Method and apparatus for calculating risk of failure of component on the basis of vibration load simulation

Abstract

The present application discloses a method for calculating the risk of failure of a component on the basis of vibration load simulation, comprising: inputting a determined PSD spectrum load into predetermined target software, so as to convert the PSD spectrum load into initial time domain information; executing a signal repetition operation, obtaining target time domain information, and calculating fatigue damage information; converting the information into target frequency domain data, and generating compressed spectrum data; and inputting the data into a vibration simulation model, obtaining a model output result, and determining a risk of failure evaluation result of a target component.

Description

Component failure risk calculation method and device based on vibration load simulation

[0001] The present application claims priority to the Chinese patent application No. 2024109977097 filed on July 23, 2024 with the China Patent Office, the whole content of the above application is incorporated herein by reference. TECHNICAL FIELD

[0002] The present application relates to the technical field of data processing, in particular to a component failure risk calculation method and device based on vibration load simulation. BACKGROUND

[0003] In the related art, the PSD (Power Spectral Density) spectrum mentioned in the safety requirements and test methods for lithium-ion power battery packs and systems for electric vehicles is mainly related to the vibration fatigue test of the power battery pack of the electric vehicle. Currently, random vibration simulation mainly obtains the root mean square stress value of each component by directly using the PSD spectrum for random vibration simulation analysis through frequency spectrum analysis, and obtains the failure risk of each component by comparing with the tensile strength or yield strength of the corresponding material of each component; or obtains the damage value of each component based on the PSD spectrum for random vibration fatigue analysis and determines whether the structure has a failure risk in combination with the Miner theory. TECHNICAL PROBLEM

[0004] The above component failure risk evaluation method has low accuracy, low efficiency and low intelligence. TECHNICAL SOLUTION

[0005] In a first aspect, the present application provides a component failure risk calculation method based on vibration load simulation, the method comprising:

[0006] inputting the determined PSD spectrum load into a pre-determined target software to convert the PSD spectrum load into initial time domain information by the target software;

[0007] performing a signal repetition operation on the initial time domain information to obtain target time domain information, and calculating fatigue damage information based on the target time domain information;

[0008] converting the fatigue damage information into target frequency domain data, and generating compressed frequency spectrum data according to the target frequency domain data;

[0009] inputting the compressed frequency spectrum data into a pre-determined vibration simulation model to obtain a model output result of the vibration simulation model, and determining a failure risk evaluation result of a target component based on the model output result.

[0010] In a second aspect, the application provides a component failure risk calculation device based on vibration load simulation, the device comprising:

[0011] a conversion module configured to input the determined PSD spectral load into a predetermined target software to convert the PSD spectral load into initial time domain information by the target software;

[0012] a signal repetition module configured to perform a signal repetition operation on the initial time domain information to obtain target time domain information;

[0013] a calculation module configured to calculate fatigue damage information based on the target time domain information;

[0014] a generation module further configured to convert the fatigue damage information into target frequency domain data and generate compressed frequency spectrum data according to the target frequency domain data;

[0015] an input module configured to input the compressed frequency spectrum data into a predetermined vibration simulation model to obtain a model output result of the vibration simulation model;

[0016] a determination module configured to determine a failure risk evaluation result of a target component based on the model output result.

[0017] In a third aspect, the application provides another component failure risk calculation device based on vibration load simulation, the device comprising:

[0018] a memory storing executable program codes;

[0019] a processor coupled with the memory;

[0020] the processor invokes the executable program codes stored in the memory to execute the component failure risk calculation method based on vibration load simulation disclosed in the first aspect of the application.

[0021] In a fourth aspect, the application provides a computer storage medium storing computer instructions, the computer instructions being invoked to execute the component failure risk calculation method based on vibration load simulation disclosed in the first aspect of the application. Advantages

[0022] In this application, the determined PSD spectral load is input into a pre-defined target software to convert the PSD spectral load into initial time-domain information. A signal repetition operation is performed on the initial time-domain information to obtain target time-domain information, and fatigue damage information is calculated based on this target time-domain information. The fatigue damage information is then converted into target frequency-domain data, and compressed spectrum data is generated based on this target frequency-domain data. The compressed spectrum data is input into a vibration simulation model to obtain the model output results, and the failure risk assessment results of the target component are determined based on these model output results. Therefore, implementing this application can improve the intelligence and efficiency of calculating the failure risk of components, as well as the accuracy and reliability of such calculations, thereby improving the safety of the components used. Attached Figure Description

[0023] Figure 1 is a flowchart illustrating a component failure risk calculation method based on vibration load simulation disclosed in an embodiment of this application;

[0024] Figure 2 is a flowchart illustrating another component failure risk calculation method based on vibration load simulation disclosed in an embodiment of this application;

[0025] Figure 3 is a schematic diagram of the structure of a component failure risk calculation device based on vibration load simulation disclosed in an embodiment of this application;

[0026] Figure 4 is a schematic diagram of another component failure risk calculation device based on vibration load simulation disclosed in an embodiment of this application;

[0027] Figure 5 is a schematic diagram of another component failure risk calculation device based on vibration load simulation disclosed in an embodiment of this application. Embodiments of the present invention

[0028] This application discloses a method and apparatus for calculating component failure risk based on vibration load simulation. This method improves the intelligence and efficiency of calculating component failure risk, as well as the accuracy and reliability of the calculation, thereby enhancing the safety of the components in use. Detailed explanations follow.

[0029] Example 1

[0030] Please refer to FIG. 1, which is a flowchart of a component failure risk calculation method based on vibration load simulation disclosed in an embodiment of the present application. The component failure risk calculation method based on vibration load simulation described in FIG. 1 can be applied to a component failure risk calculation device based on vibration load simulation, wherein the component failure risk calculation based on vibration load simulation can be integrated in a local server or a cloud server, and the present application embodiment is not limited. As shown in FIG. 1, the component failure risk calculation method based on vibration load simulation can include the following operations:

[0031] 101, input the determined PSD spectral load into the pre-determined target software to convert the PSD spectral load into initial time domain information by the target software.

[0032] In some embodiments of the present application, the determined PSD spectral load can include the PSD spectral load of the random vibration part in GB31467.3_2015 national standard; wherein the PSD (power spectral density) spectral load of the random vibration part in GB / T 31467.3-2015 national standard mainly relates to the safety requirements and test methods of lithium-ion power battery pack and system for electric vehicles. Further, according to GB / T 31467.3-2015, electric vehicle power battery pack needs to be tested for vibration fatigue on a vibration table to ensure its safety and reliability under various vibration conditions. In the vibration test, the random excitation load is represented by the PSD curve, which can describe the frequency domain characteristics of the random vibration signal and provide an important basis for analyzing the response and fatigue life of the structure under random vibration. The national standard clearly specifies the specific parameters and requirements of the vibration test, including the vibration frequency range, acceleration amplitude, etc. The specific shape and parameters of the PSD spectral load may vary depending on different test requirements, but generally should reflect the vibration characteristics under actual road conditions. The pass rate of the vibration test is low, which reflects the severity of the test conditions. Therefore, when formulating the PSD spectral load, the actual road conditions and the structural characteristics of the battery pack should be fully considered to ensure the rationality and effectiveness of the test; the random excitation signal is represented by PSD, and the stress and strain response of the structure should also be represented by PSD. Therefore, it is necessary to study the relationship between the excitation signal PSD and the response signal PSD to accurately evaluate the response and fatigue life of the structure under random vibration.

[0033] In some embodiments of the embodiments of the present application, the PSD spectrum is used to verify the safety and reliability of the lithium-ion power battery pack for electric vehicles under random vibration conditions. The PSD spectrum, i.e., Power Spectral Density (PSD), is a way to describe the frequency content of a signal or time series. The PSD describes the power distribution of a signal at each frequency, which indicates the change of signal power with frequency. The PSD is a physical quantity representing the relationship between the power energy of a signal and frequency, and is usually used to study random vibration signals. The PSD is often used to analyze the spectral characteristics of signals and the distribution of noise power, especially in the fields of signal processing, communication systems, and control systems.

