A method, system and storage medium for predicting the life of a seal ring

Abstract

The application belongs to the technical field of seal ring life prediction, in particular to a seal ring life prediction method and system and a storage medium, comprising the following steps: obtaining seal ring material information, calculating the theoretical prediction life of the seal ring based on the seal ring material information, and synchronously storing it to the cloud; calculating the corrected prediction life of the seal ring based on the online detection data and the theoretical prediction life, and synchronously updating it to the cloud; performing aging test, calculating the limit prediction life of the seal ring based on the corrected prediction life, and synchronously updating it to the cloud; verifying the limit prediction life of the seal ring under the limit environment based on the aging test, writing / updating the data of each stage to the cloud time series database in real time, and constructing the material-production-aging data chain, so as to realize efficient prediction of the seal ring life, avoid low-life products from flowing into the market, and solve the problems of complicated after-sales and high after-sales cost of products.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of seal ring life prediction, and in particular to a seal ring life prediction method, system and storage medium. BACKGROUND

[0002] Seal rings are general technologies required in various fields and have been widely used in various engines, mainly for sealing oil, gas and other media. Since the working environment of engine accessories is often harsh, such as high temperature and high load, and in the non-working state, it is also subjected to the long-term effects of storage environment conditions, the seal ring will inevitably age, thereby causing its performance to decline and the seal to fail, which will cause serious damage to the engine, thereby causing economic losses.

[0003] A Chinese patent with publication number CN119692033A discloses an aviation rubber static seal ring life prediction method based on temperature analysis, which comprises the following steps: S1, performing an aging temperature test on the aviation rubber static seal ring to obtain performance parameters; S2, establishing a dynamic curve performance degradation model of the aviation rubber static seal ring by estimating aging parameters, test parameters and aging rate parameters; S3, determining the working state of the aviation rubber static seal ring according to the deformation rate of the aviation rubber static seal ring; S4, establishing an aging prediction model of the aviation rubber static seal ring under the target working condition to obtain a life prediction result. The static seal life prediction method based on temperature influence injects extreme high temperature conditions into the dynamic curve performance degradation model, and the obtained aging prediction model has high accuracy. Through high temperature accelerated test, the test period is effectively shortened, and aging data of the seal ring can be obtained in a short time, which has a positive effect on product design, optimization of materials and improvement of process.

[0004] In the above prior art, the seal ring life prediction depends on the aging test results, and the material characteristic fluctuation, production defects and working condition environment multi-factor linkage are not considered. Moreover, the aging test is only relied on. On the one hand, the temperature-life linear extrapolation error is large, and on the other hand, the aging test termination time is long, causing a large amount of energy waste, resulting in a serious mismatch between the predicted life and the actual life, thereby causing a high factory return rate and a cumbersome product after-sales.

[0005] Therefore, the application provides a seal ring life prediction method, system and storage medium. SUMMARY

[0006] In order to make up for the shortcomings of the prior art and solve at least one technical problem proposed in the background art.

[0007] The technical scheme adopted by the application to solve the technical problems is that the seal ring life prediction method comprises the following steps:

[0008] S1: Obtain the sealing ring material information, calculate the theoretical predicted life of the sealing ring based on the sealing ring material information, and store it to the cloud at the same time;

[0009] S2: Calculate the corrected predicted life of the sealing ring based on the online detection data and the theoretical predicted life, and update it to the cloud at the same time;

[0010] S3: Perform aging test, calculate the limit predicted life of the sealing ring based on the corrected predicted life, and update it to the cloud at the same time;

[0011] S4: When the limit predicted life is updated to the cloud at the same time, generate a life prediction report based on the theoretical predicted life and the corrected predicted life.

[0012] Preferably, in S1, the sealing ring material information includes viscosity value and carbon black dispersion , and the sealing ring material information is obtained based on a Mooney viscometer and a laser scattering instrument;

[0013] The method for calculating the theoretical predicted life of the sealing ring is:

[0014] S11: Based on the material information obtained by the material detector, according to the formula:

[0015] ;

[0016] Wherein, is the theoretical predicted life, is a constant;

[0017] S12: Write the calculated theoretical predicted life to the cloud time series database, and associate the sealing ring batch ID.

[0018] Preferably, in S2, the online detection data is sealing ring point cloud data, which is collected based on a three-dimensional scanner;

[0019] The method for calculating the corrected predicted life of the sealing ring is:

[0020] S21: Extract the sealing ring point cloud data, including the feature point distance deviation and the feature line curvature change amount :

[0021] When , trigger material parameter recheck, which is used to dynamically update the constant ;

[0022] S22: Identify defects based on the sealing ring point cloud data, and match the attenuation rate according to the defects;

[0023] S23: Calculate the corrected predicted life based on the attenuation rate, according to the formula:

[0024] ;

[0025] in, To correct predicted lifespan, This indicates the lifespan decay rate of the sealing ring;

[0026] S24: Will revise predicted lifetime Update to the cloud-based time-series database.

