Residual service life prediction method and system of safety control system

Abstract

The invention provides a residual service life prediction method and system of a safety control system, and belongs to the technical field of computers. The method comprises the following steps: based on a plurality of different truncation moments, obtaining a plurality of equipment life test data in a non-failure equipment state; obtaining an estimated value of the equipment failure probability corresponding to each truncation moment based on the service life test data of each piece of equipment; based on the multiple pieces of equipment life test data and the estimated value of the equipment failure probability corresponding to each truncation moment, performing parameter estimation of a pre-constructed Weibull distribution model to obtain a parameter estimated value of the equipment life obeying Weibull distribution; and predicting the residual service life of the target safety control system based on the parameter estimation value and the operation rule of the double N-K safety control system. The purpose of accurately predicting the reliability and the residual life of the double N-K safety control system under the condition of no failure data is achieved.

Description

Technical Field

[0001] The present invention relates to the field of computer technology, and in particular to a method for predicting the remaining useful life of a safety control system and a system for predicting the remaining useful life of a safety control system. Background Art

[0002] With the continuous development of the economy, the number of petrochemical plants in my country continues to increase. Thanks to the improvement of the automation level of the plant, the probability of accidents caused by equipment, environment, materials, etc. has been significantly reduced, but accidents caused by management and human factors still occur from time to time. Since the current development trend of petrochemical plants is large-scale and complex, once an accident occurs, it often causes huge economic losses and a large number of casualties. Therefore, chemical companies are equipped with safety control systems (SIS) for high-risk plants in order to prevent further development of accidents when danger occurs, thereby reducing the frequency of danger.

[0003] Due to its working characteristics, the SIS system is in standby state most of the time when there is no danger in the industrial device, so the probability of being detected when it fails is low. Once a dangerous failure of SIS occurs, its ability to reduce risks will disappear, so it is particularly important to obtain the reliability data and remaining life of the SIS system.

[0004] In order to improve its reliability, SIS systems often use redundant architectures. The most common architecture is the K-out-of-N (KooN) structure, that is, N identical devices are running at the same time. As long as K or more of them can work properly, the system can operate normally, including 1oo2 structure, 2oo2 structure and 2oo3 structure. In addition, there is currently a two-out-of-N redundant structure, where "two-out-of-N" means that two sets of redundant hardware devices are running simultaneously in the main and backup modes. When the main device fails, it can be switched to the backup device at any time, thereby improving reliability. These complex redundant structures greatly increase the difficulty of calculating reliability and remaining life.

[0005] At present, the main methods for predicting equipment life include methods based on expert experience, methods based on machine learning, and methods based on statistics. Among them, the empirical method is too dependent on personal experience and has low accuracy; the machine learning-based method requires a large amount of historical degradation data of the same equipment as support, but due to the high requirements of the SIS system, it is too difficult to obtain a large amount of relevant data; the statistical method requires historical life data of the equipment, which is also difficult to obtain for SIS systems with higher reliability.

[0006] In the existing research, only the N out of K system has been analyzed to a certain extent, and the reliability and remaining life prediction of the double N out of K system has not been involved. In addition, due to the high reliability of the SIS system, it is often difficult to obtain the corresponding failure data in the life test. Therefore, predicting the reliability and remaining life of the double N out of K redundant structure without failure data is an urgent problem to be solved. Summary of the invention

[0007] The purpose of the embodiments of the present invention is to provide a method and system for predicting the remaining useful life of a safety control system, so as to at least solve the above-mentioned problem of failing to accurately predict the reliability and remaining useful life of a two-by-N-out-of-K redundant structure under zero-failure data.

[0008] In order to achieve the above object, the present invention provides a method for predicting the remaining service life of a safety control system in a first aspect, comprising:

[0009] Based on multiple different truncation times, multiple equipment life test data in a non-failure equipment state are obtained;

[0010] Based on the life test data of each device, the estimated value of the device failure probability corresponding to each truncation time is obtained;

[0011] Based on multiple equipment life test data and the estimated values ​​of equipment failure probabilities corresponding to each truncation time, the parameters of the pre-built Weibull distribution model are estimated to obtain the parameter estimates of the equipment life following the Weibull distribution;

[0012] Based on the parameter estimation and the K-by-N safety control system operation rules, the remaining useful life of the target safety control system is predicted.

