Measurement production equipment fault prediction and state evaluation method based on digital twinning

Abstract

The invention discloses a metering production equipment fault prediction and state evaluation method based on digital twinning, which relates to the technical field of production equipment fault prediction, and comprises the following steps: by setting a deterioration coefficient cross-line value lower than an early warning threshold value, analyzing the time of the equipment deterioration coefficient of metering production equipment from the deterioration coefficient cross-line value to the early warning threshold value; obtaining a fault prediction duration, and displaying the fault prediction duration when the equipment degradation coefficient of the metering production equipment exceeds a degradation coefficient cross-line value; when the equipment degradation coefficient of the production equipment is measured to exceed the degradation coefficient cross-line value, the calculated fault prediction duration is displayed, the time point when the equipment possibly breaks down is predicted in advance by accurately calculating the fault prediction duration, accurate time reference is provided for preventative maintenance of the equipment, the downtime caused by sudden faults is shortened, and the production efficiency is improved. And continuous and stable operation of the equipment is ensured through timely maintenance.

Description

Technical Field

[0001] The present invention relates to the technical field of production equipment failure prediction, and in particular to a method for failure prediction and status evaluation of metrological production equipment based on digital twins. Background Art

[0002] Digital twin technology, by building a virtual mapping of the physical system, enables real-time exchange and in-depth analysis of information. In an intelligent manufacturing environment, metrology production equipment needs to have a higher level of automation and intelligence. Digital twin technology can provide equipment with real-time status information and decision support, helping the equipment to make autonomous decisions and optimize operations.

[0003] However, when predicting faults of metrological production equipment, the prediction accuracy is not high due to factors such as incomplete data, simplified models or algorithm limitations. It is difficult to accurately reflect the actual status of the equipment and cannot predict faults in a timely and accurate manner, resulting in maintenance only after the equipment failure occurs, causing great losses. Maintenance personnel should be able to understand the operation status and failure risks of the equipment more intuitively. Based on this, a fault prediction and status evaluation method for metrological production equipment based on digital twins is proposed. Summary of the invention

[0004] The purpose of the present invention is to provide a fault prediction and status evaluation method for metrological production equipment based on digital twins, which solves the technical problem that it is difficult to accurately reflect the actual status of the equipment, cannot predict faults in a timely and accurate manner, and causes maintenance to be performed only after the equipment failure occurs.

[0005] The purpose of the present invention can be achieved through the following technical solutions:

[0006] The method for fault prediction and status evaluation of metrological production equipment based on digital twin includes the following steps:

[0007] Step 1: Obtain the parameter values ​​corresponding to each parameter when the measurement production equipment fails;

[0008] Step 2: Analyze the parameter values ​​corresponding to each parameter when the metering production equipment fails, and obtain the lower and upper limits of each parameter that must stop working:

[0009] Step 3: Analyze the normal operating values ​​corresponding to each parameter of the measurement production equipment, and obtain the standard values ​​corresponding to each parameter of the production equipment under normal working conditions;

[0010] Step 4: Analyze the number of times each parameter exceeds the limit when the measurement production equipment fails b times, and obtain the correlation coefficient between each parameter and the measurement production equipment;

[0011] Step 5: Calculate the degradation degree corresponding to each parameter by combining the real-time operating value of each parameter with the lower limit value, upper limit value, standard value and correlation coefficient at which each parameter must stop working;

[0012] Step 6: Measure the equipment degradation coefficient corresponding to the production equipment, and generate a fault warning signal based on the equipment degradation coefficient;

[0013] Step 7: By setting a degradation coefficient crossing value lower than the warning threshold, analyze the time from the degradation coefficient crossing value to the warning threshold of the equipment degradation coefficient of the metrological production equipment, and obtain the fault prediction duration. When the equipment degradation coefficient of the metrological production equipment exceeds the degradation coefficient crossing value, the fault prediction duration is displayed.

[0014] As a further solution of the present invention, it is characterized in that the specific method of obtaining the lower limit value and the upper limit value of each parameter at which the operation must be stopped is:

[0015] S1: Select any one of the parameters as the analysis item parameter, obtain the parameter value Fn corresponding to each time when the measurement production equipment fails n times, where n is the preset number of times, n is a positive integer, n≥1, calculate the standard deviation U of Fn, analyze the standard deviation U, and obtain the lower limit value XB1 and upper limit value XA1 of the analysis item parameter that must stop working according to the analysis results;

[0016] S2: In the same way as obtaining the lower limit value XB1 and upper limit value XA1 at which the analysis item parameters must stop working, analyze the parameter values ​​of various parameters of the metrological production equipment when a fault occurs, and obtain the lower limit value XBi and upper limit value XAi corresponding to the time when each parameter must stop working according to the analysis results, where i refers to different parameter items of the metrological production equipment, i is a positive integer, i=1, 2, ..., e, e is the number of parameter items of the metrological production equipment, e≥1.

