Reliability determination method and device for electromechanical equipment and computer equipment

Abstract

The present disclosure provides a reliability determination method, device and computer equipment for electromechanical equipment, which belongs to the field of equipment safety technology. The reliability determination method includes: obtaining a reliability model of the electromechanical equipment, judging whether the top event is a performance failure, establishing a simulation model for the top event that is a performance failure, and determining the probability of occurrence of the top event that is a performance failure based on the simulation model; establishing a fault tree for the top event that is not a performance failure, establishing a simulation model for the first bottom event, and determining the probability of occurrence of the first bottom event based on the simulation model; obtaining the probability of occurrence of the second bottom event; determining the probability of occurrence of the top event that is not a performance failure based on the probability of occurrence of the first bottom event and the probability of occurrence of the second bottom event; and determining the reliability of the electromechanical equipment based on the probability of occurrence of multiple top events. The present disclosure can quantitatively determine the reliability of the electromechanical equipment through this method.

Description

Technical Field

[0001] The present invention belongs to the technical field of equipment safety, and in particular relates to a reliability determination method and device for electromechanical equipment, and computer equipment. Background Art

[0002] Marine electromechanical equipment not only includes equipment that propels the ship to sail, but also equipment that maintains the ship's performance, structural integrity, and personnel life services. Once a marine electromechanical equipment fails, it may not only affect the normal operation of the ship, but also threaten the safety of life and property of personnel. Therefore, when developing and designing marine electromechanical equipment, quantitative reliability analysis is required.

[0003] In related technologies, the reliability quantitative analysis of electromechanical equipment is generally based on a fault tree. The so-called fault tree is also called an error tree, which is a special inverted tree-like logical cause-and-effect relationship diagram. The reliability of electromechanical equipment is analyzed by a fault tree by statistically analyzing the historical failure probability of each component in the electromechanical equipment, and then calculating the probability of failure of the electromechanical equipment based on the historical failure probability of each component.

[0004] However, electromechanical equipment often involves numerous mutually coupled customized products, such as mutually coupled mechanical equipment, hydraulic equipment, electrical equipment, etc. It is not convenient to statistically analyze the historical failure probability of the failures involved in these customized products. Therefore, when conducting quantitative reliability analysis, it is impossible to quantitatively analyze the failures involving customized products (also known as performance failures, that is, there is a correlation between failures), and only the failures involved in batch products can be analyzed (also known as sporadic failures, there is no correlation between failures). Ultimately, the reliability analysis results of marine electromechanical equipment are inaccurate. Summary of the invention

[0005] The embodiments of the present disclosure provide a reliability determination method, device and computer device for electromechanical equipment, which can improve the reliability analysis results of electromechanical equipment. The technical solution is as follows:

[0006] An embodiment of the present disclosure provides a reliability determination method for an electromechanical device, the reliability determination method comprising: obtaining a reliability model of the electromechanical device, the reliability model comprising a plurality of top events; determining whether the top event is a performance failure, establishing a simulation model for the top event that is a performance failure, and determining the occurrence probability of the top event that is a performance failure based on the simulation model; establishing a fault tree for the top event that is not a performance failure, the fault tree comprising a first bottom event and a second bottom event, the first bottom event being a performance failure, and the second bottom event being an occasional failure; establishing a simulation model for the first bottom event, and determining the occurrence probability of the first bottom event based on the simulation model; obtaining the occurrence probability of the second bottom event; determining the occurrence probability of the top event that is not a performance failure based on the occurrence probability of the first bottom event and the occurrence probability of the second bottom event; and determining the reliability of the electromechanical device based on the occurrence probabilities of the plurality of top events.

[0007] In another implementation of the present disclosure, the probability of occurrence of the top event determined as a performance failure based on the simulation model includes: acquiring sample data, the sample data including at least one of the size, strength, and load of the structure where the performance failure occurs; based on the simulation model, determining the number of samples in the sample data that cause the top event to occur; and determining the probability of occurrence of the top event based on the number of samples in the sample data that cause the top event to occur and the capacity of the sample data.

[0008] In another implementation of the present disclosure, establishing a simulation model for the top event that is a performance failure includes: acquiring model parameters, the model parameters including at least one of the size, strength, and load of the structure where the performance failure occurs; and establishing a simulation model for the top event that is a performance failure based on the model parameters.

[0009] In another implementation of the present disclosure, determining the probability of occurrence of the first bottom event based on the simulation model includes: acquiring sample data, the sample data including at least one of size, strength, and load of the structure where the performance failure occurs; determining the number of samples in the sample data that cause the first bottom event to occur based on the simulation model; and determining the probability of occurrence of the first bottom event based on the number of samples in the sample data that cause the first bottom event to occur and the capacity of the sample data.