[0013] Optionally, the above equipment life test data includes the number of non-failed equipment;

[0014] Based on multiple different truncation times, multiple equipment life test data are obtained in the non-failure equipment state, including:

[0015] Divide all devices into multiple test groups, each test group includes at least one device;

[0016] Configure corresponding timed censoring tests for each test group, with different censoring times for each test group;

[0017] Based on the corresponding censoring time, life test is conducted for each test group;

[0018] When the life test is completed and all equipment has not failed, the equipment life test data (τ i ,s i ),i=1,2,…m, where m is the number of experimental groups, s iIndicated in τ i The number of devices that have not failed at the moment.

[0019] Optionally, obtaining the estimated value of the equipment failure probability corresponding to each truncation time based on the life test data of each equipment includes:

[0020] Obtain the estimated value of the equipment failure probability corresponding to each censoring time based on order statistics

[0021] Optionally, the above-mentioned estimated value of the equipment failure probability corresponding to each truncation time is obtained based on the order statistics The calculation formula is as follows:

[0022] in, Indicated in τ i The estimated value of the probability of equipment failure at time t, m is equal to the number of test groups, s i Indicated in τ i The number of devices that have not failed at the moment.

[0023] Optionally, the parameter estimation of the pre-constructed Weibull distribution model based on the multiple equipment life test data and the estimated values ​​of the equipment failure probabilities corresponding to each truncation time includes:

[0024] The weighted least squares method is used to estimate the parameters of the Weibull distribution model;

[0025] The parameters of the Weibull distribution model include shape parameter and scale parameter.

[0026] Optionally, the Weibull distribution model includes a Weibull distribution probability density function and a cumulative distribution function;

[0027] Among them, the probability density function of Weibull distribution is The cumulative distribution function is t is the equipment operation time, β>0 is the shape parameter, and η>0 is the scale parameter.

[0028] Optionally, the above parameter estimates include shape parameter estimates and the scale parameter estimates

[0029] The parameter estimation of the Weibull distribution model is performed using the weighted least squares method, including:

[0030] Arrange the cumulative distribution function into a linear function;

[0031] Designing the weight of the weighted least square method based on the censoring moment of each test group, and determining the objective function of the weighted least square method based on the weight of the weighted least square method;

[0032] The parameter estimation of the Weibull distribution model is performed based on the linear function and the objective function of the weighted least squares method. The estimation formula is:

[0033]

[0034] Among them, X and Y are arrays of linear function variables;

[0035] W is a diagonal matrix with the weights of the weighted least squares method as diagonal elements;

[0036] is the predicted value of the first parameter of the linear function, μ = lnη;

[0037] is the predicted value of the first parameter of the linear function,

[0038] Optionally, the above-mentioned prediction of the remaining useful life of the target safety control system based on the parameter estimation value and the K-by-N safety control system operation rule includes:

[0039] Based on the estimated parameter values, the Weibull distribution model of equipment life is obtained;

[0040] Based on the operation rules of the double-N-take-K safety control system, the remaining service life of the target safety control system is predicted through the Weibull distribution model obeyed by the equipment life.

[0041] Optionally, the above-mentioned safety control system operation rule based on the squared N out of K is used to predict the remaining service life of the target safety control system through the Weibull distribution model obeyed by the equipment life, including:

[0042] According to the working principle of KooN safety control system and the operating rules of K-by-N safety control system, the distribution function of the target safety control system fault is determined;

[0043] Based on the distribution function of the target safety control system failure, determine the probability density function of the target safety control system failure;

[0044] The expectation of the probability density function of failure is calculated to obtain the expected model of the remaining service life of the target safety control system after being put into use for δ time, which is expressed as:

[0045]

[0046] Where E(T) is the expected value of the remaining service life of the target safety control system after it has been put into use for δ time;

[0047] f 2s (T) is the probability density function of failure of the target safety control system.

[0048] A second aspect of the present invention provides a remaining useful life prediction system for a safety control system, comprising:

[0049] An equipment life test data acquisition module, used for acquiring a plurality of equipment life test data in a non-failure equipment state based on a plurality of different truncation moments;

[0050] The equipment failure probability estimation module is used to obtain the estimated value of the equipment failure probability corresponding to each truncation time based on the life test data of each equipment;

[0051] A model parameter estimation module is used to estimate the parameters of a pre-built Weibull distribution model based on multiple equipment life test data and the estimated values ​​of equipment failure probabilities corresponding to each truncation time, and obtain the parameter estimation value that the equipment life follows the Weibull distribution;

[0052] The life prediction module is used to predict the remaining service life of the target safety control system based on parameter estimation values ​​and the K-by-N safety control system operation rules.

[0053] In a third aspect of the present invention, a machine-readable storage medium is provided, on which instructions are stored. When the instructions are executed by a processor, the processor is configured to execute the remaining useful life prediction method of the safety control system.