[0017] As a further solution of the present invention, it is characterized in that the specific method of obtaining the lower limit value XB1 and the upper limit value XA1 at which the analysis item parameter must stop working is:

[0018] When the standard deviation U is less than or equal to the preset value Y1, the maximum and minimum values ​​in Fn are respectively used as the upper limit value XA1 and the lower limit value XB1 at which the analysis item parameters must stop working; when the standard deviation U is greater than the preset value Y1, the mean of the absolute values ​​of the differences between each Fn and the mean Fp is marked as the deviation value P, and the difference between the mean Fp and the deviation value P is used as the lower limit value XB1 at which the analysis item parameters must stop working; at the same time, the sum of the mean value of Fn and the deviation value P is used as the upper limit value XA1 at which the analysis item parameters must stop working.

[0019] As a further solution of the present invention, it is characterized in that the specific method of obtaining the standard values ​​corresponding to the various parameters of the production equipment under normal working conditions is:

[0020] S01: Select the same parameter item as that in step S1 as the analysis item parameter, and obtain the value Er of the analysis item parameter corresponding to the normal operation of the measurement production equipment r times, where r is the preset number of times, r is a positive integer, and r≥1; obtain the value Ee in Er that satisfies |Er-Ep|≥Y3, where e is the number of values ​​in Er that meet the preset screening condition A1, r≥e≥1, when the number e is greater than the preset value Y2, the mean value Ep of Er is used as the standard value D1 corresponding to the analysis item parameter under normal working conditions, and when the number e is less than or equal to the preset value Y2, the mean of the maximum and minimum values ​​in Er is used as the standard value D1 corresponding to the analysis item parameter under normal working conditions, where Y2 and Y3 are both preset values;

[0021] S02: In the same manner as obtaining the standard value D1 corresponding to the analysis item parameter under normal working conditions, the standard values ​​D i corresponding to the various parameters of the production equipment under normal working conditions are analyzed and obtained.

[0022] As a further solution of the present invention, it is characterized in that the specific method of obtaining the correlation coefficient between each parameter and the measurement production equipment is:

[0023] First, the parameter intervals [XAi, XB i] corresponding to each parameter are generated according to the lower limit value and upper limit value at which each parameter must stop working. The number of times each parameter does not belong to its corresponding parameter interval when b failures occur in the metrological production equipment is obtained, and it is marked as the number of over-limit times Gi corresponding to each parameter. The ratio between the number of over-limit times Gi corresponding to each parameter and b is used as the correlation coefficient Hi corresponding to each parameter.

[0024] As a further solution of the present invention, it is characterized in that the specific method of obtaining the degradation degree corresponding to each parameter is:

[0025] The real-time running numbers of various parameters of the metering production equipment are marked as Zi, and the formula is: The degradation degree Mi corresponding to each parameter of the metrological production equipment is calculated and obtained.

[0026] As a further solution of the present invention, it is characterized in that the specific method of generating a fault warning signal according to the equipment degradation coefficient is:

[0027] pass The equipment degradation coefficient Q corresponding to the metrological production equipment is calculated and obtained. When Q is greater than the warning threshold Y4, a fault warning signal is generated. Here, e≥f≥1, Y4=0.82.

[0028] As a further solution of the present invention, it is characterized in that the specific method of obtaining the fault prediction duration is:

[0029] Set a degradation coefficient crossing value Y5 that is lower than the warning threshold Y4, and obtain the time Tg required for the equipment degradation coefficient of the metrological production equipment to reach the warning threshold Y4 from exceeding the degradation coefficient crossing value Y5 for multiple times, and calculate the discrete value W of Tg. When the discrete value W is less than the preset value Y6, the mean Tp of Tg is used as the fault prediction time L. When W is greater than or equal to the preset value Y6, the mean of the maximum and minimum values ​​in Tg is used as the fault prediction time L, where g is the number of acquisitions, g is a positive integer, g≥1, and Y5 is 0.72.