[0010] In another implementation of the present disclosure, establishing a simulation model for the first bottom event includes: acquiring model parameters, the model parameters including at least one of the size, strength, and load of the structure where the performance failure occurs; and establishing a simulation model of the first bottom event based on the model parameters.

[0011] In another implementation of the present disclosure, the obtaining of the probability of occurrence of the second bottom event includes: obtaining the number of historical occurrences of the second bottom event; determining the usage time of the electromechanical device; and determining the probability of occurrence of the second bottom event according to the number of historical occurrences and the usage time.

[0012] In another implementation of the present disclosure, a reliability quantitative analysis device for electromechanical equipment is provided, characterized in that the reliability quantitative analysis device includes: a reliability model acquisition module, used to acquire a reliability model of the electromechanical equipment, the reliability model includes multiple top events; a judgment module, used to judge whether the top event is a performance fault, establish a simulation model for the top event that is a performance fault, and determine the occurrence probability of the top event that is a performance fault based on the simulation model; a fault tree establishment module, used to establish a fault tree for the top event that does not belong to the performance fault, the fault tree includes a first bottom event and a second bottom event, the first bottom event is a performance fault, and the second bottom event is an occasional fault; a simulation model establishment module, used to establish a simulation model for the first bottom event, and determine the occurrence probability of the first bottom event based on the simulation model; a second bottom event occurrence probability acquisition module, used to acquire the occurrence probability of the second bottom event; a probability determination module, used to determine the occurrence probability of the top event that does not belong to the performance fault based on the occurrence probability of the first bottom event and the occurrence probability of the second bottom event; a reliability determination module, used to determine the reliability of the electromechanical equipment based on the occurrence probabilities of the multiple top events.

[0013] In another implementation of the present disclosure, the judgment module is also used to: obtain sample data, the sample data including at least one of the size, strength, and load of the structure where the performance failure occurs; determine the number of samples in the sample data that cause the top event to occur based on the simulation model; determine the probability of occurrence of the top event based on the number of samples in the sample data that cause the top event to occur and the capacity of the sample data.

[0014] In yet another implementation of the present disclosure, a computer device is provided, the computer device comprising a processor and a memory configured to store instructions executable by the processor; the processor is configured to execute the reliability determination method of the electromechanical device described above.

[0015] In yet another implementation of the present disclosure, a computer storage medium is provided on which computer instructions are stored. When the computer instructions are executed by a processor, the reliability determination method of the electromechanical device described above is implemented.

[0016] The technical solution provided by the embodiments of the present disclosure has the following beneficial effects:

[0017] When performing reliability analysis on electromechanical equipment through the reliability determination method provided by the embodiment of the present invention, multiple top events are obtained according to the reliability model of the electromechanical equipment, and the occurrence probabilities of different top events are determined respectively, wherein the occurrence probability of the top event that is a performance failure is calculated according to the simulation model, and a fault tree is established for the top event that is not a performance failure. Then, a simulation model is established for the first bottom event that is a performance failure in the fault tree to determine its occurrence probability, and the occurrence probability of the second bottom event that is an occasional failure is obtained. In this way, the probability of failure of the electromechanical equipment can be determined, and the reliability of the electromechanical equipment can be obtained. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions in the embodiments of the present disclosure, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present disclosure. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0019] Figure 1 is a flow chart of a reliability determination method of an electromechanical device provided by an embodiment of the present disclosure;

[0020] Figure 2 is a flow chart of another method for determining reliability of electromechanical equipment provided by an embodiment of the present disclosure;

[0021] Figure 3 is an architecture diagram of a reliability model provided by an embodiment of the present disclosure;

[0022] Figure 4 is a schematic diagram of a fault tree provided by an embodiment of the present disclosure;

[0023] Figure 5 is a schematic diagram of a failure occurrence probability of a reliability model provided by an embodiment of the present disclosure;

[0024] Figure 6 is a structural schematic diagram of a reliability determination device for electromechanical equipment provided by an embodiment of the present disclosure;

[0025] Figure 7 It is a structural diagram of a computer device provided in an embodiment of the present disclosure. DETAILED DESCRIPTION

[0026] In order to make the objectives, technical solutions and advantages of the present disclosure more clear, the embodiments of the present disclosure will be further described in detail below with reference to the accompanying drawings.

[0027] The present disclosure provides a method for determining the reliability of an electromechanical device. Figure 1 As shown, the reliability determination method of electromechanical equipment includes:

[0028] S101: Obtain a reliability model of electromechanical equipment, where the reliability model includes a plurality of top events.