[0027] Preferably, the method for calculating the corrected predicted life of the sealing ring further includes:

[0028] S221: Identify sealing ring defects based on sealing ring point cloud data;

[0029] S222: Calculate and output the life decay rate based on the point cloud data of the sealing ring. ;

[0030] S223: If the identified seal ring decision is to be scrapped, then the batch of seal rings shall be scrapped.

[0031] Preferably, in step S3, the method for performing the aging test based on the aging verification parameters is as follows:

[0032] S31: When adjusting the predicted lifetime Update the time-series database in the cloud and trigger the aging test.

[0033] S32: Retrieve aging verification parameters according to the preset aging test template, and perform aging test based on the aging verification parameters;

[0034] S33: Real-time monitoring of sealing force attenuation rate during aging tests , and when When the time is right, terminate the aging test and output the aging verification parameters.

[0035] Preferably, the method for performing aging tests based on the aging verification parameters further includes:

[0036] S321: Identify defect distribution based on point cloud data, and adjust preset aging verification parameters based on defect distribution;

[0037] S322: Based on the feature point distance deviation Predicted trial termination time :

[0038] ;

[0039] in, The baseline aging time;

[0040] S323: If the test time is greater than then use the priority terminate the aging test, otherwise use the decay rate terminate the aging test.

[0041] Preferably, in S3, the method for calculating the ultimate predicted life of the sealing ring is:

[0042] S31: Extract the activation energy from the material database ;

[0043] S32: Calculate the ultimate predicted life based on the aging verification parameter, according to the formula:

[0044] ;

[0045] wherein, is the reference time unit, is the aging test temperature, is the gas constant, is the normal temperature reference value, is the actual test time;

[0046] S33: Update the ultimate predicted life to the cloud time series database.

[0047] Preferably, in S4, the life prediction report includes theoretical predicted life , corrected predicted life and ultimate predicted life .

[0048] A life prediction system for sealing rings, comprising:

[0049] a material detection module for calculating and storing the theoretical predicted life of the sealing ring;

[0050] a three-dimensional scanning module for identifying defects in the sealing ring and correcting the life calculation based on the defects, outputting and storing the corrected predicted life;

[0051] an aging verification module for performing an aging test on the sealing ring, calculating and storing the ultimate predicted life;

[0052] a report generation module for generating a life prediction report combining the theoretical predicted life, the corrected predicted life and the ultimate predicted life.

[0053] A computer-readable storage medium storing program instructions.

[0054] The beneficial effects of the present application are as follows:

[0055] 1. The method, system and storage medium for predicting the service life of a sealing ring, which first predicts the service life through material characteristic calculation theory, then identifies possible defects of the sealing ring in the production process based on online monitoring data, and calculates the corrected predicted service life of the sealing ring based on the defects, and verifies the ultimate predicted service life of the sealing ring under extreme environment based on aging test after the corrected predicted service life is generated, and writes / updates the data of each stage to a cloud time series database in real time to build a data chain of material-production-aging, thereby realizing efficient prediction of the service life of the sealing ring and avoiding the problem of low service life products flowing into the market, which leads to complicated after-sales and high after-sales cost.

[0056] 2. The method, system and storage medium for predicting the service life of a sealing ring, which identifies defects of the sealing ring through online detection data, first improves the detection speed of the sealing ring product to screen out scrapped products, then maps the service life decay rate based on the product defects to calculate the corrected predicted service life, realizes more accurate prediction of the service life of the sealing ring product, and finally performs correction of the constant based on three-dimensional point cloud data to amplify the influence of specific defects on the service life of the sealing ring, further compresses the theoretical predicted service life by using dynamic constant , and further compresses the corrected predicted service life, realizes accurate judgment of the predicted service life of the sealing ring, and avoids complicated after-sales and high after-sales cost. BRIEF DESCRIPTION OF DRAWINGS

[0057] The application will be further described below with reference to the drawings.

[0058] Figure 1 is a flowchart of the application. DETAILED DESCRIPTION

[0059] In order to make the technical means, creative features, purposes and effects realized by the application easy to understand, the application will be further described below with reference to specific embodiments.

[0060] As shown in Figure 1 , the method for predicting the service life of a sealing ring according to the embodiments of the application comprises the following steps:

[0061] S1: Obtain sealing ring material information, calculate the theoretical predicted service life of the sealing ring based on the sealing ring material information, and store it to the cloud at the same time;

[0062] S2: Calculate the corrected predicted service life of the sealing ring based on the online detection data and the theoretical predicted service life, and update it to the cloud at the same time;

[0063] S3: Perform aging test, calculate the ultimate predicted service life of the sealing ring based on the corrected predicted service life, and update it to the cloud at the same time;

[0064] S4: When the limit prediction life is synchronously updated to the cloud, generate a life prediction report based on the theoretical prediction life and the corrected prediction life.