[0054] In a fourth aspect of the present invention, an electronic device is provided, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the remaining useful life prediction method of the safety control system when executing the computer program.

[0055] Through the above technical scheme, a method and system for predicting the remaining service life of a safety control system is provided. The equipment groups of a target safety control system (i.e., a two-by-N take K safety control system) are subjected to timed truncated life tests at different truncation times to obtain a plurality of equipment life test data in a non-failure equipment state. Based on the life test data of each device, the failure probability of the equipment at each truncation time is estimated. Based on the multiple equipment life test data and the estimated value of the equipment failure probability corresponding to each truncation time, the parameters of the Weibull distribution model are estimated to obtain the shape parameter estimation value and the scale parameter estimation value, thereby obtaining the Weibull distribution model that the equipment life obeys. Combining the operation rules of the two-by-N take K safety control system and the Weibull distribution model that the equipment life obeys, according to the definition of the distribution function, the distribution function of the target safety control system failure is obtained, thereby obtaining the probability density function of the failure of the target safety control system, and the expected value of the life of the target safety control system is obtained by integrating the probability density function of the failure, thereby obtaining the remaining service life of the target safety control system. The purpose of accurately predicting the reliability and remaining service life of the two-by-N take K safety control system without failure data is achieved.

[0056] Other features and advantages of the embodiments of the present invention will be described in detail in the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] The accompanying drawings are used to provide a further understanding of the embodiments of the present invention and constitute a part of the specification. Together with the following specific embodiments, they are used to explain the embodiments of the present invention, but do not constitute a limitation on the embodiments of the present invention. In the accompanying drawings:

[0058] Figure 1 It is a flow chart of a method for predicting the remaining useful life of a safety control system provided by one embodiment of the present invention;

[0059] Figure 2 It is a schematic diagram of the distribution probability density function that the equipment obeys;

[0060] Figure 3 is a schematic diagram of the cumulative distribution function that the device obeys;

[0061] Figure 4 It is a block diagram of a remaining useful life prediction system for a safety control system provided by one embodiment of the present invention;

[0062] Figure 5 It is a schematic diagram of the structure of an electronic device provided by a preferred embodiment of the present invention.

[0063] Description of Reference Numerals

[0064] 10 - electronic device, 100 - processor, 101 - memory, 102 - computer program. DETAILED DESCRIPTION

[0065] The specific implementation of the present invention is described in detail below in conjunction with the accompanying drawings. It should be understood that the specific implementation described here is only used to illustrate and explain the present invention, and is not used to limit the present invention.

[0066] Example 1

[0067] Figure 1 FIG. 1 is a flowchart of a method for predicting the remaining useful life of a safety control system provided by one embodiment of the present invention. Figure 1 As shown, an embodiment of the present invention provides a method for predicting the remaining useful life of a safety control system, comprising:

[0068] S110: obtaining a plurality of equipment life test data in a non-failure equipment state based on a plurality of different truncation moments;

[0069] Among them, this method uses the above-mentioned multiple equipment life test data as the data basis for subsequent calculation of the estimated value of the equipment failure probability corresponding to each truncation time, parameter estimation of the Weibull distribution model, and prediction of the remaining service life of the target safety control system, which can effectively reduce the demand for data.

[0070] In some implementations of this embodiment, the equipment life test data includes the number of non-failed equipment; the multiple equipment life test data obtained in the non-failed equipment state based on multiple different truncation moments include:

[0071] Divide all devices into multiple test groups, each test group includes at least one device;

[0072] Specifically, n devices are divided into m groups, and the number of devices is n 1 、n 2 ,…,n m There are m test groups; among them, n devices form a double N take K safety control system, n 1 +n 2 +…+n m =n.

[0073] Configure corresponding timed censoring tests for each test group, with different censoring times for each test group;

[0074] Specifically, a corresponding timed truncation test is configured for each test group; wherein the number of devices is n 1 The censoring time of the timed censoring test corresponding to the experimental group is τ 1 , the number of devices is n 2The censoring time of the timed censoring test corresponding to the experimental group is τ 2 , ..., the number of devices is n m The censoring time of the timed censoring test corresponding to the experimental group is τ m , and τ 1 <τ 2 <…<τ m .

[0075] Based on the corresponding censoring time, life test is conducted for each test group;

[0076] When the life test is completed and all equipment has not failed, the equipment life test data (τ i ,s i ),i=1,2,…m, where m is the number of experimental groups, s i Indicated in τ i The number of non-failed devices that have not failed at the time. i =n i +n i+1 +…+n m .