[0034] In some embodiments of the embodiments of the present application, the predetermined target software can include Ncode software, which is a complex engineering software widely used in the field of engineering design and analysis, and is mainly used for evaluating and managing the durability of products. The Ncode software provides complex engineering software and services, enabling users to effectively manage the durability of products in engineering design. It supports fatigue life prediction, calculates stress and strain through finite element analysis (FEA) results, and accumulates damage caused by repeated load to determine the service life of products.

[0035] In some embodiments of the embodiments of the present application, the PSD spectrum load determined is input into the predetermined target software to convert the PSD spectrum load into initial time domain information through the target software. This can include inputting the determined PSD spectrum load into the Ncode software to convert the frequency domain signal of the PSD spectrum load into a time domain signal through a time domain generator in the Ncode software to obtain initial time domain information.

[0036] 102. Perform a signal repetition operation on the initial time domain information to obtain target time domain information, and calculate fatigue damage information based on the target time domain information.

[0037] In some embodiments of the embodiments of the present application, the signal repetition operation includes a signal repetition number operation, i.e., performing a signal repetition operation on the initial time domain information for a predetermined number of times. The predetermined number of times can be 2.

[0038] In some embodiments of the embodiments of the present application, the fatigue damage information can include a fatigue damage value corresponding to the initial time domain information, i.e., a fatigue damage value corresponding to the target component.

[0039] 103. Convert the fatigue damage information into target frequency domain data, and generate compressed frequency spectrum data according to the target frequency domain data.

[0040] In some embodiments, converting the fatigue damage information into target frequency domain data can be converting the fatigue damage information into corresponding frequency domain data by a TestSynthesis module in the Ncode software, wherein the fatigue damage information can include one or more of vibration stress information, strain information, etc.; and the corresponding frequency domain data can include a PSD (Power Spectral Density) spectrum. Further, the TestSynthesis module is usually used for test synthesis, especially in certain specific test environments, such as vibration fatigue tests; the estSynthesis module calculates test specification PSD (Power Spectral Density) or sine sweep from the combination of frequency spectra such as Shock Response Spectrum (SRS) and Fatigue Damage Spectrum (FDS), which are usually created based on time series data and power spectral density (PSD) acceleration data, the module accepts the output from other figures (such as Shock Response Spectrum and Extreme Response Spectrum) as input, which are respectively used to calculate the shock response spectrum according to the time series data and the extreme response spectrum from the PSD histogram, the TestSynthesis module uses the input frequency spectrum data to synthesize the test specification that meets the specific requirements through certain algorithms or calculation rules, and these test specifications can be vibration fatigue tests for specific products, which aims to simulate the vibration environment in actual use to evaluate the durability and reliability of the product, the TestSynthesis module in the nCode GlyphWorks or other similar test and analysis software usually works with other modules (such as Vibration Generator) to form a complete test and analysis process.

[0041] 104. inputting the compressed frequency spectrum data into the pre-determined vibration simulation model to obtain a model output result of the vibration simulation model, and determining a failure risk evaluation result of the target component based on the model output result.

[0042] In some embodiments, the pre-determined vibration simulation model is a vibration simulation model that is built and trained to convergence.

[0043] In some embodiments, the target component can be any component that is subjected to vibration load simulation test, and the number of target components can be one or more, which is not limited in the embodiments.

[0044] In some embodiments, the failure risk assessment result of the target component can include one or more of a material fatigue assessment result, an operation life assessment result, and an operation failure assessment result of the target component.

[0045] It can be seen that the component failure risk calculation method based on vibration load simulation described in FIG. 1 can input the PSD spectrum load into the target software to convert the PSD spectrum load into initial time domain information, perform signal repetition operation on the initial time domain information to obtain target time domain information, and further calculate fatigue damage information. The fatigue damage information is converted into target frequency domain data and compressed spectrum data is generated. The compressed spectrum data is input into a vibration simulation model to obtain a model output result, and the failure risk assessment result of the target component is determined according to the model output result. By converting the PSD spectrum load into time domain information, the vibration situation that the target component may encounter in the actual working environment can be accurately simulated, which can facilitate more accurate assessment of the performance and durability of the target component under real conditions. The signal repetition operation is performed on the initial time domain information to obtain the target time domain information, and the fatigue damage information is calculated based on these information. The repeated stress effect of the component in the vibration environment can be considered, which can improve the accuracy and reliability of the fatigue damage assessment of the target component. The fatigue damage information is converted into target frequency domain data, and compressed spectrum data is generated, which can reduce the redundancy and storage requirement of data. The compressed spectrum data is easier to process in the subsequent vibration simulation model, which improves the analysis efficiency and data processing speed. The compressed spectrum data is input into the pre-determined vibration simulation model to obtain the model output result. The dynamic response of the component in the vibration environment can be simulated through the vibration simulation model to predict its performance and potential failure risk, which can improve the accuracy and reliability of the failure risk assessment of the target component, and improve the intelligence and efficiency of obtaining the failure assessment result of the target component, and further improve the safety of using the components.

[0046] Embodiment Two

[0047] Please refer to FIG. 2, which is a flowchart of another component failure risk calculation method based on vibration load simulation disclosed in an embodiment of the present application. The component failure risk calculation method based on vibration load simulation described in FIG. 2 can be applied in a component failure risk calculation device based on vibration load simulation. The component failure risk calculation based on vibration load simulation can be integrated in a local server or a cloud server, which is not limited in the embodiments of the present application. As shown in FIG. 2, the component failure risk calculation method based on vibration load simulation can include the following operations:

[0048] 201, input the determined PSD spectrum load into the pre-determined target software to convert the PSD spectrum load into initial time domain information through the target software.

[0049] 202、performing a signal repetition operation on the initial time domain information to obtain target time domain information, and calculating fatigue damage information based on the target time domain information.

[0050] 203、converting the fatigue damage information into target frequency domain data, and generating compressed spectrum data according to the target frequency domain data.

[0051] 204、performing a data conversion operation on the compressed spectrum data based on the predetermined target coordinates to obtain a data coordinate graph corresponding to the compressed spectrum data.

[0052] In some embodiments of the present application, the predetermined target coordinates can be double logarithmic coordinates. The double logarithmic coordinates are a special plane coordinate system, and the characteristic is that the unit length of the two coordinate axes is calculated by logarithm. The double logarithmic coordinates refer to a plane coordinate system in which both the x-axis and the y-axis are logarithmic coordinates. This means that in the double logarithmic coordinates, the values increase exponentially with the corresponding base under the condition of equal scale of the two axes.

[0053] In some embodiments of the present application, the data coordinate graph corresponding to the compressed spectrum data can include converting the compressed spectrum data to double logarithmic coordinates to take the logarithm of the abscissa and ordinate, so as to more easily observe and analyze the change range of the data. Further, the compressed spectrum data represents the energy distribution of the signal in the frequency domain.

[0054] In some embodiments of the present application, for example, the obtained compressed spectrum data represents the energy distribution of the signal in the frequency domain, and the compressed spectrum data is converted to double logarithmic coordinates to obtain the data coordinate graph corresponding to the compressed spectrum data. This coordinate system takes the logarithm of the abscissa and ordinate to more easily observe and analyze the change range of the data.

[0055] 205、determining target spectrum data corresponding to each frequency point according to the data coordinate graph and the compressed spectrum data, generating a target envelope curve according to the target spectrum data corresponding to all frequency points, and updating the compressed spectrum data based on the target envelope curve.

[0056] In some embodiments of the present application, the number of frequency points is at least one, and further, each frequency point has a corresponding maximum value, and the target spectrum data of each frequency point is determined based on the maximum value corresponding to each frequency point.

[0057] In some embodiments of the present application, the target envelope curve is a special curve described by the overall characteristics of a group of curves or surfaces. Further, the target envelope curve can be a curve obtained by connecting the maximum values corresponding to each frequency point.

[0058] 206. Input the compressed spectrum data into the predetermined vibration simulation model to obtain the model output results of the vibration simulation model, and determine the failure risk assessment results of the target component based on the model output results.