[0065] In the prior art, the life prediction of the sealing ring depends on the experience of engineers and does not consider the material characteristic fluctuation, production defects and working condition environment multi-factor linkage, resulting in a serious inconsistency between the predicted life and the actual life, and further resulting in a high factory return rate and a complicated product after-sales;

[0066] In an embodiment, when predicting the life of the offline sealing ring, a quantitative model needs to be constructed in combination with material parameters to obtain the theoretical prediction life of the sealing ring based on material characteristics. The theoretical prediction life needs to be calculated based on the material information of the sealing ring product, and the calculated theoretical prediction life is synchronized to the cloud time series database and is bound to the sealing ring batch ID, so as to realize the visualization of the theoretical prediction life of the sealing ring batch and the product after-sales traceability. In addition, since the life of the sealing ring is affected by many factors, the above-mentioned theoretical prediction life calculated according to the material characteristics also needs to consider the process factors in the production process of the sealing ring. Due to the influence of the process factors, the theoretical prediction life of the sealing ring will be weakened, so that the actual life of the sealing ring is lower than the theoretical prediction life. It can be foreseen that, when the sealing ring is offline, the online monitoring data also needs to be used to identify possible defects of the sealing ring in the production process, and the corrected prediction life of the sealing ring is calculated based on the defects. When the corrected prediction life is generated, the limit prediction life of the sealing ring under the limit environment is verified based on the aging test. The above-mentioned data of each stage is written / updated to the cloud time series database in real time to construct a data chain of material-production-aging, so as to realize efficient prediction of the life of the sealing ring, avoid low-life products flowing into the market, and solve the problems of complicated product after-sales and high after-sales cost. In addition, based on the correlation of the established sealing ring batch ID and the prediction life of each stage, efficient traceability of the sold products can be realized, the complete chain from material to finished product to after-sales of the product is optimized, and the management of the sealing ring production is strengthened.

[0067] Preferably, in S1, the sealing ring material information includes viscosity value and carbon black dispersion degree , and the sealing ring material information is obtained based on a Mooney viscometer and a laser scattering instrument.

[0068] The method for calculating the theoretical prediction life of the sealing ring is as follows:

[0069] S11: Based on the material information obtained by the material detector, the theoretical prediction life is calculated according to the formula:

[0070] ;

[0071] wherein, is the theoretical prediction life, is a constant.

[0072] S12: record the calculated theoretical prediction life of the sealing ring in the material database. Write to the cloud time series database and associate the sealing ring batch ID.

[0073] Based on the above, the materials required for the production of the sealing ring are greatly affected by environmental factors, especially under the influence of temperature and humidity, which will cause the viscosity value and carbon black dispersion to change significantly, therefore, the viscosity value and carbon black dispersion need to be dynamically adjusted according to environmental factors, so as to calculate the theoretical prediction life of the sealing ring, and record the dynamically adjusted viscosity value and carbon black dispersion in the material database, when calculating the theoretical prediction life of the sealing ring, the viscosity value and carbon black dispersion are real-time retrieved, if the theoretical prediction life of the sealing ring is calculated with fixed viscosity value and carbon black dispersion , there will be a large error due to environmental factors, resulting in the separation of material parameters and theoretical prediction life calculation, and the quality of the batch sealing ring cannot be associated in real time, in the embodiment, the sealing ring production process includes selecting materials, processing, testing and other links, in these links, first, the material information is obtained by using the Mooney viscometer and the laser scattering instrument, including the viscosity value and carbon black dispersion , the measurement method of the viscosity value is to take a sealing ring raw material rubber sample, test it in the Mooney viscometer at 100°C for 4 minutes, and get the viscosity value, the unit is , and the measurement method of the carbon black dispersion is to scan the sample cross section by laser scattering instrument, calculate the uniformity of carbon black particle distribution, and get the dispersion score, for the constant in the theoretical prediction life calculation formula, the determination method is based on 10000+ groups of sealing ring accelerated aging test data regression analysis, covering various commonly used sealing ring materials, including nitrile rubber and fluorine rubber, first, the measured life of the sealing ring is obtained, such as years, then the viscosity value and carbon black dispersion of the sealing ring are obtained, recorded in the material database, such as , , then according to the formula:

[0074] ;

[0075] the calculated value is ;

[0076] By analogy, when the sealing ring is inspected in subsequent processing and manufacturing, the theoretical predicted service life of the sealing ring can be calculated according to the material information and the constant recorded in the material database . It can be understood that, since the constant is only obtained based on historical data regression analysis, therefore, in the actual application stage, there is still a certain error between the theoretical predicted service life calculated according to the constant and the viscosity value and the carbon black dispersion degree and the actual service life, therefore, further analysis and verification are still needed to correct the predicted service life of the sealing ring. At the end of this stage, the calculated theoretical predicted service life is written into the cloud time series database, and is associated with the sealing ring batch ID. In the subsequent calculation of the corrected predicted service life, the theoretical predicted service life corresponding to the sealing ring batch ID can be directly called based on the cloud time series database , and the corrected predicted service life is calculated.

