibration spectrum state (residual standing waves refer to vibration waves that still exist in the vibration spectrum state after the hydraulic damper suppresses the energy reflection at the parasitic resonance frequency point). Specifically, set the amplitude background level (i.e., the acceleration amplitude of the automotive fastener and clamp at the non-resonant frequency). Use an accelerometer to measure the amplitude at the non-resonant frequency (e.g., 100 Hz, 300 Hz) multiple times and record the average amplitude. Select the average amplitude as the amplitude background level, such as 0.1g. When the amplitude in the vibration spectrum state is greater than the amplitude noise level, it is marked as a residual standing wave (e.g., an amplitude of 0.4g at a vibration frequency of 150 Hz is greater than 0.1g, so it is marked).

[0098] Extract 1000 amplitude points from the vibration spectrum (one point every 0.01 seconds, e.g., 0.4g), calculate the phase of the residual standing wave. Taking 150 Hz as an example, multiply the amplitude 0.4g by the sine value (sin(2π×150×t), where t is time, such as 0.001 seconds, 0.002 seconds, etc.), and accumulate 1000 amplitude points to obtain the sine component, for example, 0.4×sin(0.942) + 0.39×sin(1.884) + ... ≈ 200g. Multiply each amplitude point by the cosine value, i.e., cos(2π×150×t), and accumulate to obtain the cosine component, for example, 0.4×cos(0.942) + ... ≈ 50g. Divide the sine component by the cosine component to obtain the phase of the residual standing wave as arctan(200÷50) ≈ 0.2 radians (rad). Repeatedly calculate the phase of all residual standing waves and integrate them with the vibration spectrum state into a residual wave table, which includes vibration frequency, amplitude, and residual standing wave phase, for example, 150 Hz, 0.4 g, 0.2 rad.

[0099] S4.5 Next, we need to use a piezoelectric actuator to calculate the voltage of the reverse waveform. Specifically, taking a 150 Hz residual standing wave as an example, we convert the amplitude 0.4g into acceleration, which is 0.4 × 9.8 = 3.92 m / s² (the amplitude of the reverse waveform is consistent with the amplitude of the residual standing wave, which is 0.4g). After adding π to the phase of the residual standing wave, we obtain the reverse phase of the reverse waveform, which is 0.2 radians + π ≈ 3.34 radians. Then, we calculate the displacement of the reverse waveform by dividing the acceleration amplitude by the square of the angular frequency (the angular frequency is 2π × 150 ≈ 942.477 radians per second): 3.92 ÷ (942.477). 2 ≈4.4×10 -6 Meters (i.e., 4.4 micrometers); assuming the sensitivity of the piezoelectric actuator is 0.1 micrometers per volt, then the voltage of the reverse waveform is 4.4 ÷ 0.1 = 44 volts.

[0100] The voltage, vibration frequency, amplitude, and phase of the reverse waveform are integrated to form the reverse waveform parameters, such as 150Hz, 0.4g, 3.34rad, and 44V.

[0101] S4.6. Generate a reverse waveform using reverse waveform parameters and cancel the residual standing wave. Fix the piezoelectric brake to the bottom of the automotive fastener bolt using high-strength adhesive. Connect the piezoelectric brake to a signal generator, input the reverse waveform parameters to the signal generator, and drive the piezoelectric actuator to generate vibrations opposite to the residual standing wave, thereby canceling the residual standing wave. Taking a vibration frequency of 150 Hz as an example, the input parameters are amplitude 0.4g (reverse waveform voltage 44 volts), frequency 150 Hz, and residual standing wave phase 3.34 radians. Manually set the signal generator by adjusting the voltage knob to 44 volts, the frequency knob to 150 Hz, and the phase knob to 3.34 radians to activate the sine wave output. When the piezoelectric actuator receives the signal from the signal generator, it produces a 4.4-micrometer displacement (44 volts × 0.1 micrometers per volt), which is opposite in direction to the 150 Hz residual standing wave (0.4g, 4.4-micrometer displacement). The resulting sine wave (e.g., 150 Hz, 44 volts, 3.34 radians) is the reverse waveform. Activate the accelerometer, apply the 150 Hz reverse waveform, and read the sensor display to record the amplitude decrease of the automotive fastener, for example, from 0.4g to 0.05g. Compile the results of the residual standing wave cancellation into a handwritten table named "Stable Vibration State Report," including the vibration frequency, reverse waveform voltage, and amplitude after residual standing wave cancellation, for example, 150Hz, 44V, 0.05g.