[0059] For detailed descriptions of steps 201-203 and 206 in this embodiment, please refer to the other descriptions of steps 101-104 in Embodiment 1. These descriptions will not be repeated in this embodiment.

[0060] As can be seen, the component failure risk calculation method based on vibration load simulation described in Figure 2 can perform data transformation operations on compressed spectrum data based on pre-determined target coordinates to obtain the corresponding data coordinate map. Based on the data coordinate map and compressed spectrum data, the target spectrum data corresponding to each frequency point is determined, thereby generating the target envelope curve. Based on the target envelope curve, the compressed spectrum data is updated, and the operation of inputting the compressed spectrum data into the vibration simulation model to obtain the model output results is triggered. By generating the data coordinate map corresponding to the compressed spectrum data, the distribution and characteristics of the spectrum data can be intuitively displayed, enabling analysts to more quickly understand the inherent laws and trends of the data. Based on the data coordinate map and compressed spectrum data, the target spectrum data corresponding to each frequency point can be accurately determined, which is beneficial to improving the accuracy and reliability of subsequent data analysis. Updating the target envelope curve with compressed spectral data ensures the accuracy and relevance of the data while reducing redundancy and complexity. This improves the intelligence and efficiency of subsequent data processing. Inputting the updated compressed spectral data into the vibration simulation model yields more accurate model output, allowing the updated data to more precisely reflect the spectral characteristics of the actual system. Consequently, it enhances the accuracy and reliability of simulation analysis. By automating data conversion, target spectral data extraction, envelope curve generation, and spectral data updates, the efficiency of the analysis process is significantly improved. This, in turn, enhances the accuracy and reliability of assessing the failure risk of target components, improves the intelligence and efficiency of obtaining failure assessment results, and ultimately improves the safety of the components used.

[0061] In some implementations, a signal repetition operation is performed on the initial time-domain information to obtain the target time-domain information, including:

[0062] Determine the target analysis frequency corresponding to the initial time domain information, and determine the response parameters corresponding to the initial time domain information, wherein the response parameters include one or more of the data response type parameters and data response value parameters;

[0063] Determine the signal repetition amplification factor based on the target analysis frequency and response parameters;

[0064] The signal repetition operation is performed on the initial time domain information based on the signal repetition amplification coefficient to obtain target time domain information.

[0065] In some embodiments, the determination of the target analysis frequency corresponding to the initial time domain information can include:

[0066] An analysis frequency range corresponding to the initial time domain information is determined, and a target analysis frequency corresponding to the initial time domain information is determined in the analysis frequency range; wherein the analysis frequency range can include 5Hz-200Hz, and the target analysis frequency can be 0.2Hz.

[0067] In some embodiments, the data response type parameter can be an Acceleration response type, and the data response value parameter can include an AbsMax response value. Wherein the Acceleration response type mainly refers to the acceleration reaction or response, and the Acceleration response usually refers to the speed change of the system or object when subjected to external force, that is, the size and direction of acceleration; the AbsMax response value, that is, the absolute value maximum response, is mainly used to describe the absolute value of the largest number in a group of numbers, and the AbsMax function is represented as abs_max(x), wherein x is a list of numerical values, and its function is to find the largest absolute value from the list x and return the value of the number.

[0068] In some embodiments, the signal repetition amplification coefficient can be a dynamic amplification coefficient, wherein the dynamic amplification coefficient can be 10.

[0069] In some embodiments, the signal repetition operation is performed on the initial time domain information based on the signal repetition amplification coefficient to realize 2 times of signal repetition operation on the initial time domain information to obtain target time domain information.

[0070] It can be seen that in some embodiments, the initial time domain information corresponding to the target analysis frequency can be determined, and the corresponding response parameter can be determined, the signal repetition amplification coefficient can be determined according to the target analysis frequency and the response parameter, the signal repetition operation can be performed on the initial time domain information based on the signal repetition amplification coefficient to obtain the target time domain information, the suitable analysis frequency can be selected to ensure that the signal characteristics are accurately captured, and information loss or unnecessary noise is avoided, and detailed information about the signal characteristics is provided through the data response type parameter (such as the AbsMax response value, the acceleration response type, etc.) and the data response value parameter, which are crucial for understanding and analyzing the signal, and are conducive to improving the accuracy and reliability of subsequent determination of the signal repetition amplification coefficient and obtaining of the target time domain information. According to the target analysis frequency and the response parameter, the signal repetition amplification coefficient can be determined to ensure that the key information is appropriately amplified and the non-key information is not excessively amplified during the signal repetition process, thereby improving the signal-to-noise ratio. The signal repetition operation is usually used to enhance the characteristics of the signal or to meet specific analysis requirements. The signal repetition based on the signal repetition amplification coefficient can ensure the efficiency of the processing process, reduce unnecessary calculation amount, and ensure the accuracy of the processing result, which is conducive to improving the accuracy and reliability of the signal analysis and signal amplification, and is conducive to improving the efficiency of the signal processing and the flexibility of the system, thereby being conducive to improving the accuracy and reliability of the failure risk evaluation of the target component, and being conducive to improving the intelligence and efficiency of the failure evaluation result of the target component, and further being conducive to improving the safety of the use of the component.

[0071] In some embodiments, the fatigue damage information is calculated based on the target time domain information, including:

[0072] A compression calculation coefficient of the target time domain information is determined, and a calculation operation is performed on the target time domain information based on the compression calculation coefficient to generate an impact response spectrum and a fatigue damage value;

[0073] Based on the impact response spectrum and the fatigue damage value, the fatigue damage information is generated.

[0074] In some embodiments, the compression calculation coefficient can include an SN curve slope and a signal repetition amplification number, wherein the SN curve slope can be 5 and the signal repetition amplification number can be 2 times. In some embodiments, the signal repetition number can be 2 times. Further, the SN curve slope is an important parameter for evaluating material fatigue performance and predicting life. In the SN curve, the slope region is usually located in the region above the endurance limit, showing a large downward trend. The slope of this region directly reflects the decreasing rate of the material cycle life when the stress amplitude increases. The SN curve slope represents the rate at which the material fatigue life decreases with the increase of the stress amplitude at a certain stress level. It reflects the material's ability to resist fatigue failure when subjected to cyclic stress. Generally, the larger the slope of the SN curve, the faster the fatigue life decreases with the increase of the stress amplitude, and the poorer the material's fatigue resistance. Conversely, the smaller the slope, the better the material's fatigue resistance.

[0075] In some embodiments, performing the signal repetition operation twice on the initial time domain information can obtain the damage value of the output vibration standard time domain signal with a doubled length.

[0076] In some embodiments, the impact response spectrum (IRS) describes the relationship between the maximum response of a single-degree-of-freedom vibration system under impact and the natural frequency or natural period of the vibration system. It is usually divided into two types: maximum impact response spectrum (also known as impact initial spectrum) and impact residual spectrum. Fatigue damage refers to the damage caused by the cumulative process of crack initiation, propagation, and final fracture of materials or structures under cyclic loading. Through the S-N curve (stress-life curve) of the material, combined with the actual stress level, the fatigue life and fatigue damage value can be estimated. Further, according to the S-N curve and load data, the fatigue damage value can be calculated using appropriate damage accumulation models.

[0077] It can be seen that, in some embodiments, the compression calculation coefficient of the target time domain information can be determined, and the calculation operation is performed on the target time domain information based on the compression calculation coefficient to generate the impact response spectrum and the fatigue damage value, and the fatigue damage information is generated based on the impact response spectrum and the fatigue damage value. The compression calculation coefficient of the target time domain information can be determined, and the target time domain information is processed by the coefficient, which helps to filter out redundant information and noise, highlights the key signal characteristics, and more accurately reflects the fatigue damage of the structure or material. The impact response spectrum and the fatigue damage value generated based on the processed target time domain information can help improve the accuracy and reliability of the generated fatigue damage value. By introducing the compression calculation coefficient to perform the calculation operation on the target time domain information, the fatigue damage analysis process is simplified, the analysis efficiency is improved, the intelligence and efficiency of data analysis are improved, and by comparing the fatigue damage information under different conditions, the fatigue damage mechanism and influencing factors can be further explored. The accuracy and reliability of the generated fatigue damage value are further improved, the intelligence and efficiency of the generated fatigue damage value are further improved, the efficiency of signal processing is improved, and the flexibility of the system is improved, thereby improving the accuracy and reliability of the failure risk assessment of the target component, improving the intelligence and efficiency of the failure assessment result of the target component, and further improving the safety of the use of the component.