[0077] Based on the above, in the embodiment, the material information updated in real time based on the material database is used to calculate the theoretical predicted service life of the sealing ring, which can weaken the error of the theoretical predicted service life caused by the fluctuation of environmental data, thereby improving the accuracy of the theoretical predicted service life.

[0078] Preferably, in S2, the online detection data is sealing ring point cloud data, which is collected based on a three-dimensional scanner.

[0079] The method for calculating the corrected predicted service life of the sealing ring is as follows:

[0080] S21: Extract the sealing ring point cloud data, including the feature point distance deviation and the feature line curvature change amount :

[0081] When , trigger the material parameter re-inspection, which is used to dynamically update the constant .

[0082] S22: Identify defects based on the sealing ring point cloud data, and match the attenuation rate according to the defects;

[0083] S23: Calculate the corrected predicted service life based on the attenuation rate, according to the formula:

[0084]

[0085] wherein, is the corrected predicted service life, and the life attenuation rate of the sealing ring represents the attenuation rate of the sealing ring;

[0086] S24: updating the corrected predicted service life to the cloud time series database.

[0087] In actual application, as described above, the theoretical predicted service life calculated according to the sealing ring material information can be understood as the upper limit of the predicted service life determined based on the material properties, that is, the predicted service life that can be theoretically achieved based on the material properties, which is determined based on historical data regression analysis. However, as described above, in actual production, errors based on production processes can cause the performance of the produced sealing ring to be less than ideal, so the sealing ring cannot achieve the theoretical predicted service life. In this embodiment, when the sealing ring is produced offline, the same batch of sealing rings also needs to be sampled and inspected. It can be understood that the purpose of sampling and inspection is to detect whether the appearance of the sealing ring is consistent with the ideal appearance. It is worth noting that if there are cracks or burrs on the surface of the finished sealing ring after production, it will not be able to achieve the theoretical predicted service life. Therefore, when the sealing ring is produced offline, the point cloud data of the sealing ring is obtained based on the three-dimensional scanner, and the point cloud data is used to calculate the feature point distance deviation and the feature line curvature change First, according to the obtained point cloud data, it is analyzed whether the feature line curvature change is greater than a threshold value If it is greater than the threshold value , the material parameter re-inspection is automatically triggered, so as to update the constant , and further adjust the theoretical predicted service life. For the above, the physical essence of the feature line curvature change is to verify whether there is a molecular chain rupture or filler agglomeration on the surface of the sealing ring, which causes a sudden change in local stiffness. When the feature line curvature change , it means that the surface has the above defects, and at this time the actual performance of the material has deviated from the original parameters, that is, the viscosity value and the carbon black dispersion recorded in the material database have changed, for example, the viscosity value may rise (molecular chain rupture causes decreased fluidity), and the carbon black dispersion may decrease (carbon black agglomeration). At this time, if feedback is not established, the error of the theoretical predicted service life relative to the actual value will be greatly enlarged, so the corrected predicted service life and the limit predicted service life calculated subsequently are all higher than the actual value. Therefore, when the feature line curvature change is greater than the threshold value , the material parameter re-inspection is automatically triggered.

[0088]

[0089] For example, assume that the following data is measured: and , , then the updated constant ;

[0090] In the determination of the sealing ring defect according to the point cloud data, if the sealing ring is identified to have no defect, it is released and the production is continued, if the sealing ring is identified to have a defect, the corresponding decision is automatically triggered, and the decay rate is matched according to the defect, the corrected prediction life of the sealing ring is calculated according to the obtained decay rate After obtaining the corrected prediction life of the sealing ring , the corrected prediction life is uploaded to the cloud time series database and is associated with the sealing ring batch ID;

[0091] In an embodiment, the corrected prediction life calculated is compared with the theoretical prediction life , and the main influencing factor is defined as the defect, that is, the point cloud data of the sealing ring obtained based on the three-dimensional scanner, which can be understood as that the theoretical prediction life of the sealing ring calculated according to the material information is the upper limit of the prediction life of the sealing ring, as described above, due to the existence of some influencing factors, the sealing ring cannot reach the theoretical prediction life, therefore, in this embodiment, the sealing ring defect is identified based on the point cloud data, and the theoretical prediction life of the sealing ring is attenuated according to the defect, so as to obtain the corrected prediction life ; based on this, firstly, the detection speed of the sealing ring product is improved, and the scrapped product is screened, secondly, based on the product defect, the life decay rate is mapped, so as to obtain the corrected prediction life, and finally, based on the three-dimensional point cloud data, the constant is corrected, so as to amplify the influence of the specific defect on the life of the sealing ring, and the dynamic constant is used to further compress the theoretical prediction life, and then the corrected prediction life is compressed, so as to realize the accurate judgment of the prediction life of the sealing ring, avoid the complicated after-sales and reduce the high after-sales cost;