[0030] Beneficial effects of the present invention:

[0031] (1) The present invention generates a fault warning signal to prompt maintenance personnel to conduct inspections or maintenance in a timely manner, which provides maintenance personnel with sufficient time to prepare for maintenance work, greatly reduces maintenance costs, helps reduce unexpected equipment downtime, and improves overall production efficiency and equipment utilization. Timely fault prediction can also prevent accidents caused by equipment failure, ensure the safety of production processes and employees, and improve overall production efficiency and equipment utilization;

[0032] (2) The present invention calculates the equipment degradation coefficient in real time by setting the degradation coefficient crossing value and the warning threshold, and displays the fault prediction duration when the crossing value is exceeded, thereby providing maintenance personnel with clear warning information and time reference, predicting in advance the time point when the equipment may fail, providing accurate time reference for preventive maintenance of the equipment, reducing downtime caused by sudden failures, ensuring continuous and stable operation of the equipment through timely maintenance, improving overall production efficiency, avoiding safety accidents that may be caused by equipment failures, and ensuring the safety of the production environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] The present invention will be further described below in conjunction with the accompanying drawings.

[0034] Figure 1 It is a schematic diagram of the framework structure of the digital twin-based metrology production equipment fault prediction and status evaluation method of the present invention. DETAILED DESCRIPTION

[0035] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0036] Embodiment 1

[0037] See also Figure 1 As shown, the present invention is a method for fault prediction and state evaluation of metrological production equipment based on digital twins, comprising:

[0038] Step 1: Obtain the fault data corresponding to each parameter of the measurement production equipment. The historical fault data refers to the parameter values ​​corresponding to each parameter when the measurement production equipment fails;

[0039] Step 2: Analyze the parameter values ​​corresponding to each parameter when the metering production equipment fails, and obtain the lower limit and upper limit of each parameter that must stop working. The specific method is as follows:

[0040] Obtain the numerical values ​​corresponding to various parameters of the measurement production equipment when a failure occurs;

[0041] S1: Select any one of the parameters as the analysis parameter;

[0042] Obtain the parameter values ​​corresponding to the analysis item parameters each time when the metrological production equipment fails n times, and mark them as Fn, where n is a preset number of times, n is a positive integer, and n≥1;

[0043] pass n≥c≥1, calculate the standard deviation U of Fn, when the standard deviation U is less than or equal to the preset value Y1, obtain the maximum value F in Fn max and the minimum value F min , the maximum value F max As the upper limit value XA1 of the analysis item parameter that must stop working, the minimum value F min As the lower limit value XB1 at which the analysis item parameter must stop working, when the standard deviation U is greater than the preset value Y1, the mean of the absolute values ​​of the differences between each Fn and the mean Fp is obtained and marked as the deviation value P, the difference between the mean Fp and the deviation value P is taken as the lower limit value XB1 at which the analysis item parameter must stop working, and the sum of the mean Fp and the deviation value P is taken as the upper limit value XA1 at which the analysis item parameter must stop working, wherein the specific value of the preset value Y is formulated by relevant personnel according to actual needs;

[0044] S2: adopt the same method as that of obtaining the lower limit value XB1 and the upper limit value XA1 of the analysis item parameter at which the operation must be stopped, analyze the parameter values ​​of various parameters of the metrological production equipment when a fault occurs, and obtain the lower limit value XBi and the upper limit value XAi corresponding to the time when each parameter must stop working according to the analysis results, wherein i refers to different parameter items of the metrological production equipment, i is a positive integer, i=1, 2, ..., e, e is the number of parameter items of the metrological production equipment, e≥1;

[0045] By analyzing historical fault data, the lower and upper limits of various parameters at which they must stop working are obtained. This enables accurate judgment of when equipment parameters are in a dangerous state in practical applications, providing a clear basis for timely maintenance measures.

[0046] Step 3: Obtain and analyze the normal operating values ​​corresponding to each parameter of the measurement production equipment, and obtain the standard values ​​corresponding to each parameter of the production equipment under normal working conditions according to the analysis results. The specific method is as follows:

[0047] S01: Select the same parameter item as in step S1 as the analysis item parameter;

[0048] Obtain the numerical value corresponding to each analysis item parameter when the metrological production equipment operates normally r times, and mark it as Er, where r is the preset number, r is a positive integer, and r≥1;