[0078] In some embodiments, the fatigue damage information is converted into target frequency domain data, and the compression spectrum data is generated according to the target frequency domain data, including:

[0079] Based on the fatigue damage information, a target damage value corresponding to the fatigue damage information is determined, and a target frequency domain data and a data input parameter are determined based on the target damage value, wherein the data input parameter includes a data input type parameter;

[0080] Based on the data input parameter, a test time coefficient of the fatigue damage information is determined, and a compression parameter is generated based on the test time coefficient;

[0081] According to the compression parameter, a data compression operation is performed on the target frequency domain data to obtain a compressed PSD spectrum, and a compression spectrum data is generated according to the compressed PSD spectrum.

[0082] In some embodiments, the above determination of the target damage value corresponding to the fatigue damage information based on the fatigue damage information can include:

[0083] Based on the fatigue damage information, a damage value corresponding to a vibration standard time domain signal is determined, and a target damage value corresponding to the fatigue damage information is determined based on the damage value corresponding to the vibration standard time domain signal.

[0084] In some embodiments, the data input type parameter can include a Histogram input type parameter and a FDSInputType input type parameter. Histogram is a commonly used data visualization tool to show the distribution of data. When making a histogram, factors such as data distribution characteristics, sample size, group number, and group distance need to be considered. Reasonable grouping and setting of group distance are the key to making an effective histogram. FDSInputType is not a standard HTML input type, but in some specific contexts or frameworks, it may represent a certain custom input type. However, since it is not a general or standard term, I will base the type attribute of the HTML input element to summarize common input types and try to provide a hypothetical explanation or application of FDSInputType.

[0085] In some embodiments, the data output parameter can also be determined based on the target damage value, wherein the data output parameter can include an output type parameter, and the output type parameter can include a PSD type, and the PSD type can include a Lalanne type, and the Lalanne type mainly refers to a time management method established by Claude Lalanne, a French efficiency expert.

[0086] In some embodiments, the target frequency domain data can be obtained by performing a conversion operation on the target damage value by a test synthesis module.

[0087] In some embodiments, the test time coefficient can be 21 hours.

[0088] In some embodiments, the above performing a data compression operation on the target frequency domain data according to the compression parameter to obtain a compressed PSD spectrum, and generating compressed frequency spectrum data according to the compressed PSD spectrum, can include:

[0089] determining a compression time parameter, a dynamic amplification parameter, and an SN curve slope according to the compression parameter, and performing a calculation compression operation on the target frequency domain data based on the compression time parameter, the dynamic amplification parameter, and the SN curve slope to obtain a compressed PSD spectrum, and determining the compressed PSD spectrum as the compressed frequency spectrum data;

[0090] wherein the compression time parameter includes 21 hours, the dynamic amplification parameter is 10, and the SN curve slope is 5.

[0091] In some embodiments, for example, the damage value is converted into the compression spectrum under 21 hours using the Test Synthesis to evaluate the failure risk of each component under the GB31467.3_2015 vibration standard for twice the time length in the simulation; further, the Test Synthesis module in the Ncode software can be used to convert the damage value (such as vibration stress, strain, etc.) into the corresponding frequency domain data, such as the PSD (power spectral density) spectrum, as the vibration load input in the simulation model.

[0092] It can be seen that, in some embodiments, the corresponding target damage value can be determined based on the fatigue damage information, and the target frequency domain data and the data input parameter can be determined based on the target damage value, the test time coefficient of the fatigue damage information can be determined based on the data input parameter and the compression parameter can be generated, the data compression operation is performed on the target frequency domain data based on the compression parameter to obtain the compressed PSD spectrum and generate the compressed spectrum data, by converting the fatigue damage information from the original time domain or other non-frequency domain form to the target frequency domain data, the distribution and change of the damage information at different frequencies can be more intuitively analyzed, at the same time, the generation of the compressed spectrum data reduces the data amount and improves the efficiency of subsequent analysis and processing, the target frequency domain data and the compressed spectrum data provide a more convenient and accurate means for identification and evaluation of fatigue damage, in the frequency domain, different damage modes may produce different frequency response characteristics, which makes the analysis based on the frequency domain data more intuitive and reliable, the generation of the compressed PSD spectrum reduces the redundant information of the data, significantly reduces the storage demand of the data, can effectively save the storage space and reduce the storage cost, further helps to improve the efficiency of data processing, the frequency domain data and the compressed spectrum data can be visually displayed in the form of charts, images, etc., making the distribution and change trend of the fatigue damage more intuitive and clear, and further improving the intelligence and efficiency of generating the fatigue damage value, and helping to improve the efficiency of processing the signal and the flexibility of the system, thereby helping to improve the accuracy and reliability of evaluating the failure risk of the target component, and helping to improve the intelligence and efficiency of obtaining the failure evaluation result of the target component, and further helping to improve the safety of using the components.

[0093] In some embodiments, according to the target spectrum data corresponding to all frequency points, a target envelope curve is generated, including:

[0094] For each frequency point, according to the data coordinate graph and the compressed spectrum data, a peak point matched with the probability point is determined in the data coordinate graph;

[0095] According to each probability point and the peak point of each probability point, a target envelope curve is generated, wherein the target envelope curve at least includes a curve connecting the peak points of all probability points.

[0096] In some embodiments, the peak point matched with each probability point can be the maximum value corresponding to the frequency point in the double logarithmic coordinate. Further in some embodiments, the maximum value of the compressed spectrum data in the double logarithmic coordinate means finding the maximum value at each frequency point, and these maximum values will constitute the maximum envelope curve.

[0097] In some embodiments, before the method of generating the target envelope curve according to each probability point and the peak point of each probability point is executed, the method can further include:

[0098] determining the deviation value corresponding to each probability point, judging whether there is a deviation point in all probability points based on the deviation values corresponding to all probability points, when it is judged that there is a deviation point in all probability points, removing all deviation points from the probability points, updating all probability points, and triggering the operation of generating the target envelope curve according to each probability point and the peak point of each probability point.

[0099] In some embodiments, judging whether there is a deviation point in all probability points can include:

[0100] judging whether there is a target deviation value greater than or equal to the preset deviation threshold value in the deviation values corresponding to all probability points based on the deviation values corresponding to all probability points;

[0101] when it is judged that there is a target deviation value greater than or equal to the preset deviation threshold value in the deviation values corresponding to all probability points, determining that there is a deviation point in all probability points; when it is judged that there is no target deviation value greater than or equal to the preset deviation threshold value in the deviation values corresponding to all probability points, determining that there is no deviation point in all probability points.

[0102] In this way, it can be judged whether there is a deviation point in all probability points based on the deviation value of each probability point, and if there is, all deviation points are removed, and the target envelope curve is generated based on the probability points after removing all deviation points and the peak point of each probability point, which is beneficial to further improve the accuracy and reliability of the generated target envelope curve, thereby being beneficial to improve the accuracy and reliability of the evaluation of the failure risk of the target component, and being beneficial to improve the intelligence and efficiency of the failure evaluation result of the target component.

[0103] In some embodiments, for example, the compressed spectrum data representing the energy distribution of the signal in the frequency domain is obtained, and the compressed spectrum data is converted to a double logarithmic coordinate system. This coordinate system takes the horizontal and vertical coordinates as logarithms, so that the range of data changes can be more easily observed and analyzed. The maximum value of the compressed spectrum data is taken in the double logarithmic coordinate system. This means finding the maximum value at each frequency point, which will form the maximum outer envelope curve. By connecting the maximum values at each frequency point in the compressed spectrum data, the maximum outer envelope curve is obtained, which represents the maximum energy density variation in a given frequency range.