[0092] Among them, the feature point distance deviation is mainly used to measure whether the appearance of the sealing ring is consistent with the ideal appearance size, and is used to detect deformation and the like; and the feature line curvature change amount is mainly used to measure whether there is a depression and the like;

[0093] For the feature point distance deviation Measurement: A topological mesh of feature points of the sealing ring is established based on the CAD design model, and key measurement points are defined, such as the fixed point of the sealing lip and the center point of the bottom of the groove. Then, a 3D laser scanner is used to acquire point cloud data of the sealing ring surface, with ≥500,000 points collected per piece. The measured point cloud data is aligned and registered with the CAD model using the ICP algorithm, and the distance difference between corresponding points is calculated. The largest distance difference is selected as the feature point distance deviation. Under normal circumstances, Defined as qualified, it is released normally, while Defined as a defect, the remedial measure is lifespan modification, and Defined as a defect, the appropriate course of action is mandatory scrapping;

[0094] For the change in curvature of the characteristic line Measurement: Feature lines are acquired by first identifying 30 generatrices along the circumference of the sealing ring, with adjacent segments spaced 12° apart. 200 data points are sampled for each generatrices. Curvature is then calculated, and the change in curvature is used as the change in curvature of the feature lines. In general, each material corresponds to a threshold. If NBR rubber is used, then .

[0095] Preferably, the method for calculating the corrected predicted life of the sealing ring further includes:

[0096] S221: Identify sealing ring defects based on sealing ring point cloud data;

[0097] S222: Calculate and output the life decay rate based on the point cloud data of the sealing ring. ;

[0098] S223: If the identified seal ring decision is to be scrapped, then the batch of seal rings shall be scrapped.

[0099] Based on the above embodiments, it is understood that when the sealing ring comes off the production line, it is necessary to perform a 3D scan of the sealing ring's appearance and then compare it with the ideal appearance recorded in the CAD model library to identify whether there are defects in the appearance of the sealing ring coming off the line. It is foreseeable that if the identified sealing ring appearance differs from the ideal appearance, the sealing ring can be forcibly scrapped or its lifespan can be extended based on the specific manifestation of the difference. In this embodiment, firstly, based on the point cloud data and the ideal appearance recorded in the CAD model library, based on the feature point distance deviation... With the change in curvature of the characteristic line , if the seal ring has defects, then according to the specific performance of the defects, specifically including: according to the point cloud data, if it is identified that the seal ring has perforation, feature point distance deviation greater than the threshold or crack, then forced scrap;

[0100] If bubbles are identified, the predicted service life is corrected; if pits or burrs are identified, the predicted service life is corrected; based on the above, according to the seal ring point cloud data, mainly used to force the seal ring that cannot be used to be scrapped, prevent waste products from flowing into the market, cause adverse effects or safety accidents;

[0101] In addition, after identifying the defects of the seal ring, the decay rate of the corrected predicted service life of the seal ring also needs to be calculated, specifically:

[0102] First, the feature parameters are extracted from the seal ring point cloud data, including:

[0103] In addition, after identifying the defects of the seal ring, the decay rate of the corrected predicted service life of the seal ring also needs to be calculated, specifically:

[0104] First, the feature parameters are extracted from the seal ring point cloud data, including:

[0105] Distance deviation extreme value , the maximum distance deviation value of the feature points on the surface of the seal ring and the corresponding points of the CAD ideal model, which is calculated by three-dimensional point cloud registration, is used to quantify the maximum size deformation, which can reflect the macroscopic geometric distortion of the seal ring caused by forming stress, mold wear, etc., including ovality, uneven lip thickness, etc., corresponding to the above, when , it is determined as A-level defect, forced scrap;

[0106] Curvature change entropy value , extract the curvature change value along the seal ring generatrix , calculate the information entropy to represent the molecular chain disorder degree, unit: When , it indicates that the carbon black agglomeration or crosslinking fracture causes local stiffness mutation;

[0107] Defect distribution entropy , the composite entropy value of the spatial distribution and aggregation density of the defect points, used to reflect the aggregation degree of the defects, when , it shows that the defects are distributed in clusters, inducing stress concentration and causing cracks;

[0108] Subsequently, the above data is input into the decay rate calculation model:

[0109] According to the formula:

[0110]

[0111] Set weight configuration:

[0112] , the weight of the distance deviation extreme value characterizes the size deformation dominant;

[0113] , the weight of the curvature change entropy value characterizes the molecular structure influence;

[0114] , the weight of the defect distribution entropy characterizes the defect distribution correction;

[0115] The above weights are derived from the regression analysis of historical data;

[0116] is a constant, based on experience, set ;

[0117] , the maximum attenuation upper limit, to avoid failure;

[0118] Output attenuation rate

[0119] Exemplary:

[0120] Take butane rubber seal ring as an example, based on point cloud scanning found:

[0121] , ,

[0122] Then based on the calculation:

[0123]

[0124] Then the corrected life calculation is:

[0125] ;