[0049] Get the value Ee in Er that meets the preset screening condition A1, where e is the number of values ​​in Er that meet the preset screening condition A1, r≥e≥1, compare the number e with the preset value Y2, when the number e is greater than the preset value Y2, it means that the number of values ​​in Er that meet the preset screening condition A1 is large, and the mean value of Er is representative, and then the mean value Ep of Er is used as the standard value D1 corresponding to the analysis item parameter under normal working conditions, when the number e is less than or equal to the preset value Y2, the mean of the maximum and minimum values ​​in Er is used as the standard value D1 corresponding to the analysis item parameter under normal working conditions, that is, D1=(E min +E max ) / 2, where E max and E min are the maximum and minimum values ​​in En respectively; here, the preset screening condition A1 is specifically: |Er-Ep|≥Y3, where Y2 and Y3 are both preset values, and the specific values ​​of Y2 and Y3 are formulated by relevant personnel according to actual needs;

[0050] S02: Analyze and obtain the standard values ​​D i corresponding to the various parameters of the production equipment under normal working conditions in the same way as obtaining the standard values ​​D1 corresponding to the analysis item parameters under normal working conditions;

[0051] By analyzing the normal operating values ​​of various parameters of metrological production equipment, the standard values ​​of various parameters under normal working conditions are obtained, which helps to monitor whether the equipment parameters are within the normal range in daily operation and timely discover potential abnormal situations.

[0052] Step 4: Analyze the number of times each parameter exceeds the limit when the measurement production equipment fails b times, and obtain the correlation coefficient between each parameter and the measurement production equipment. The specific method is as follows:

[0053] First, the parameter intervals [XAi, XB i] corresponding to each parameter are generated according to the lower limit value and upper limit value of each parameter that must stop working, and the number of times each parameter does not belong to its corresponding parameter interval when the metrological production equipment has b failures is obtained, and it is marked as the number of over-limit times Gi corresponding to each parameter;

[0054] The ratio between the number of times of exceeding the limit Gi and b corresponding to each parameter is taken as the correlation coefficient Hi corresponding to each parameter;

[0055] The number of times each parameter exceeds the limit when the metrological production equipment fails is analyzed, and the correlation coefficient between each parameter and the metrological production equipment is obtained;

[0056] Step 5: Obtain the real-time operating values ​​of various parameters of the measurement production equipment, and perform calculations and analysis based on the lower limit value, upper limit value, standard value and correlation coefficient at which each parameter must stop working, and then obtain the degradation degree corresponding to each parameter. The specific method is as follows:

[0057] The real-time running numbers of various parameters of the metering production equipment are marked as Zi, and the formula is:

[0058] or Calculate and obtain the degradation degree Mi corresponding to each parameter of the metrological production equipment;

[0059] By combining the real-time operating values, upper and lower limits, standard values ​​and correlation coefficients to calculate the degradation degree of each parameter, we can have a more comprehensive understanding of the operating status of the equipment.

[0060] Step 6: According to the degradation degree analysis corresponding to each parameter of the metrological production equipment, the equipment degradation coefficient corresponding to the metrological production equipment is obtained, and the fault warning signal is generated according to the equipment degradation coefficient. The specific method is as follows:

[0061] pass Calculate and obtain the equipment degradation coefficient Q corresponding to the metrological production equipment. When Q is greater than the warning threshold Y4, a fault warning signal is generated. Otherwise, no peak sum processing is performed. Here, e≥f≥1, Y4=0.82;

[0062] When the equipment degradation coefficient is greater than the preset value, a fault warning signal is generated to prompt maintenance personnel to conduct inspections or maintenance in a timely manner. This provides maintenance personnel with sufficient time to prepare for maintenance work, significantly reducing maintenance costs, helping to reduce unexpected equipment downtime, and improving overall production efficiency and equipment utilization. Timely fault prediction can also prevent accidents caused by equipment failure, ensure the safety of production processes and employees, and improve overall production efficiency and equipment utilization: Timely fault prediction prevents accidents caused by equipment failure, ensuring the safety of production processes and employees.