[0104] As can be seen, in some embodiments, the peak points matched with each probability point can be determined according to the data coordinate diagram and the compressed spectrum data, and the target envelope curve can be generated according to each probability point and its corresponding peak point. The compressed spectrum curve can be simplified to fewer points by taking the maximum outer envelope, which can simplify the data processing and analysis process, reduce the computational burden by taking the maximum outer envelope with fewer points, improve the efficiency of data processing, and retain the key information in the compressed spectrum by taking the maximum outer envelope curve with fewer points, which represents the maximum energy density of the system at different frequencies, so the intensity characteristics of the compressed spectrum can still be accurately represented. The method of accurately representing the intensity of the PSD spectrum by taking the maximum envelope is to find the peak points on the curve in the double logarithmic coordinate system and connect these points to form the maximum outer envelope curve. These peak points represent the maximum energy density in a given frequency range, so they can accurately represent the intensity characteristics of the PSD spectrum. If there are abnormal or special frequency bands in the spectrum, these frequency bands will usually appear as significant protrusions or depressions on the target envelope curve. By observing and comparing the shape and characteristics of the target envelope curve, these abnormal frequency bands can be more easily detected and further analyzed and processed. The generation method of the target envelope curve has certain flexibility, which can be adjusted and optimized according to specific application requirements and data characteristics. For example, different shapes and characteristics of the target envelope curve can be generated by changing the selection criteria of the peak points and adjusting the smoothness of the curve, which is beneficial to improve the efficiency of signal processing and the flexibility of the system, thereby improving the accuracy and reliability of the failure risk assessment of the target component, and improving the intelligence and efficiency of the failure assessment result of the target component, thereby improving the safety of the use of the component.

[0105] In some embodiments, a data conversion operation is performed on the compressed spectrum data based on the predetermined target coordinate to obtain a data coordinate diagram corresponding to the compressed spectrum data, including:

[0106] The compressed spectrum data is converted into coordinates matching the target coordinates based on the predetermined target coordinates, to obtain coordinate distribution information corresponding to the compressed spectrum data;

[0107] Based on the coordinate analysis information corresponding to the compressed spectrum data, a data coordinate graph corresponding to the compressed spectrum data is generated.

[0108] In some embodiments, the predetermined target coordinates can be double logarithmic coordinates, where the double logarithmic coordinates are a special plane coordinate system, and the characteristic is that the unit length of the two coordinate axes is calculated after logarithm. Double logarithmic coordinates refer to a plane coordinate system in which both coordinate axes (x-axis and y-axis) are logarithmic coordinates. This means that in double logarithmic coordinates, the values increase exponentially with the corresponding base under the condition of equal scale of the two axes.

[0109] In some embodiments, converting the compressed spectrum data into coordinates matching the target coordinates to obtain the coordinate distribution information corresponding to the compressed spectrum data can be converting the compressed spectrum data into double logarithmic coordinates to obtain the data situation of the compressed spectrum data in the double logarithmic coordinates, and then taking the logarithm of the abscissa and the ordinate through the double logarithmic coordinates to more easily observe and analyze the change range of the data.

[0110] In some embodiments, the above generating the data coordinate graph corresponding to the compressed spectrum data based on the coordinate analysis information corresponding to the compressed spectrum data can include determining each target point of the compressed spectrum data on the target coordinates based on the coordinate analysis information corresponding to the compressed spectrum data, and generating the data coordinate graph corresponding to the compressed spectrum data based on each target point.

[0111] It can be seen that, in some embodiments, the compressed spectrum data can be converted into coordinate distribution information matching the target coordinates based on the predetermined target coordinates, and the data coordinate graph corresponding to the compressed spectrum data can be generated based on the coordinate distribution information corresponding to the compressed spectrum data. The visualization of the spectrum data can be realized by converting the compressed spectrum data into coordinate distribution information matching the predetermined target coordinates and generating the data coordinate graph accordingly. The features and trends of the spectrum data can be more intuitive and easy to understand, which helps to quickly grasp the overall situation of the data. Moreover, the accuracy and reliability of the conversion of the compressed spectrum data can be ensured by combining the target coordinates, so that the generated coordinate distribution information can accurately reflect the actual features of the compressed spectrum data. The features and trends of the compressed spectrum data can be displayed through the intuitive data coordinate graph, so that the user can understand the data more quickly, thereby improving the decision-making efficiency. Therefore, the accuracy and reliability of the generated data coordinate graph can be improved, and the intelligence and efficiency of the generated data coordinate graph can be improved. Moreover, the ability and convenience of data visualization can be improved, the efficiency of signal processing can be improved, and the flexibility of the system can be improved, thereby improving the accuracy and reliability of the evaluation of the failure risk of the target component, improving the intelligence and efficiency of the failure evaluation result of the target component, and further improving the safety of the use of the component.

[0112] In some embodiments, the failure risk evaluation result of the target component is determined based on the model output result, including:

[0113] Based on the model output result, the stress information of the target component under the preset target vibration standard is determined.

[0114] Based on the stress information of the target component, the failure risk evaluation result of the target component is determined.

[0115] In some embodiments, the stress information of the target component can include one or more of tensile stress information, compressive stress information, shear stress information, and bending stress information, which is not limited in the embodiments of the present application. Further, when the part working load exceeds the maximum allowable stress, stress concentration and overload phenomenon may occur, and residual stress may cause early failure of the automobile part, affecting the service life and working performance of the part.

[0116] In some embodiments, the preset target vibration standard can be GB31467 vibration standard.

[0117] In some embodiments, for example, the type of stress to which the target component is subjected, such as tensile stress, compressive stress, shear stress, etc., is determined by measuring or calculating the stress magnitude of the target component under different operating conditions, analyzing the stress distribution of the target component, especially the high stress area and the low stress area, using the stress data to analyze the stress distribution of the component under different operating conditions, identifying the stress concentration area and the potential failure point, based on the stress analysis results, determining the failure mode of the target component that may occur, such as fatigue fracture, creep failure, etc., combining the material properties, manufacturing process, etc. of the component, analyzing the potential causes of failure, such as material fatigue, stress corrosion, etc., evaluating the impact of failure on the performance and safety of the component, including loss of function, performance degradation, safety hazards, etc., according to the severity and frequency of failure, combined with the stress analysis results, determining the failure risk level of the target component. Risk matrix and other tools can be used for quantitative evaluation, and the failure risk can be divided into different levels such as high risk, medium risk, low risk, etc.

[0118] As can be seen, in some embodiments, the stress information of the target component under the preset target vibration standard can be determined based on the model output results, and the failure risk evaluation results of the target component can be determined based on the stress information of the target component. The stress information of the target component under a specific vibration standard can be directly obtained through the model output, avoiding the measurement errors and uncertainties that may exist in traditional methods, and the accurate stress information can more accurately reflect the stress condition of the component under actual working conditions, thereby improving the accuracy of failure risk evaluation. Through the automated and digitized method, the time for manual operation and data processing is greatly reduced, the evaluation efficiency and intelligence are improved, and through real-time or rapid acquisition of stress information, the failure risk evaluation can be performed more timely, which helps to take preventive measures in time. The stress information under various vibration standards and different working conditions can be considered, thereby more comprehensively evaluating the failure risk of the target component, and the simultaneous evaluation of multiple components can be implemented, which is beneficial to improving the comprehensiveness and efficiency of evaluating the failure risk of the target component. Through the analysis of stress information under different vibration standards, the failure mode and risk level of the target component under different working conditions can be predicted, which is beneficial to improving the intelligence and efficiency of evaluating the failure risk of the target component. Accurate failure risk evaluation helps to discover potential safety hazards in time and take corresponding measures to eliminate or reduce risks, which is beneficial to improving the safety and stability of the target component, thereby improving the accuracy and reliability of evaluating the failure risk of the target component, and improving the intelligence and efficiency of obtaining the failure evaluation results of the target component, which is further beneficial to improving the safety of using the components.