[0102] This embodiment also provides a monitoring system for an automotive fastener vibration testing device, including: a data generation module that sprays a photonic crystal thin film onto the automotive fastener, collects light wavelength distribution data on the surface of the photonic crystal thin film, and generates a wavelength offset data stream;

[0103] The operation module uses wavelength offset data stream to calculate the strain value of the monitoring point of the car fastener, draws the strain thermogram and calculates the local strain gradient, sets the strain gradient threshold, and when the local strain gradient exceeds the strain gradient threshold, it issues an audible and visual alarm signal and transmits it to the vibration table, activates the vibration table frequency reduction protection mechanism, and obtains the operating status of the vibration table.

[0104] The frequency identification module collects vibration signals and generates vibration spectrum diagrams, constructs an equivalent impedance network model, analyzes the vibration spectrum diagrams, and identifies parasitic resonance frequency points.

[0105] The state generation module uses a hydraulic damper to suppress frequency band energy reflection at parasitic resonance frequency points to obtain the vibration spectrum state. It then uses a piezoelectric actuator to generate a reverse waveform, which is used to cancel the residual standing waves in the vibration spectrum state and output a stable vibration state report.

[0106] This embodiment also provides a computer device applicable to a monitoring method for automotive fastener vibration testing equipment, comprising: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the monitoring method for automotive fastener vibration testing equipment as proposed in the above embodiment.

[0107] The computer device can be a terminal, comprising a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0108] This embodiment also provides a storage medium storing a computer program, which, when executed by a processor, implements the monitoring method for an automotive fastener vibration testing device as proposed in the above embodiments. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0109] In summary, this invention effectively avoids the risk of equipment damage caused by inaccurate resonant frequency suppression by immediately triggering early warning and protection measures upon detecting abnormal strain gradients, thereby improving the safety and efficiency of testing. Simultaneously, the constructed equivalent impedance network model improves the accuracy of parasitic resonant frequency point identification, solves the problem of inaccurate resonant frequency suppression, and reduces unnecessary energy consumption and mechanical damage. Furthermore, by calculating the strain values ​​at the monitoring points of automotive fasteners, the local strain of automotive fasteners is efficiently quantified, greatly improving the anomaly detection accuracy of the dynamic protection mechanism and compensating for the lack of refined strain analysis in existing automotive fastener vibration testing equipment monitoring methods.

[0110] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended 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 monitoring method for an automotive fastener vibration testing device, characterized in that: include, Photonic crystal thin films are sprayed onto automotive fasteners, and light wavelength distribution data on the surface of the photonic crystal thin film are collected to generate a wavelength offset data stream. The strain value of the monitoring point of the car fastener is calculated by using the wavelength offset data stream, the strain thermogram is drawn and the local strain gradient is calculated, the strain gradient threshold is set, and when the local strain gradient exceeds the strain gradient threshold, an audible and visual alarm signal is issued and transmitted to the vibration table to activate the vibration table frequency reduction protection mechanism and obtain the operating status of the vibration table. Vibration signals are collected and vibration spectrum diagrams are generated. An equivalent impedance network model is constructed and the vibration spectrum diagrams are analyzed to identify parasitic resonance frequency points. Hydraulic dampers are used to suppress frequency band energy reflection at parasitic resonant frequency points to obtain the vibration spectrum state. A reverse waveform is generated by a piezoelectric actuator, and the reverse waveform is used to cancel the residual standing waves in the vibration spectrum state to output a stable vibration state report.

2. The monitoring method of the automotive fastener vibration testing equipment as described in claim 1, characterized in that: The generation of wavelength offset data stream includes using a laser interferometer to collect optical wavelength distribution data on the surface of a photonic crystal thin film, and numbering the optical wavelength distribution data on the surface of the photonic crystal thin film in spatial order to form automotive fastener monitoring points; The LabVIEW Spectrum Processing Suite was used to extract the wavelength peak values ​​of each automotive fastener monitoring point and organize them to form a reflectance spectrum database. The reflectance spectral data of photonic crystal thin films were collected using an HR4000 spectrometer to form a reflectance spectral dataset. The reflectance spectral dataset was compared with the reflectance spectral database to calculate the wavelength offset and organize it into a wavelength offset data stream.

3. The monitoring method for the automotive fastener vibration testing equipment as described in claim 2, characterized in that: The calculation of the strain value of the automotive fastener monitoring point includes establishing a calibration curve, finding the wavelength offset that is closest to the wavelength offset data stream in the calibration curve, and converting the closest wavelength offset into the strain value of the automotive fastener monitoring point through linear interpolation.