[0077] S120: based on the life test data of each device, obtaining an estimated value of the device failure probability corresponding to each truncation time;

[0078] In some implementations of this embodiment, the above-mentioned obtaining the estimated value of the equipment failure probability corresponding to each truncation time based on each equipment life test data includes: obtaining the estimated value of the equipment failure probability corresponding to each truncation time based on the order statistic Specifically, the failure probability of the equipment is estimated based on order statistics when no failure data is obtained in the life test, so as to further perform parameter estimation and life prediction, which can be used for systems with high equipment reliability and difficult to obtain failure data.

[0079] In some implementations of this embodiment, the estimated value of the equipment failure probability corresponding to each truncation time obtained based on the order statistics is The calculation formula is as follows: in, Indicated in τ i The estimated value of the probability of equipment failure at time t, m is equal to the number of test groups, s i Indicated in τ i The number of devices that have not failed at the moment.

[0080] S130: Based on multiple equipment life test data and the estimated values ​​of equipment failure probabilities corresponding to each truncation time, perform parameter estimation of a pre-constructed Weibull distribution model to obtain parameter estimation values ​​that the equipment life follows the Weibull distribution;

[0081] In some implementations of the present embodiment, the above-mentioned parameter estimation of the pre-constructed Weibull distribution model based on multiple equipment life test data and the estimated value of the equipment failure probability corresponding to each truncation time includes: using the weighted least squares method to estimate the parameters of the Weibull distribution model; wherein the parameters of the Weibull distribution model include shape parameters and scale parameters.

[0082] In some implementations of this embodiment, the above-mentioned Weibull distribution model includes a Weibull distribution probability density function and a cumulative distribution function; wherein the Weibull distribution probability density function is The cumulative distribution function is t is the equipment operation time, β>0 is the shape parameter, and η>0 is the scale parameter.

[0083] In some implementations of this embodiment, the above parameter estimates include shape parameter estimates and the scale parameter estimates The parameter estimation of the Weibull distribution model is performed using the weighted least squares method, including:

[0084] Arrange the cumulative distribution function into a linear function;

[0085] Specifically, the cumulative distribution function is deformed to obtain the first deformation formula of ln(-ln(1-Ft=βlnt-lnη. Let x=ln-ln1-Ft, y=lnt, σ=1β, μ=lnη, and the first deformation formula is deformed to obtain the second deformation formula of y=σx+μ. Substitute multiple equipment life test data obtained from the life test (each equipment life test data contains zero failure data) into the above formula to obtain y i =lnτ i , i=1,2,…,m.

[0086] Designing the weight of the weighted least square method based on the censoring moment of each test group, and determining the objective function of the weighted least square method based on the weight of the weighted least square method;

[0087] Specifically, the weights of the weighted least squares method are configured according to the truncation time, and we get make The objective function of the weighted least squares method is in,

[0088] The parameter estimation of the Weibull distribution model is performed based on the linear function and the objective function of the weighted least squares method. The estimation formula is: Where X and Y are arrays of linear function variables; W is a diagonal matrix with weights of the weighted least squares method as diagonal elements; is the predicted value of the first parameter of the linear function, μ = lnη; is the predicted value of the first parameter of the linear function,

[0089] Specifically, based on the objective function of weighted least squares method, let Get an estimate of θ for Where W = diag(ω 1 ,ω 2 ,…,ω m ) is the weight ω i is a diagonal matrix with diagonal elements. Finally, according to the equation y = lnt and the equation The shape parameter estimates can be obtained and the scale parameter estimates

[0090] S140: Predicting the remaining useful life of the target safety control system based on the parameter estimation value and the K-by-N safety control system operation rule.

[0091] In some implementations of the present embodiment, the above-mentioned prediction of the remaining service life of the target safety control system based on parameter estimation values ​​and double-N-take-K safety control system operating rules includes: based on the parameter estimation values, obtaining a Weibull distribution model that the equipment life obeys; based on the double-N-take-K safety control system operating rules, predicting the remaining service life of the target safety control system through the Weibull distribution model that the equipment life obeys.

[0092] Specifically, the shape parameter estimate and the scale parameter estimates By replacing β and η in the probability density function and cumulative distribution function of the Weibull distribution, the Weibull distribution model obeyed by the equipment life is obtained. The remaining service life of the target safety control system can be predicted by combining the Weibull distribution model obeyed by the equipment life with the operation rules of the double-N-take-K safety control system.