[0119] Example Three

[0120] Please refer to FIG. 3, which is a structural schematic diagram of a component failure risk calculation device based on vibration load simulation according to an embodiment of the present application. As shown in FIG. 3, the component failure risk calculation device based on vibration load simulation can include:

[0121] The conversion module 301 is configured to input the determined PSD spectral load into a predetermined target software, so as to convert the PSD spectral load into initial time domain information by the target software.

[0122] The signal repetition module 302 is configured to perform a signal repetition operation on the initial time domain information, so as to obtain target time domain information.

[0123] The calculation module 303 is configured to calculate fatigue damage information based on the target time domain information.

[0124] The generation module 304 is further configured to convert the fatigue damage information into target frequency domain data, and generate compressed frequency spectrum data according to the target frequency domain data.

[0125] The input module 305 is configured to input the compressed frequency spectrum data into a predetermined vibration simulation model, so as to obtain a model output result of the vibration simulation model.

[0126] The determination module 306 is configured to determine a failure risk evaluation result of a target component based on the model output result.

[0127] It can be seen that the device described in Figure 3 can input the PSD spectral load into the target software to convert the PSD spectral load into initial time domain information, perform signal repetition operation on the initial time domain information to obtain target time domain information, and then calculate fatigue damage information, convert the fatigue damage information into target frequency domain data and generate compressed spectrum data, input the compressed spectrum data into the vibration simulation model to obtain the model output result and determine the failure risk assessment result of the target component according to the model output result. By converting the PSD spectral load into time domain information, the vibration situation that the target component may encounter in the actual working environment can be accurately simulated, which can help to more accurately assess the performance and durability of the target component under real conditions. By performing signal repetition operation on the initial time domain information to obtain target time domain information, and calculating fatigue damage information based on this information, the repeated stress effect of the component in the vibration environment can be considered, which can help to improve the accuracy and reliability of the fatigue damage assessment of the target component. By converting the fatigue damage information into target frequency domain data and generating compressed spectrum data, the redundancy and storage requirements of the data can be reduced, and the compressed spectrum data is easier to process in the subsequent vibration simulation model, improving the analysis efficiency and data processing speed. By inputting the compressed spectrum data into the pre-determined vibration simulation model, the model output result can be obtained, and the dynamic response of the component in the vibration environment can be simulated through the vibration simulation model, thereby predicting its performance and potential failure risk, which can help to improve the accuracy and reliability of the failure risk assessment of the target component, and can help to improve the intelligence and efficiency of obtaining the failure assessment result of the target component, and further help to improve the safety of using the components.

[0128] In some embodiments, as shown in Figure 4, the conversion module 301 is also used to perform data conversion operation on the compressed spectrum data based on the pre-determined target coordinate to obtain a data coordinate graph corresponding to the compressed spectrum data after the generation module 304 converts the fatigue damage information into target frequency domain data and generates compressed spectrum data according to the target frequency domain data;

[0129] The determination module 306 is also used to determine the target spectrum data corresponding to each frequency point according to the data coordinate graph and the compressed spectrum data;

[0130] The generation module 304 is also used to generate a target envelope curve according to the target spectrum data corresponding to all frequency points;

[0131] The device further comprises:

[0132] The update module 307 is used to update the compressed spectrum data based on the target envelope curve, and trigger the input module 305 to perform the operation of inputting the compressed spectrum data into the pre-determined vibration simulation model to obtain the model output result of the vibration simulation model.

[0133] It can be seen that the device described in Figure 4 can perform data conversion operation on the compressed frequency spectrum data based on the predetermined target coordinates to obtain the corresponding data coordinate graph, determine the target frequency spectrum data corresponding to each frequency point according to the data coordinate graph and the compressed frequency spectrum data, and generate the target envelope curve, update the compressed frequency spectrum data based on the target envelope curve, and trigger the operation of inputting the compressed frequency spectrum data into the vibration simulation model to obtain the model output result. By generating the data coordinate graph corresponding to the compressed frequency spectrum data, the distribution and characteristics of the frequency spectrum data can be intuitively displayed, so that the analyst can more quickly understand the internal law and trend of the data. According to the data coordinate graph and the compressed frequency spectrum data, the target frequency spectrum data corresponding to each frequency point can be accurately determined, which is beneficial to improve the accuracy and reliability of subsequent female and data analysis. And updating the compressed frequency spectrum data based on the target envelope curve can ensure that the accuracy and relevance of the data are maintained, while reducing the redundancy and complexity of the data, thereby facilitating the intelligence and efficiency of subsequent data processing. Inputting the updated compressed frequency spectrum data into the vibration simulation model can obtain more accurate model output results, which can improve the updated data to more accurately reflect the frequency spectrum characteristics of the actual system, thereby improving the accuracy and reliability of the simulation analysis. By automatically performing data conversion, target frequency spectrum data extraction, envelope curve generation, and frequency spectrum data updating operations, the working efficiency of the analysis process can be greatly improved, thereby facilitating the accuracy and reliability of the evaluation of the failure risk of the target component, and facilitating the intelligence and efficiency of obtaining the failure evaluation result of the target component, thereby also facilitating the safety of using the component.

[0134] In some embodiments, as shown in Figure 4, the signal repetition module 302 performs a signal repetition operation on the initial time domain information to obtain the target time domain information in the following specific manner:

[0135] Determine the target analysis frequency corresponding to the initial time domain information, and determine the response parameter corresponding to the initial time domain information, wherein the response parameter includes one or more of a data response type parameter and a data response value parameter;

[0136] Determine the signal repetition amplification coefficient according to the target analysis frequency and the response parameter;

[0137] Perform a signal repetition operation on the initial time domain information based on the signal repetition amplification coefficient to obtain the target time domain information.

[0138] It can be seen that the device described in Figure 4 can determine the target analysis frequency corresponding to the initial time domain information and determine the corresponding response parameter, determine the signal repetition amplification coefficient according to the target analysis frequency and the response parameter, perform the signal repetition operation on the initial time domain information based on the signal repetition amplification coefficient to obtain the target time domain information, can select the appropriate analysis frequency to ensure that the signal characteristics are accurately captured, avoid information loss or introduce unnecessary noise, and provide detailed information about the signal characteristics through the data response type parameter (such as AbsMax response value, acceleration response type, etc.) and the data response value parameter. These parameters are crucial for understanding and analyzing the signal, which is conducive to improving the accuracy and reliability of subsequent determination of the signal repetition amplification coefficient and obtaining the target time domain information. According to the target analysis frequency and the response parameter, the signal repetition amplification coefficient is determined, which can ensure that key information is properly amplified during the signal repetition process, while non-key information is not excessively amplified, thereby improving the signal-to-noise ratio. The signal repetition operation is usually used to enhance the characteristics of the signal or meet specific analysis requirements. Based on the signal repetition amplification coefficient, the signal repetition can ensure the efficiency of the processing process, reduce unnecessary calculation amount, while ensuring the accuracy of the processing result, which is conducive to improving the accuracy and reliability of the signal analysis and signal amplification, and is conducive to improving the efficiency of the signal processing and the flexibility of the system, thereby being conducive to improving the accuracy and reliability of the failure risk assessment of the target component, and being conducive to improving the intelligence and efficiency of the failure assessment result of the target component, and further being conducive to improving the safety of using the components.

[0139] In some embodiments, as shown in Figure 4, the specific way in which the calculation module 303 calculates the fatigue damage information based on the target time domain information includes:

[0140] determining a compression calculation coefficient of the target time domain information, and performing a calculation operation on the target time domain information based on the compression calculation coefficient to generate an impact response spectrum and a fatigue damage value;

[0141] generating fatigue damage information based on the impact response spectrum and the fatigue damage value.