4. The monitoring method for the automotive fastener vibration testing equipment as described in claim 3, characterized in that: The process of obtaining the operating status of the vibration table includes organizing the strain values ​​of the monitoring points of the automotive fasteners, forming a strain value dataset, and using a 3D optical scanner to create a 3D geometric coordinate file of the automotive fasteners. The 3D coordinates of the 3D geometric coordinate file are extracted using MATLAB software. The strain values ​​in the strain value dataset are then matched with the 3D coordinates to form a comprehensive dataset. The Python interpreter is used to map the three-dimensional coordinates in the comprehensive dataset to two-dimensional planar coordinates, construct a two-dimensional grid, find the monitoring point that is closest to the comprehensive dataset for each grid point and assign strain values ​​to it, and generate a stress-thermal map. Based on the stress-thermal diagram, the local strain gradient values ​​between adjacent monitoring points are calculated and compiled into a local strain gradient value table. A strain gradient threshold is set, and each local strain gradient value in the local strain gradient table is compared with the strain gradient threshold. An anomaly log file is then output. Based on the anomaly log file, the early warning mechanism is activated to issue an audible and visual alarm signal. The trigger command in the anomaly log file is sent to the vibration table, and the operating status of the vibration table is obtained after observing the changes in its operating status.

5. The monitoring method for the automotive fastener vibration testing equipment as described in claim 4, characterized in that: The construction of the equivalent impedance network model includes compiling the operating status of the vibration table into a vibration table operating status log file, which includes vibration frequency and amplitude. Use LabVIEW software to create a data input control, record the vibration frequency and amplitude as excitation units, calculate the mass of the automotive fastener, the mass of the fixture, and the stiffness value of the automotive fastener, and label them as automotive fastener mass unit, fixture mass unit, and automotive fastener stiffness unit, respectively. Create connection controls and arrange all elements according to the order of vibration energy transmission to form an equivalent impedance network model.

6. The monitoring method of the automotive fastener vibration testing equipment as described in claim 5, characterized in that: The identification of parasitic resonant frequency points includes using an accelerometer to collect vibration signals from automotive fasteners and clamps, and generating vibration signal files; The vibration signal file is processed by Fourier transform to calculate the amplitude values ​​of all vibration frequencies and form a vibration spectrum. Examine the amplitude peak in the vibration spectrum and predict the theoretical amplitude peak using the equivalent impedance network model. Set an amplitude difference threshold, calculate the difference between the amplitude peak and the theoretical amplitude peak, and compare it with the amplitude difference threshold to identify the parasitic resonance frequency point.

7. The monitoring method for the automotive fastener vibration testing equipment as described in claim 6, characterized in that: The output stable vibration state report includes selecting a hydraulic damper as a suppressor, calculating the viscosity coefficient of the hydraulic damper and verifying the suppression, and obtaining the damping adjustment result. Based on the damping adjustment results, a signal at the non-parasitic resonance frequency point is applied to the automotive fastener, and the vibration frequency and amplitude of the automotive fastener are recorded to form a vibration spectrum state. The residual standing wave is identified by the vibration spectrum state, and the phase of the residual standing wave is calculated. At the same time, the voltage of the reverse waveform is calculated by using a piezoelectric actuator. The voltage of the reverse waveform, the vibration spectrum state, and the phase of the residual standing wave are integrated to form the reverse waveform parameters. A reverse waveform is generated using the reverse waveform parameters, and the residual standing wave is canceled to obtain a stable vibration state report.

8. A monitoring system for an automotive fastener vibration testing device, based on the monitoring method for the automotive fastener vibration testing device according to any one of claims 1 to 7, characterized in that: include, The data generation module sprays a photonic crystal thin film onto automotive fasteners, collects light wavelength distribution data on the surface of the photonic crystal thin film, and generates a wavelength offset data stream. The operation module uses wavelength offset data stream to calculate the strain value of the monitoring point of the car fastener, draws the strain thermogram and calculates the local strain gradient, sets the strain gradient threshold, and when the local strain gradient exceeds the strain gradient threshold, it issues an audible and visual alarm signal and transmits it to the vibration table, activates the vibration table frequency reduction protection mechanism, and obtains the operating status of the vibration table. The frequency identification module collects vibration signals and generates vibration spectrum diagrams, constructs an equivalent impedance network model, analyzes the vibration spectrum diagrams, and identifies parasitic resonance frequency points. The state generation module uses a hydraulic damper to suppress frequency band energy reflection at parasitic resonance frequency points to obtain the vibration spectrum state. It then uses a piezoelectric actuator to generate a reverse waveform, which is used to cancel the residual standing waves in the vibration spectrum state and output a stable vibration state report.

9. 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 monitoring method for the automotive fastener vibration testing equipment according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of the monitoring method of the automotive fastener vibration testing equipment according to any one of claims 1 to 7.

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