[0093] In some implementations of this embodiment, the above-mentioned safety control system operation rule based on double N out of K is used to predict the remaining service life of the target safety control system through the Weibull distribution model obeyed by the equipment life, including:

[0094] According to the working principle of the KooN type safety control system and the operating rules of the double-N-take-K safety control system, the distribution function of the target safety control system failure is determined; based on the distribution function of the target safety control system failure, the probability density function of the target safety control system failure is determined; the expectation of the probability density function of failure is calculated to obtain the expected model of the remaining service life of the target safety control system after being put into use for δ time, which is expressed as: Where E(T) is the expected value of the remaining service life of the target safety control system after it is put into use for δ time; f 2s (T) is the probability density function of failure of the target safety control system.

[0095] Among them, the target safety control system is a two-times-N-take-K safety control system.

[0096] Specifically, according to the working principle of the KooN type safety control system, it is determined that when at least N-K+1 devices in the KooN type safety control system fail, the KooN type safety control system is in a system failure state. Then the distribution function F of the KooN type safety control system failure is s (t) is Where N represents that there are N identical devices running simultaneously in the KooN type safety control system, and K represents that there are K devices running normally among the N identical devices. The distribution function F of the KooN type safety control system fault is s (t) Take the derivative and get the probability density function f of KooN type safety control system failure s (t) is Since the 2xN-out-of-K safety control system is composed of two KooN type safety control systems, the 2xN-out-of-K safety control system will fail only when at least N-K+1 devices in the two KooN type safety control systems fail. Therefore, the distribution function of the 2xN-out-of-K system failure is F 2s (t) is: F 2s (t) = F s (t) 2 The distribution function F of the safety control system for quadratic N and K is 2s (t) By taking the derivative, we can get the probability density function f of the safety control system failure when N is K. 2s (t) is f 2s (t) = 2F s (t)f s (t). Finally, the probability density function f of the safety control system failure is obtained by taking K for the quadratic N. 2s (t) Calculate the expectation, and we can get the expected value of the remaining service life of the target safety control system after it is put into use for δ time:

[0097] In the above implementation process, the method performs a timed truncated life test with different truncation times on the equipment groups of the target safety control system (i.e., the double-N-take-K safety control system) to obtain a plurality of equipment life test data in a non-failure equipment state. Based on the life test data of each device, the failure probability of the device at each truncation time is estimated using order statistics. Then, based on the multiple equipment life test data and the estimated value of the equipment failure probability corresponding to each truncation time, the weighted least squares method is used to estimate the parameters of the Weibull distribution model, and the shape parameter estimation value and the scale parameter estimation value are obtained, thereby obtaining the Weibull distribution model that the equipment life obeys. Combining the operation rules of the double-N-take-K safety control system and the Weibull distribution model that the equipment life obeys, according to the definition of the distribution function, the distribution function of the target safety control system failure is obtained, thereby obtaining the probability density function of the failure of the target safety control system, and the expected value of the life of the target safety control system is obtained by integrating the probability density function of the failure, and then the remaining life of the target safety control system is obtained. This method estimates the failure probability of equipment in the case of no failure data in the life test based on order statistics, and uses the weighted least squares method to estimate the parameters of the Weibull distribution obeyed by the equipment to calculate the remaining life of the two-by-N-out-of-K system in the case of no failure data. This method achieves the purpose of accurately predicting the reliability and remaining life of the two-by-N-out-of-K safety control system in the case of no failure data.

[0098] It should be noted that this method can be widely used in the life prediction of safety control systems with a two-by-N-take-K structure and a component equipment life that follows a Weibull distribution. This method can establish a reliability model and calculate the expected value of the remaining life for a system that follows the above two-by-N-take-K structure and a device life that follows a Weibull distribution, guide the timely maintenance of component equipment in the system, reduce the high cost of excessive maintenance, and prevent safety accidents caused by untimely maintenance.

[0099] Example 2

[0100] This embodiment provides a method for predicting the remaining life of a 2x2 type safety control system, and the specific process is as follows:

[0101] Step 1: Life Test Obtain equipment life test data which may include right-censored data.

[0102] A total of 1000 devices that form a 2x2-1 system are randomly divided into 20 groups with truncation times of τ 1 ,τ 2 ,…,τ 20 Life test, and τ 1 <τ 2 <…<τ 20At the end of the test, all equipment had no failures, and 20 data were obtained as shown in Table 1:

[0103] Table 1 Life test data of 20 groups of equipment (unit: h)

[0104]

[0105] In Table 1, when the censoring time of the first group is 5356, the 40 devices in the first group have not failed, so the total number of non-failures is 1000. The next step is to remove the 40 devices in the first group, and the total number of devices in the initial life test is 960, which are the devices in groups 2 to 20.