[0142] It can be seen that the device described in Figure 4 can determine the compression calculation coefficient of the target time domain information, perform calculation operation on the target time domain information based on the compression calculation coefficient to generate the impact response spectrum and the fatigue damage value, generate the fatigue damage information based on the impact response spectrum and the fatigue damage value, and improve the determination of the compression calculation coefficient of the target time domain information and the processing of the target time domain information through the coefficient, which helps to filter out redundant information and noise, highlights the key signal characteristics, and more accurately reflects the fatigue damage of the structure or material. The impact response spectrum and the fatigue damage value generated based on the processed target time domain information can improve the accuracy and reliability of the generated fatigue damage value. By introducing the compression calculation coefficient to perform calculation operation on the target time domain information, the fatigue damage analysis process is simplified, the analysis efficiency is improved, the intelligence and efficiency of data analysis are improved, and by comparing the fatigue damage information under different conditions, the fatigue damage mechanism and influencing factors can be further explored, which can further improve the accuracy and reliability of the generated fatigue damage value, the intelligence and efficiency of the generated fatigue damage value, and the efficiency and flexibility of signal processing, thereby improving the accuracy and reliability of the failure risk assessment of the target component, the intelligence and efficiency of the failure assessment result of the target component, and the safety of the use of the component.

[0143] In some embodiments, as shown in Figure 4, the generation module 304 converts the fatigue damage information into target frequency domain data, and the specific way of generating compressed frequency spectrum data according to the target frequency domain data includes:

[0144] Based on the fatigue damage information, determine the target damage value corresponding to the fatigue damage information, and determine the target frequency domain data and the input parameter based on the target damage value, wherein the data input parameter includes a data input type parameter;

[0145] Based on the data input parameter, determine the test time coefficient of the fatigue damage information, and generate the compression parameter based on the test time coefficient;

[0146] According to the compression parameter, perform data compression operation on the target frequency domain data to obtain compressed PSD spectrum, and generate compressed frequency spectrum data according to the compressed PSD spectrum.

[0147] It can be seen that the device described in Figure 4 can determine the corresponding target damage value based on the fatigue damage information, determine the target frequency domain data and the data input parameter based on the target damage value, determine the test time coefficient of the fatigue damage information based on the data input parameter and generate the compression parameter, perform the data compression operation on the target frequency domain data based on the compression parameter to obtain the compressed PSD spectrum and generate the compressed frequency spectrum data, and convert the fatigue damage information from the original time domain or other non-frequency domain form to the target frequency domain data, so that the distribution and change of the damage information at different frequencies can be more directly analyzed, at the same time, the generation of the compressed frequency spectrum data reduces the data amount and improves the efficiency of subsequent analysis and processing, the target frequency domain data and the compressed frequency spectrum data provide a more convenient and accurate means for the identification and evaluation of fatigue damage, in the frequency domain, different damage modes can produce different frequency response characteristics, which makes the analysis based on the frequency domain data more intuitive and reliable, the generation of the compressed PSD spectrum reduces the redundant information of the data and significantly reduces the storage requirement of the data, which can effectively save the storage space and reduce the storage cost, further helps to improve the efficiency of data processing, the frequency domain data and the compressed frequency spectrum data can be visually displayed in the form of charts, images and the like, so that the distribution and change trend of the fatigue damage are more intuitive and clear, and the intelligence and efficiency of generating the fatigue damage value can be further improved, and the efficiency of processing the signal and the flexibility of the system are improved, so as to improve the accuracy and reliability of the failure risk evaluation of the target component, and improve the intelligence and efficiency of the failure evaluation result of the target component, and further improve the safety of the use of the component.

[0148] In some embodiments, as shown in Figure 4, the specific way of generating the target envelope curve by the generation module 304 according to the target frequency spectrum data corresponding to all frequency points includes:

[0149] For each frequency point, according to the data coordinate graph and the compressed frequency spectrum data, the peak point matched with the probability point is determined in the data coordinate graph;

[0150] According to each probability point and the peak point of each probability point, the target envelope curve is generated, wherein the target envelope curve at least includes the curve obtained by connecting the peak points of all probability points.

[0151] It can be seen that the device described in Figure 4 can determine the peak points matched with each probability point according to the data coordinate graph and the compressed spectrum data, and generate the target envelope curve according to each probability point and its corresponding peak point. By taking the maximum envelope, the compressed spectrum curve can be simplified to fewer points, which can simplify the data processing and analysis process, reduce the computational burden by taking the maximum envelope with fewer points, improve the efficiency of data processing, and retain the key information in the compressed spectrum by taking the maximum envelope curve with fewer points. These points represent the maximum energy density of the system at different frequencies, so they can still accurately represent the intensity characteristics of the compressed spectrum. The method of taking the maximum envelope to accurately represent the PSD spectrum intensity is to find the peak points on the curve in the double logarithmic coordinates and connect these points to form the maximum envelope curve. These peak points represent the maximum energy density in a given frequency range, so they can accurately represent the intensity characteristics of the PSD spectrum. If there are abnormal or special frequency bands in the spectrum, these frequency bands will usually appear as significant protrusions or depressions on the target envelope curve. By observing and comparing the shape and characteristics of the target envelope curve, it is easier to detect these abnormal frequency bands and further analyze and process them. The generation method of the target envelope curve has certain flexibility, which can be adjusted and optimized according to the specific application requirements and data characteristics. For example, different shapes and characteristics of the target envelope curve can be generated by changing the selection criteria of the peak points, adjusting the smoothness of the curve, etc., which is beneficial to improve the efficiency of signal processing and the flexibility of the system, thereby improving the accuracy and reliability of the failure risk assessment of the target component, and improving the intelligence and efficiency of the failure assessment result of the target component. Therefore, it is also beneficial to improve the safety of using components.

[0152] In some embodiments, as shown in Figure 4, the conversion module 301 performs a data conversion operation on the compressed spectrum data based on the pre-determined target coordinates to obtain the specific manner of the data coordinate graph corresponding to the compressed spectrum data, which includes:

[0153] Based on the pre-determined target coordinates, the compressed spectrum data is converted into coordinates matched with the target coordinates to obtain the coordinate distribution information corresponding to the compressed spectrum data;

[0154] Based on the coordinate analysis information corresponding to the compressed spectrum data, the data coordinate graph corresponding to the compressed spectrum data is generated.

[0155] It can be seen that the device described in Figure 4 can convert the compressed spectrum data into the corresponding coordinate distribution information based on the predetermined target coordinates, and generate the data coordinate graph corresponding to the compressed spectrum data based on the coordinate distribution information corresponding to the compressed spectrum data. By converting the compressed spectrum data into the coordinate distribution information matching the predetermined target coordinates and generating the data coordinate graph accordingly, the visualization expression of the spectrum data is realized, which can make the features and trends of the spectrum data more intuitive and easy to understand, helping to quickly grasp the overall situation of the data. Moreover, by combining the target coordinates, the accuracy and reliability of the conversion of the compressed spectrum data can be ensured, so that the generated coordinate distribution information can accurately reflect the actual features of the compressed spectrum data. The features and trends of the compressed spectrum data are displayed through the intuitive data coordinate graph, so that the user can understand the data more quickly, thereby improving the decision-making efficiency, thereby helping to improve the accuracy and reliability of the generated data coordinate graph, and helping to improve the intelligence and efficiency of the generated data coordinate graph. Moreover, it can also help to improve the ability and convenience of data visualization, help to improve the efficiency of signal processing and the flexibility of the system, thereby helping to improve the accuracy and reliability of the evaluation of the failure risk of the target component, and helping to improve the intelligence and efficiency of the failure evaluation result of the target component. Furthermore, it can also help to improve the safety of using the components.

[0156] In some embodiments, as shown in Figure 4, the specific manner in which the determination module 306 determines the failure risk evaluation result of the target component based on the model output result includes:

[0157] Based on the model output result, the stress information of the target component under the preset target vibration standard is determined.

[0158] Based on the stress information of the target component, the failure risk evaluation result of the target component is determined.