[0126] Based on the above, the sealing ring point cloud data is used to calculate the attenuation rate corresponding to the sealing ring, and then the corrected predicted service life of the sealing ring is calculated according to the attenuation rate. If the identified sealing ring needs to be compulsorily scrapped, an instruction for compulsorily scrapping the sealing ring in the same batch is automatically triggered. Based on the above, the appearance point cloud data of the sealing ring after being offline is obtained according to the ideal sealing ring appearance recorded in the preset CAD model library, and the defect type and position of the sealing ring in the batch can be obtained based on comparison. In order to speed up the sealing ring detection rate, when it is determined that the sealing ring needs to be compulsorily scrapped, the compulsory scrapping instruction is automatically executed. If the sealing ring does not need to be compulsorily scrapped and has defects, the corresponding attenuation rate of the sealing ring in the batch is matched. The attenuation rate calculated based on the sealing ring point cloud data can analyze the structural defects of the sealing ring from the microscopic point cloud data, so as to more effectively control the quality of the sealing ring, find defects that are difficult to find by visual inspection, and calculate the service life attenuation rate of the sealing ring, so as to calculate and output the corrected predicted service life of the sealing ring. The predicted service life of the sealing ring can be more truly reflected, and the error can be reduced.

[0127] Preferably, in S3, the method for performing the aging test based on the aging verification parameter is:

[0128] S31: When the corrected predicted service life is updated to the cloud time series database, an instruction for performing the aging test is triggered;

[0129] S32: The aging verification parameter is retrieved according to a preset aging test template, and the aging test is performed based on the aging verification parameter;

[0130] S33: The sealing force attenuation rate of the sealing ring in the aging test is monitored in real time, and when , the aging test is terminated, and the aging verification parameter is output.

[0131] In the actual application stage, after the corrected predicted service life of the sealing ring is calculated, the corrected predicted service life can be regarded as the influence of actual production on the theoretical predicted service life, mainly reflected in the predicted service life attenuation corresponding to the defect. In the previous embodiment, it has been clearly recorded how to calculate the corrected predicted service life according to the attenuation rate corresponding to the defect. It can be understood that the sealing ring is also susceptible to environmental interference in actual use, resulting in a difference between the predicted service life and the actual service life. It can be understood that the conventional aging test uses a fixed parameter template, which cannot adapt to sealing rings with different defects, resulting in low test efficiency and distorted results. Therefore, the environmental factors need to be considered in the prediction of the service life. In this embodiment, after the corrected predicted service life is updated in the cloud time series database , an instruction for performing the aging test is triggered immediately. The system performs the aging test on the sealing ring based on the preset aging test template and the preset aging verification parameter. In the aging test process, the sealing force attenuation rate and setting the condition of terminating the test, i.e. when the aging test is terminated, at this time the aging verification parameter is output; by setting the condition of terminating the aging test as the physical failure critical point, invalid tests can be avoided, the aging result is ensured to be related to the actual sealing performance, in addition, based on the setting of the termination time condition, dynamic termination can be achieved, the test period is shortened to avoid excessive testing, and energy consumption is saved.

[0132] Preferably, the method of performing an aging test based on the aging verification parameter further comprises:

[0133] S321: identifying a defect distribution based on the point cloud data, and adjusting the preset aging verification parameter based on the defect distribution;

[0134] S322: adjusting the aging verification parameter according to the feature point distance deviation pre-test termination time :

[0135]

[0136] wherein, is the reference aging time;

[0137] S323: if the test time is greater than , the aging test is terminated using , otherwise the aging test is terminated at the decay rate .

[0138] In actual application, the preset aging test template is suitable for all sealing rings, but the defects of different batches of sealing rings are completely different, therefore, if there is any sealing ring whose defects are concentrated in one place, and the aging test is performed using the preset aging test template and the corresponding aging verification parameter, a lot of time may be wasted, and the problem of late discovery of fatal defects may occur. Based on the above, since it has been shown in the foregoing embodiments that the point cloud data of the offline sealing ring needs to be acquired, therefore, in this embodiment, based on the acquired sealing ring point cloud data, the defect distribution is identified to specify the aging verification parameter. It is worth noting that the aging verification parameter here is based on the preset aging test template, such as defect concentration, the local temperature of the defect area can be increased on the preset aging verification parameter, thereby accelerating the aging test process and shortening the aging test time.