[0106] The number of samples in each group is n. i , the total number of unfailed i , i = 1, 2, ..., 20. For example, the number of samples in the first group is 40, that is, n 1 is 40, while the total number of non-failures in the first group is 1000.

[0107] Step 2: Estimate the equipment failure rate based on order statistics, the formula is as follows:

[0108]

[0109] Get an estimate of the failure rate As shown in Table 2:

[0110] Table 2 Estimated failure rates of 20 groups of equipment

[0111]

[0112] Step 3: Based on the weighted least squares method, the estimated values ​​of the parameters β and η of the device's distribution are obtained.

[0113] Furthermore, the Weibull distribution probability density function that the device obeys is:

[0114]

[0115] The cumulative distribution function is:

[0116]

[0117] The distribution probability density function of the device is as follows: Figure 2 As shown, the cumulative distribution function of the device is as follows Figure 3 As shown, we can see that at the censoring time τ of each trial 1 ,τ 2 ,…,τ 20 The failure rate of this distribution is low, so the absence of failures is acceptable.

[0118] Step 4: Calculate the expected value of the remaining useful life of the 2x2-to-1 system.

[0119] Since the system is a two-by-two-take-1 structure, N=2, K=1, and the failure probability density function of the redundant system is:

[0120]

[0121] Therefore, the expected value of the remaining service life after the system has been running for δ time can be obtained based on the following formula: The average life of the system is:

[0122]

[0123] Calculating the above formula, we can get the average life of the system to be 41992 hours. To obtain the remaining life, we only need to subtract the current operating time from the calculated average life.

[0124] Example 3

[0125] This embodiment provides a method for predicting the remaining life of a 2-by-3 type safety control system.

[0126] A total of 2000 devices in a 2x3-out-of-2 system are randomly divided into 40 groups with truncation times of τ 1 ,τ 2 ,…,τ 40 Life test, and τ 1 <τ 2 <…<τ 40 At the end of the test, all equipment had no failures, and 40 pieces of data were obtained as shown in Table 3:

[0127] Table 3 Life test data of 40 groups of equipment (unit: h)

[0128]

[0129] The number of samples in this group is n. i , the total number of unfailed i , i=1,2,…,40.

[0130] According to S110 to S140 of Example 1, the expected life of the redundant system can be obtained as 28244 hours. To obtain the remaining life, it is only necessary to subtract the current operating time from the calculated average life.

[0131] Example 4

[0132] Figure 4FIG. 1 is a block diagram of a remaining useful life prediction system for a safety control system provided by an embodiment of the present invention. Figure 4 As shown, an embodiment of the present invention provides a remaining useful life prediction system for a safety control system, comprising:

[0133] An equipment life test data acquisition module, used for acquiring a plurality of equipment life test data in a non-failure equipment state based on a plurality of different truncation moments;

[0134] The equipment failure probability estimation module is used to obtain the estimated value of the equipment failure probability corresponding to each truncation time based on the life test data of each equipment;

[0135] A model parameter estimation module is used to estimate the parameters of a pre-built Weibull distribution model based on multiple equipment life test data and the estimated values ​​of equipment failure probabilities corresponding to each truncation time, and obtain the parameter estimation value that the equipment life follows the Weibull distribution;

[0136] The life prediction module is used to predict the remaining service life of the target safety control system based on parameter estimation values ​​and the K-by-N safety control system operation rules.

[0137] Specifically, the system performs timed truncated life tests with different truncation times on the equipment groups of the target safety control system (i.e., the double-N-take-K safety control system) to obtain multiple equipment life test data in the state of no failure equipment. Based on the life test data of each device, the failure probability of the equipment at each truncation time is estimated using order statistics. Then, based on the multiple equipment life test data and the estimated value of the equipment failure probability corresponding to each truncation time, the weighted least squares method is used to estimate the parameters of the Weibull distribution model, and the shape parameter estimation value and the scale parameter estimation value are obtained, thereby obtaining the Weibull distribution model that the equipment life obeys. Combining the operation rules of the double-N-take-K safety control system and the Weibull distribution model that the equipment life obeys, according to the definition of the distribution function, the distribution function of the target safety control system failure is obtained, thereby obtaining the probability density function of the failure of the target safety control system, and the expected value of the target safety control system life is obtained by integrating the probability density function of failure, and then the remaining life of the target safety control system is obtained. The system estimates the failure probability of equipment in the case of no failure data in the life test based on order statistics, and uses the weighted least squares method to estimate the parameters of the Weibull distribution obeyed by the equipment to calculate the remaining life of the two-by-N-out-of-K system in the case of no failure data. This achieves the purpose of accurately predicting the reliability and remaining life of the two-by-N-out-of-K safety control system in the case of no failure data.