[0159] It can be seen that the device described in FIG. 4 can determine the stress information of the target component under the preset target vibration standard based on the model output result, and determine the failure risk assessment result of the target component based on the stress information of the target component. The stress information of the target component under a specific vibration standard can be directly obtained through the model output, avoiding the measurement errors and uncertainties that may exist in traditional methods. The accurate stress information can more accurately reflect the stress condition of the component under actual working conditions, thereby improving the accuracy of failure risk assessment. Through the automated and digitized method, the time for manual operation and data processing is greatly reduced, and the evaluation efficiency and evaluation intelligence are improved. Through real-time or rapid acquisition of stress information, the failure risk assessment can be performed more timely, which helps to take preventive measures in time. The stress information under various vibration standards and different working conditions can be considered, thereby more comprehensively evaluating the failure risk of the target component. Through simultaneous evaluation of multiple components, the comprehensiveness and efficiency of evaluating the failure risk of the target component are improved. Through analysis of stress information under different vibration standards, the failure mode and risk level of the target component under different working conditions can be predicted, which helps to improve the intelligence and efficiency of evaluating the failure risk of the target component. Accurate failure risk assessment helps to discover potential safety hazards in time and take corresponding measures to eliminate or reduce risks, which helps to improve the safety and stability of the target component, thereby improving the accuracy and reliability of evaluating the failure risk of the target component, and improving the intelligence and efficiency of obtaining the failure assessment result of the target component, and further improving the safety of using the component.

[0160] Embodiment Four

[0161] Referring to FIG. 5, FIG. 5 is a structural schematic diagram of another component failure risk calculation device based on vibration load simulation disclosed by the embodiments of the present application. As shown in FIG. 5, the component failure risk calculation device based on vibration load simulation can include:

[0162] a memory 401 storing executable program codes;

[0163] a processor 402 coupled with the memory 401;

[0164] The processor 402 invokes the executable program codes stored in the memory 401 to execute the steps of the component failure risk calculation method based on vibration load simulation described in the first embodiment or the second embodiment of the present application.

[0165] Embodiment Five

[0166] The embodiment of the present application discloses a computer storage medium, the computer storage medium stores computer instructions, when the computer instructions are invoked, steps in the component failure risk calculation method based on vibration load simulation described in the embodiment one or the embodiment two are executed.

[0167] Embodiment six

[0168] The embodiment of the present application discloses a computer program product, the computer program product includes a non-transitory computer readable storage medium storing a computer program, and the computer program is operable to make a computer execute steps in the component failure risk calculation method based on vibration load simulation described in the embodiment one or the embodiment two.

[0169] The device embodiment described above is only schematic, wherein the modules described as separate components can or can not be physically separate, and the components displayed as modules can or can not be physical modules, i.e., can be located in one place or distributed on multiple network modules. Part or all of the modules can be selected to achieve the purpose of the embodiment scheme according to actual needs. Those skilled in the art can understand and implement without creative labor.

[0170] Those skilled in the art can clearly understand the implementation of the various embodiments by means of software and the necessary general hardware platform through the specific description of the above embodiments, and of course, the various embodiments can also be implemented by hardware. Based on such understanding, the above technical solutions, essentially or in other words, the part of the prior art that makes a contribution can be embodied in the form of a software product. The computer software product can be stored in a computer readable storage medium, and the storage medium includes a read-only memory (ROM), a random access memory (RAM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), a one-time programmable read-only memory (OTPROM), an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disk storage, a magnetic disk storage, a magnetic tape storage, or any other computer readable medium that can be used to carry or store data.

Claims

1. A component failure risk calculation method based on vibration load simulation, the method comprising: inputting the determined PSD spectral load into a predetermined target software to convert the PSD spectral load into initial time domain information by the target software; performing a signal repetition operation on the initial time domain information to obtain target time domain information, and calculating fatigue damage information based on the target time domain information; converting the fatigue damage information into target frequency domain data, and generating compressed frequency spectrum data according to the target frequency domain data; inputting the compressed frequency spectrum data into a predetermined vibration simulation model to obtain a model output result of the vibration simulation model, and determining a failure risk evaluation result of a target component based on the model output result.

2. The component failure risk calculation method based on vibration load simulation according to claim 1, wherein, After the fatigue damage information is converted into target frequency domain data, and the compressed frequency spectrum data is generated according to the target frequency domain data, the method further comprises: performing a data conversion operation on the compressed frequency spectrum data based on a predetermined target coordinate to obtain a data coordinate graph corresponding to the compressed frequency spectrum data; determining target spectral data corresponding to each frequency point according to the data coordinate graph and the compressed frequency spectrum data, generating a target envelope curve according to the target spectral data corresponding to all the frequency points, and updating the compressed frequency spectrum data based on the target envelope curve, and triggering the operation of inputting the compressed frequency spectrum data into a predetermined vibration simulation model to obtain a model output result of the vibration simulation model.

3. The component failure risk calculation method based on vibration load simulation according to claim 1, wherein, The operation of performing a signal repetition operation on the initial time domain information to obtain target time domain information comprises: determining a target analysis frequency corresponding to the initial time domain information, and determining a response parameter corresponding to the initial time domain information, wherein the response parameter comprises one or more of a data response type parameter, a data response value parameter; determining a signal repetition amplification coefficient according to the target analysis frequency and the response parameter; performing a signal repetition operation on the initial time domain information based on the signal repetition amplification coefficient to obtain target time domain information.

4. The component failure risk calculation method based on vibration load simulation according to claim 1, wherein, The operation of calculating fatigue damage information based on the target time domain information comprises: determining a compression calculation coefficient of the target time domain information, and performing a calculation operation on the target time domain information based on the compression calculation coefficient to generate an impact response spectrum and a fatigue damage value; generating fatigue damage information based on the impact response spectrum and the fatigue damage value.

5. The component failure risk calculation method based on vibration load simulation according to Claim 1, wherein, The operation of converting the fatigue damage information into target frequency domain data, and generating compressed frequency spectrum data according to the target frequency domain data comprises: determining a target damage value corresponding to the fatigue damage information based on the fatigue damage information, and determining target frequency domain data and data input parameters based on the target damage value, wherein the data input parameters comprise data input type parameters; determining a test time coefficient of the fatigue damage information based on the data input parameters, and generating a compression parameter based on the test time coefficient; performing a data compression operation on the target frequency domain data according to the compression parameter to obtain a compressed PSD spectrum, and generating compressed frequency spectrum data according to the compressed PSD spectrum.

6. The component failure risk calculation method based on vibration load simulation according to claim 1, wherein, The target envelope curve is generated according to the target spectrum data corresponding to all the frequency points, and the target envelope curve comprises: For each of the frequency points, a peak point matched with the probability point is determined in the data coordinate graph according to the data coordinate graph and the compressed spectrum data; A target envelope curve is generated according to each of the probability points and the peak point of each of the probability points, wherein the target envelope curve at least comprises a curve connecting the peak points of all the probability points.

7. The component failure risk calculation method based on vibration load simulation according to claim 2, wherein, The data conversion operation is performed on the compressed spectrum data based on the target coordinate determined in advance to obtain a data coordinate graph corresponding to the compressed spectrum data, and the data conversion operation comprises: The compressed spectrum data is converted into a coordinate matched with the target coordinate based on the target coordinate determined in advance to obtain coordinate distribution information corresponding to the compressed spectrum data; The data coordinate graph corresponding to the compressed spectrum data is generated based on the coordinate analysis information corresponding to the compressed spectrum data.

8. The component failure risk calculation method based on vibration load simulation according to Claim 1, wherein, The failure risk assessment result of the target component is determined based on the model output result, and the failure risk assessment result of the target component comprises: The stress information of the target component under a preset target vibration standard is determined based on the model output result; The failure risk assessment result of the target component is determined based on the stress information of the target component.

9. A device for calculating a component failure risk based on a vibration load simulation, wherein, The device comprises: a memory storing executable program codes; a processor coupled with the memory; the processor invokes the executable program codes stored in the memory to execute the component failure risk calculation method based on vibration load simulation according to any one of claims 1-8.

10. A computer storage medium, wherein, The computer storage medium stores computer instructions, and the computer instructions are set to execute the component failure risk calculation method based on vibration load simulation according to any one of claims 1-8 when invoked.

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