[0139] For example, based on the point cloud data, it is identified that the defect positions of the batch of sealing rings include position A and position B, wherein the defect type of position A is a bubble, and the size is a diameter of 0.4 mm, and the feature line curvature change amount ; the defect type of position B is deformation, and the size is a feature point distance deviation ​, show that the feature point offset is out of standard; based on the above, when setting the aging verification parameters, based on the sealing ring material information, the corresponding aging test template and verification parameters are set as temperature 140℃ and pressure 25MPa; due to the defect concentration, in this embodiment, the aging verification parameters are modified and adjusted, for example, for the position A area, the temperature is adjusted to 160℃, and for the position B area, the pressure is adjusted to 30Mpa; the resulting effects include that in the position A area, the bubbles expand and break due to the increase of temperature, and in the position B area, the stress concentration triggers the micro-crack propagation due to the increase of pressure;

[0140] In addition, in this embodiment, another termination mechanism of the aging test is also disclosed, that is, based on the feature point distance deviation and the reference aging time The calculated pre-test termination time ; based on the setting of the pre-test termination time , the invalid time consumption of the aging test can be avoided, and it can be understood that the reference aging time refers to the time required for an ideal sealing ring to reach 40% sealing force attenuation under standard aging conditions. According to historical data, the reference aging time of a sealing ring processed by Nitrile rubber is about 1000h, and the reference aging time of a sealing ring processed by Fluorine rubber is about 2000h. From the perspective of feature point distance deviation , analyze the mechanism of affecting the aging speed, when , it is an ideal state, at this time, the molecular chains of the microstructure of the sealing ring are uniformly distributed, and the aging rate is constant in the aging test, and if , stress concentration occurs in the microstructure, causing the molecular chain to break, and in this state of the aging test, the micro-crack propagation rate will rapidly increase, therefore, it can be predicted that the predicted aging time and the feature point distance deviation are closely related, in this embodiment, the feature point distance deviation is calculated by using the feature point deviation extreme value , and if the aging test is terminated by using only the decay rate real-time monitoring method as described in the embodiment, the test time may be too long and resources may be wasted, therefore, in this embodiment, the predicted aging time and the decay rate monitoring are combined to form a double threshold value cooperative control aging time termination condition:

[0141] When the test time is greater than , the time threshold is used to control the termination of the aging time, and secondly, when the sealing force preferentially decays to 40%, the decay rate is triggered to terminate the aging test.

[0142] Preferably, in the S3, the method for calculating the limit prediction life of the sealing ring is:

[0143] S31: Extract the activation energy from the material database ;

[0144] S32: Calculate the limit prediction life based on the aging verification parameter according to the formula:

[0145]

[0146] Wherein, is the reference time unit, is the aging test temperature, is the gas constant, is the normal temperature reference value, is the actual test time;

[0147] S33: Update the limit prediction life to the cloud time series database.

[0148] The conventional aging test tests the sealing ring life based on fixed conditions, but the working condition temperature fluctuation range is large in the actual application process, and the test result cannot be directly mapped to the formal environment. In this embodiment, according to the corrected prediction life obtained by calculation, the limit prediction life needs to be calculated according to the data obtained by the aging test. It can be understood that the corrected prediction life is calculated according to the difference between the appearance characteristics of the sealing ring at the time of offline and the ideal sealing ring appearance characteristics, so it can be understood that the theoretical prediction life corresponds to the ideal sealing ring, and if the subsequent offline sealing ring has certain defects relative to the ideal sealing ring, the prediction life needs to be corrected. Therefore, based on the above, the corrected prediction life is obtained, and the limit prediction life needs to be considered. The theoretical prediction life and the corrected prediction life are both not considered environmental factors, so the limit prediction life needs to be calculated in combination with the parameters output by the aging test. Based on the limit prediction life , the prediction life of the sealing ring in various working condition environments is represented; in this embodiment, the activation energy corresponding to the sealing ring ID is first extracted from the material database , for example, the material activation energy of nitrile rubber is , and the material activation energy of fluororubber is

[0149] Taking the sealing ring processed by nitrile rubber as an example:

[0150] Assuming that the relevant data obtained is as follows:

[0151] 、 、 、 、 、 00

[0152]

[0153]

[0154]

[0155] may be understood that, based on the above data calculation, the limit prediction life years;

[0156] Based on the above, when calculating the limit prediction life of the sealing ring after the aging test, the activation energy extracted from the material database is used to associate the material characteristics, and the calculated formula is used to adjust and correct the prediction life to obtain the limit prediction life, wherein As a temperature compensation term, the correlation between high-temperature aging and normal-temperature life is quantified, and the error problem of traditional linear prediction is reduced, and the actual test time The actual test termination time is used instead of a preset fixed value, combined with The double-mechanism termination condition is constituted to ensure that the aging data truly reflects the failure critical point, reduce the test time, and reduce the test energy consumption, wherein the activation energy is extracted from the material database and obtained based on historical data regression analysis, realizing the data closed loop from material characteristics to aging verification to limit prediction life, and based on the activation energy to the after-sales data backflow, it can also complete the self-iterative optimization of life prediction, and improve the confidence of the prediction life.

[0157] Preferably, in S4, the life prediction report includes theoretical prediction life , corrected prediction life and limit prediction life .

[0158] A life prediction system of a sealing ring, comprising:

[0159] A material detection module for calculating and storing the theoretical prediction life of the sealing ring;

[0160] A three-dimensional scanning module for identifying defects of the sealing ring and correcting life calculation based on the defects, outputting and storing the corrected prediction life;

[0161] An aging verification module for aging test of the sealing ring, calculating and storing the limit prediction life;

[0162] A report generation module is configured to generate a life prediction report in combination with the theoretical predicted life, the corrected predicted life and the ultimate predicted life.