[0138] Example 5

[0139] An embodiment of the present invention further provides a machine-readable storage medium having instructions stored thereon. When the instructions are executed by the processor 100, the processor 100 is configured to execute the above-mentioned remaining useful life prediction method for the safety control system.

[0140] Machine-readable storage media include permanent and non-permanent, removable and non-removable media, and can be implemented by any method or technology to store information. Information can be computer-readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include temporary computer-readable media (transitory media), such as modulated data signals and carrier waves.

[0141] An embodiment of the present invention also provides an electronic device 10, which includes a memory 101, a processor 100, and a computer program 102 stored in the memory 101 and executable on the processor 100. When the processor 100 executes the computer program 102, the above-mentioned remaining service life prediction method of the safety control system is implemented.

[0142] like Figure 5 FIG. 1 is a schematic diagram of an electronic device provided by an embodiment of the present invention. Figure 5 As shown, the electronic device 10 of this embodiment includes: a processor 100, a memory 101, and a computer program 102 stored in the memory 101 and executable on the processor 100. When the processor 100 executes the computer program 102, the steps in the above method embodiment are implemented. Alternatively, when the processor 100 executes the computer program 102, the functions of each module / unit in the above device embodiment are implemented.

[0143] Exemplarily, the computer program 102 may be divided into one or more modules / units, one or more modules / units are stored in the memory 101, and are executed by the processor 100 to complete the present invention. One or more modules / units may be a series of computer program instruction segments capable of completing specific functions, and the instruction segments are used to describe the execution process of the computer program 102 in the electronic device 10. For example, the computer program 102 may be divided into a device life test data acquisition module, a device failure probability estimation module, a model parameter estimation module, and a life prediction module.

[0144] The electronic device 10 may be a computing device such as a desktop computer, a notebook, a PDA, or a cloud server. The electronic device 10 may include, but is not limited to, a processor 100 and a memory 101. Those skilled in the art will appreciate that Figure 5 It is only an example of the electronic device 10 and does not constitute a limitation of the electronic device 10. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the electronic device may also include input and output devices, network access devices, buses, etc.

[0145] The processor 100 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or the processor may be any conventional processor, etc.

[0146] The memory 101 may be an internal storage unit of the electronic device 10, such as a hard disk or memory of the electronic device 10. The memory 101 may also be an external storage device of the electronic device 10, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the electronic device 10. Further, the memory 101 may also include both an internal storage unit of the electronic device 10 and an external storage device. The memory 101 is used to store computer programs and other programs and data required by the electronic device 10. The memory 101 may also be used to temporarily store data that has been output or is to be output.

[0147] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0148] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program 102 products. Therefore, the present application may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may take the form of a computer program 102 product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0149] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program 102 products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by the computer program 102 instructions. These computer program 102 instructions can be provided to a processor 100 of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor 100 of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0150] These computer program 102 instructions may also be stored in a computer readable memory 101 that can direct a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer readable memory 101 produce an article of manufacture including an instruction device that implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0151] These computer program 102 instructions may also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for executing on the computer or other programmable device to implement the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0152] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0153] The above are only embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included within the scope of the claims of the present application.

Claims

1. A method for predicting the remaining useful life of a safety control system, characterized in that: include: Based on multiple different truncation times, multiple equipment life test data in a non-failure equipment state are obtained; Based on the life test data of each device, the estimated value of the device failure probability corresponding to each truncation time is obtained; Based on multiple equipment life test data and the estimated values ​​of equipment failure probabilities corresponding to each truncation time, the parameters of the pre-built Weibull distribution model are estimated to obtain the parameter estimates of the equipment life following the Weibull distribution; Based on the parameter estimation values ​​and the K-by-N safety control system operation rules, the remaining useful life of the target safety control system is predicted.

2. The method for predicting the remaining useful life of a safety control system according to claim 1, characterized in that: The equipment life test data includes the number of non-failed equipment; The method of obtaining a plurality of equipment life test data in a non-failure equipment state based on a plurality of different truncation moments includes: Divide all devices into multiple test groups, each test group includes at least one device; Configure corresponding timed censoring tests for each test group, with different censoring times for each test group; Based on the corresponding censoring time, life test is conducted for each test group; When the life test is completed and all equipment has not failed, the equipment life test data (τ i ,s i ),i=1,2,…m, where m is the number of experimental groups, s i Indicated in τ i The number of devices that have not failed at the moment.