[0063] Embodiment 2

[0064] As the second embodiment of the present invention, when the present application is implemented, compared with the first embodiment, the technical solution of the present embodiment is different from the first embodiment only in that the present embodiment further includes step seven:

[0065] Step 7: By setting a degradation coefficient crossing value lower than the warning threshold, analyze the time from the degradation coefficient crossing value to the warning threshold of the equipment degradation coefficient of the metrological production equipment, and obtain the fault prediction duration. When the equipment degradation coefficient of the metrological production equipment exceeds the degradation coefficient crossing value, the fault prediction duration is displayed. The specific method is as follows:

[0066] Set a degradation coefficient crossing value Y5 that is lower than the warning threshold value Y4, where Y5 is 0.72, and obtain the time Tg required for the equipment degradation coefficient of the measurement production equipment to reach the warning threshold value Y4 from exceeding the degradation coefficient crossing value Y5 for multiple times, where g is the number of acquisitions, g is a positive integer, and g≥1;

[0067] By formula The discrete value W of Tg is calculated, where Tp is the mean value of Tg, g≥h≥1. When the discrete value W is less than the preset value Y6, the mean value Tp of Tg is used as the fault prediction time L. When W is greater than or equal to the preset value Y6, the mean value of the maximum and minimum values ​​in Tg is used as the fault prediction time L, that is, L=(T max +T min ) / 2, calculate the fault prediction time L, where T max is the maximum value in Tg, T min It is the minimum value in Tg, and the specific value of Y6 is set by relevant personnel according to specific application scenarios and requirements;

[0068] The equipment degradation coefficient of the measurement production equipment is calculated and obtained in real time. When the equipment degradation coefficient is greater than the degradation coefficient crossing line value Y5, the fault prediction time L is displayed as a reminder;

[0069] By setting the degradation coefficient over-limit value and warning threshold, the equipment degradation coefficient is calculated in real time. When the over-limit value is exceeded, the fault prediction duration is displayed, providing maintenance personnel with clear warning information and time reference, improving the accuracy and timeliness of fault prediction, and providing maintenance urgency information. It can effectively predict the potential failures of metrological production equipment and provide accurate guidance for equipment maintenance and management, thereby reducing downtime, improving production efficiency, and ensuring production safety. By accurately calculating the fault prediction duration, the time point when the equipment may fail can be predicted in advance, providing accurate time reference for preventive maintenance of the equipment, reducing downtime caused by sudden failures, and ensuring continuous and stable operation of the equipment through timely maintenance, improving overall production efficiency, avoiding safety accidents that may be caused by equipment failure, and ensuring the safety of the production environment.

[0070] Embodiment 3

[0071] As the third embodiment of the present invention, when the present application is specifically implemented, compared with the first and second embodiments, the technical solution of this embodiment is to combine the solutions of the first and second embodiments mentioned above for implementation.

[0072] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters and thresholds in the formula are set by technicians in this field according to actual conditions.

[0073] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A method for fault prediction and status evaluation of metrological production equipment based on digital twins, characterized in that: The following steps are involved: Step 1: Obtain the parameter values ​​corresponding to each parameter when the measurement production equipment fails; Step 2: Analyze the parameter values ​​corresponding to each parameter when the metering production equipment fails, and obtain the lower and upper limits of each parameter that must stop working: Step 3: Analyze the normal operating values ​​corresponding to each parameter of the measurement production equipment, and obtain the standard values ​​corresponding to each parameter of the production equipment under normal working conditions; Step 4: Analyze the number of times each parameter exceeds the limit when the measurement production equipment fails b times, and obtain the correlation coefficient between each parameter and the measurement production equipment; Step 5: Calculate the degradation degree corresponding to each parameter by combining the real-time operating value of each parameter with the lower limit value, upper limit value, standard value and correlation coefficient at which each parameter must stop working; Step 6: Measure the equipment degradation coefficient corresponding to the production equipment, and generate a fault warning signal based on the equipment degradation coefficient; Step 7: By setting a degradation coefficient crossing value lower than the warning threshold, analyze the time from the degradation coefficient crossing value to the warning threshold of the equipment degradation coefficient of the metrological production equipment, and obtain the fault prediction duration. When the equipment degradation coefficient of the metrological production equipment exceeds the degradation coefficient crossing value, the fault prediction duration is displayed.

2. The method for fault prediction and status evaluation of metrological production equipment based on digital twin according to claim 1 is characterized in that: The specific method for obtaining the lower and upper limits of each parameter at which it must stop working is as follows: S1: Select any one of the parameters as the analysis item parameter, obtain the parameter value Fn corresponding to each time when the measurement production equipment fails n times, where n is the preset number of times, n is a positive integer, n≥1, calculate the standard deviation U of Fn, analyze the standard deviation U, and obtain the lower limit value XB1 and upper limit value XA1 of the analysis item parameter that must stop working according to the analysis results; S2: In the same way as obtaining the lower limit value XB1 and upper limit value XA1 at which the analysis item parameters must stop working, analyze the parameter values ​​of various parameters of the metrological production equipment when a fault occurs, and obtain the lower limit value XBi and upper limit value XAi corresponding to the time when each parameter must stop working according to the analysis results, where i refers to different parameter items of the metrological production equipment, i is a positive integer, i=1, 2, ..., e, e is the number of parameter items of the metrological production equipment, e≥1.