[0163] A computer readable storage medium stores program instructions.

[0164] The above shows and describes the basic principles, main features and advantages of the present application. Those skilled in the art should understand that the present application is not limited to the above-mentioned embodiments, and the above-mentioned embodiments and descriptions in the specification are only to illustrate the principles of the present application. Without departing from the spirit and scope of the present application, various changes and improvements can be made to the present application, and these changes and improvements all fall within the scope of the claimed present application. The scope of protection of the present application is defined by the appended claims and their equivalents.

Claims

1. A method of predicting the lifetime of a seal ring, characterized by: The method comprises the following steps: S1: Obtain the sealing ring material information, calculate the theoretical predicted service life of the sealing ring based on the sealing ring material information, and store it in the cloud at the same time; S2: Calculate the corrected predicted service life of the sealing ring based on the online detection data and the theoretical predicted service life, and update it in the cloud at the same time; S3: Perform aging test, calculate the limit predicted service life of the sealing ring based on the corrected predicted service life, and update it in the cloud at the same time; S4: When the limit predicted service life is updated in the cloud at the same time, generate a service life prediction report based on the theoretical predicted service life and the corrected predicted service life; In the S1, the sealing ring material information includes viscosity value and carbon black dispersion , and the sealing ring material information is obtained based on a Mooney viscometer and a laser scattering instrument. The method for calculating the theoretical predicted service life of the sealing ring is: S11: Based on the material information obtained by the material detector, according to the formula: ； wherein, is the theoretical predicted lifetime, is a constant; S12: write the calculated theoretical prediction life into the cloud time series database and associate with the seal batch ID. write into the cloud time series database and associate with the seal batch ID; In the S2, the online detection data is the sealing ring point cloud data, which is collected based on a three-dimensional scanner; The method for calculating the corrected predicted service life of the sealing ring is: S21: Extract the sealing ring point cloud data, including the feature point distance deviation With the feature line curvature change amount : When a material parameter review is triggered for dynamic updating of constants wherein is a threshold value; S22: Identify defects based on the sealing ring point cloud data, and match the attenuation rate according to the defects; S23: Calculate the corrected predicted service life based on the attenuation rate, according to the formula: ； wherein, to correct the predicted life, represents the life deterioration rate of the seal ring; S24: updating the corrected predicted lifetime to a cloud time series database; In the S3, the method for performing aging test based on the aging verification parameters is: S31: When the predicted life is corrected Update to the cloud time series database, trigger to perform aging test; S32: Retrieve the aging verification parameters according to the preset aging test template, and perform the aging test based on the aging verification parameters; S33: Real-time monitoring of the sealing force decay rate in the aging test , and when the aging test is terminated, and the aging verification parameter is output. In the S3, the method for calculating the limit predicted service life of the sealing ring is: S31 : Extract activation energy from material database ; S32: Calculate the limit predicted service life based on the aging verification parameters, according to the formula: ； wherein, is a reference time unit, is an aging test temperature, is a gas constant, is a normal temperature reference value, is an actual test time; S33: update the limit predicted lifetime to a cloud time series database.

2. The method of predicting the life of a seal ring according to claim 1, wherein: The method for calculating the corrected predicted service life of the sealing ring further comprises: S221: Identify the sealing ring defects based on the sealing ring point cloud data; S222: Calculate and output the life decay rate according to the sealing ring point cloud data ; S223: When the identified sealing ring decision is forced to scrap, the same batch of sealing rings are forced to scrap.

3. The method of predicting the life of a seal ring according to claim 2, wherein: The method for performing aging test based on the aging verification parameters further comprises: S321: Identify the defect distribution based on the point cloud data, and adjust the preset aging verification parameters based on the defect distribution; S322: According to the feature point distance deviation Pre-test end time : ； wherein tref is the reference aging time; S323: If the test time is greater than then use the accelerated aging test is terminated, otherwise the accelerated aging test is terminated with the decay rate .

4. The method of predicting the life of a seal ring according to claim 3, wherein: In the S4, the life prediction report includes a theoretical prediction life , a corrected prediction life , and a limit prediction life .

5. A system for predicting the life of a seal ring, adapted to the method for predicting the life of a seal ring according to any one of claims 1 to 4, characterized in that: It comprises: A material detection module for calculating the theoretical predicted service life of the sealing ring and storing it; A three-dimensional scanning module for identifying sealing ring defects and correcting service life calculation based on the defects, outputting the corrected predicted service life and storing it; An aging verification module for aging test of the sealing ring, calculation of the limit predicted service life and storage; A report generation module for generating a service life prediction report combining the theoretical predicted service life, the corrected predicted service life and the limit predicted service life.

6. A computer-readable storage medium, characterized in that: A program instruction is stored, which is executed by a processor to realize the service life prediction method of the sealing ring in any one of claims 1-4.

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