3. The method for predicting the remaining useful life of a safety control system according to claim 1, characterized in that: The method of obtaining an estimated value of the equipment failure probability corresponding to each truncation time based on each equipment life test data includes: Obtain the estimated value of the equipment failure probability corresponding to each censoring time based on order statistics 4. The method for predicting the remaining useful life of a safety control system according to claim 3, characterized in that: The estimated value of the equipment failure probability corresponding to each truncation time is obtained based on the order statistics The calculation formula is as follows: in, Indicated in τ i The estimated value of the probability of equipment failure at time t, m is equal to the number of test groups, s i Indicated in τ i The number of devices that have not failed at the moment.

5. The method for predicting the remaining useful life of a safety control system according to claim 1, characterized in that: The method of estimating parameters of a pre-built Weibull distribution model based on multiple equipment life test data and estimated values ​​of equipment failure probabilities corresponding to each truncation time includes: The weighted least squares method is used to estimate the parameters of the Weibull distribution model; The parameters of the Weibull distribution model include a shape parameter and a scale parameter.

6. The method for predicting the remaining useful life of a safety control system according to claim 5, characterized in that: The Weibull distribution model includes a Weibull distribution probability density function and a cumulative distribution function; Among them, the Weibull distribution probability density function is: The cumulative distribution function is t is the equipment operation time, β>0 is the shape parameter, and η>0 is the scale parameter.

7. The method for predicting the remaining useful life of a safety control system according to claim 6, characterized in that: The parameter estimates include shape parameter estimates and the scale parameter estimates The method of using weighted least squares to estimate the parameters of the Weibull distribution model includes: Arranging the cumulative distribution function into a linear function; Designing weights of the weighted least squares method based on the truncation moments of each test group, and determining an objective function of the weighted least squares method based on the weights of the weighted least squares method; The parameter estimation of the Weibull distribution model is performed based on the linear function and the objective function of the weighted least squares method, and the estimation calculation formula is: Among them, X and Y are arrays of linear function variables; W is a diagonal matrix with the weights of the weighted least squares method as diagonal elements; is the predicted value of the first parameter of the linear function, μ = lnη; is the predicted value of the first parameter of the linear function, 8. The method for predicting the remaining useful life of a safety control system according to claim 1, characterized in that: The method of predicting the remaining service life of a target safety control system based on the parameter estimation value and the K-by-N safety control system operation rule includes: Based on the estimated parameter values, a Weibull distribution model that the equipment life obeys is obtained; Based on the operation rules of the double-N-take-K safety control system, the remaining service life of the target safety control system is predicted through the Weibull distribution model obeyed by the equipment life.

9. The method for predicting the remaining useful life of a safety control system according to claim 8, characterized in that: The method of predicting the remaining service life of the target safety control system based on the K-by-N safety control system operation rule by using the Weibull distribution model that the equipment life obeys includes: According to the working principle of KooN safety control system and the operating rules of K-by-N safety control system, the distribution function of the target safety control system fault is determined; Determining a probability density function of a target safety control system failure based on the distribution function of the target safety control system failure; The expectation of the probability density function of the failure is calculated to obtain the expected model of the remaining service life of the target safety control system after being put into use for δ time, which is expressed as: E(T)=∫0 ∞ Tf 2s (T)dT-δ Where E(T) is the expected value of the remaining service life of the target safety control system after it has been put into use for δ time; f 2s (T) is the probability density function of failure of the target safety control system.

10. A remaining useful life prediction system for a safety control system, characterized in that: include: An equipment life test data acquisition module, used for acquiring a plurality of equipment life test data in a non-failure equipment state based on a plurality of different truncation moments; The equipment failure probability estimation module is used to obtain the estimated value of the equipment failure probability corresponding to each truncation time based on the life test data of each equipment; A model parameter estimation module is used to estimate the parameters of a pre-built Weibull distribution model based on multiple equipment life test data and the estimated values ​​of equipment failure probabilities corresponding to each truncation time, and obtain the parameter estimation value that the equipment life follows the Weibull distribution; The life prediction module is used to predict the remaining service life of the target safety control system based on the parameter estimation value and the K-by-N safety control system operation rule.

11. A machine-readable storage medium having instructions stored thereon, characterized in that: When the instruction is executed by a processor, the processor is configured to execute the remaining service life prediction method for a safety control system as claimed in any one of claims 1 to 9.

12. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the remaining useful life prediction method for the safety control system according to any one of claims 1 to 9 is implemented.

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