3. The method for fault prediction and status evaluation of metrological production equipment based on digital twin according to claim 2 is characterized in that: The specific method of obtaining the lower limit value XB1 and the upper limit value XA1 at which the analysis item parameter must stop working is: When the standard deviation U is less than or equal to the preset value Y1, the maximum and minimum values ​​in Fn are respectively used as the upper limit value XA1 and the lower limit value XB1 at which the analysis item parameters must stop working; when the standard deviation U is greater than the preset value Y1, the mean of the absolute values ​​of the differences between each Fn and the mean Fp is marked as the deviation value P, and the difference between the mean Fp and the deviation value P is used as the lower limit value XB1 at which the analysis item parameters must stop working; at the same time, the sum of the mean value of Fn and the deviation value P is used as the upper limit value XA1 at which the analysis item parameters must stop working.

4. The method for fault prediction and status evaluation of metrological production equipment based on digital twin according to claim 3 is characterized in that: The specific method for obtaining the standard values ​​corresponding to each parameter of the production equipment under normal working conditions is: S01: Select the same parameter item as that in step S1 as the analysis item parameter, and obtain the value Er of the analysis item parameter corresponding to the normal operation of the measurement production equipment r times, where r is the preset number of times, r is a positive integer, and r≥1; obtain the value Ee in Er that satisfies |Er-Ep|≥Y3, where e is the number of values ​​in Er that meet the preset screening condition A1, r≥e≥1, when the number e is greater than the preset value Y2, the mean value Ep of Er is used as the standard value D1 corresponding to the analysis item parameter under normal working conditions, and when the number e is less than or equal to the preset value Y2, the mean of the maximum and minimum values ​​in Er is used as the standard value D1 corresponding to the analysis item parameter under normal working conditions, where Y2 and Y3 are both preset values; S02: In the same manner as obtaining the standard value D1 corresponding to the analysis item parameter under normal working conditions, the standard values ​​Di corresponding to the various parameters of the production equipment under normal working conditions are analyzed and obtained.

5. The method for fault prediction and status evaluation of metrological production equipment based on digital twin according to claim 4 is characterized in that: The specific method of obtaining the correlation coefficient between each parameter and the measurement production equipment is: First, the parameter intervals [XAi, XBi] corresponding to each parameter are generated according to the lower limit value and upper limit value at which each parameter must stop working. The number of times each parameter does not belong to its corresponding parameter interval when b failures occur in the metrological production equipment is obtained, and it is marked as the number of over-limit times Gi corresponding to each parameter. The ratio between the number of over-limit times Gi corresponding to each parameter and b is used as the correlation coefficient Hi corresponding to each parameter.

6. The method for fault prediction and status evaluation of metrological production equipment based on digital twin according to claim 5 is characterized in that: The specific method of obtaining the degradation degree corresponding to each parameter is: The real-time running numbers of various parameters of the metering production equipment are marked as Zi, and the formula is: Calculate and obtain the degradation degree Mi corresponding to each parameter of the metrological production equipment.

7. The method for fault prediction and status evaluation of metrological production equipment based on digital twin according to claim 6 is characterized in that: The specific method of generating fault warning signals based on the equipment degradation coefficient is as follows: pass The equipment degradation coefficient Q corresponding to the metrological production equipment is calculated and obtained. When Q is greater than the warning threshold Y4, a fault warning signal is generated. Here, e≥f≥1, Y4=0.

82.

8. The method for fault prediction and status evaluation of metrological production equipment based on digital twin according to claim 7 is characterized in that: The specific method for obtaining the fault prediction duration is as follows: Set a degradation coefficient crossing value Y5 that is lower than the warning threshold Y4, and obtain the time Tg required for the equipment degradation coefficient of the metrological production equipment to reach the warning threshold Y4 from exceeding the degradation coefficient crossing value Y5 for multiple times, and calculate the discrete value W of Tg. When the discrete value W is less than the preset value Y6, the mean Tp of Tg is used as the fault prediction time L. When W is greater than or equal to the preset value Y6, the mean of the maximum and minimum values ​​in Tg is used as the fault prediction time L, where g is the number of acquisitions, g is a positive integer, g≥1, and Y5 is 0.72.

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