[0029] S102: Determine whether the top event is a performance failure, establish a simulation model for the top event that is a performance failure, and determine the occurrence probability of the top event that is a performance failure based on the simulation model.

[0030] S103: Establish a fault tree for the top event that is not a performance fault. The fault tree includes a first bottom event and a second bottom event. The first bottom event is a performance fault, and the second bottom event is an occasional fault.

[0031] S104: Establishing a simulation model for the first bottom event, and determining the occurrence probability of the first bottom event based on the simulation model.

[0032] S105: Obtain the occurrence probability of the second bottom event.

[0033] S106: Determine the occurrence probability of a top event that is not a performance failure based on the occurrence probability of the first bottom event and the occurrence probability of the second bottom event.

[0034] S107: Determine the reliability of the electromechanical equipment based on the occurrence probabilities of the multiple top events.

[0035] When performing reliability analysis on electromechanical equipment through the reliability determination method provided by the embodiment of the present invention, multiple top events are obtained according to the reliability model of the electromechanical equipment, and the occurrence probabilities of different top events are determined respectively, wherein the occurrence probability of the top event that is a performance failure is calculated according to the simulation model, and a fault tree is established for the top event that is not a performance failure. Then, a simulation model is established for the first bottom event that is a performance failure in the fault tree to determine its occurrence probability, and the occurrence probability of the second bottom event that is an occasional failure is obtained. In this way, the probability of failure of the electromechanical equipment can be determined, and the reliability of the electromechanical equipment can be obtained.

[0036] That is to say, the reliability determination method provided by the embodiment of the present disclosure can quantitatively determine the probability of occurrence of performance failures involved in electromechanical equipment, and then can conduct a comprehensive and orderly evaluation and verification of the reliability of the electromechanical equipment, ultimately achieving the purpose of effectively improving the reliability design of marine electromechanical equipment.

[0037] The disclosed embodiment also provides another reliability determination method for electromechanical equipment, such as Figure 2 As shown, the reliability determination method of electromechanical equipment includes:

[0038] S201: Obtain a reliability model of electromechanical equipment, where the reliability model includes a plurality of top events.

[0039] Firstly, a reliability model of electromechanical equipment is established. The reliability model includes the hierarchical relationship among the whole machine, working condition, task profile, subtask and top event.

[0040] Each working condition, each task profile, and each subtask are independent events, and each top event is mutually exclusive. The so-called whole machine is the electromechanical equipment. The working condition is the various working states corresponding to the electromechanical equipment.

[0041] Figure 3 is an architecture diagram of a reliability model provided by an embodiment of the present disclosure, combined with Figure 3 For example, when the electromechanical equipment is an anchor winch, the corresponding working conditions are anchoring conditions and mooring conditions.

[0042] The task profile is the specific state that the electromechanical equipment corresponds to under different working conditions. When the electromechanical equipment is an anchor winch and the working condition is an anchoring condition, it includes task profiles such as electrically controlled anchor dropping, gravity anchor dropping, and electrically controlled anchor collection. When the working condition is a mooring condition, it includes task profiles such as electrically controlled cable dropping, automatic tensioning, and electrically controlled anchor collection. Subtasks are the different tasks corresponding to each task profile, including startup, operation, end, and so on. The top event is the specific state displayed when a fault occurs in the electromechanical equipment corresponding to each subtask, such as no action at startup, slow startup response, stalling during movement, braking during operation, uncontrollable speed, and so on.

[0043] S202: Determine whether the top event is a performance failure.

[0044] In the established reliability model, once some top events occur, performance failures will be directly caused. At this time, it is only necessary to directly determine the probability of failure of such top events.

[0045] That is to say, once such a top event occurs, the cause of the failure is relatively simple and there is no need to further subdivide it.

[0046] For example, when the whole machine is an anchor winch, a slow start-up response of a top event under an anchoring condition is a performance failure.

[0047] S203: Establish a simulation model for the top event of the performance failure.

[0048] Exemplarily, step S203 includes:

[0049] 2031: Obtain model parameters, where the model parameters include at least one of size, strength, and load of the structure where the performance failure occurs.

[0050] By statistically analyzing the physical parameters of the structure with performance failure, such as size, strength, load, etc., a simulation model related to the performance failure can be established accordingly, so as to simulate the actual working state of the structure with performance failure through the simulation model.

[0051] Of course, the model parameters are not limited to the data mentioned above, and the model parameters can also be the elastic modulus, Poisson's ratio, yield strength, tension, pressure, flow, PID logic control parameters, etc. of the structure with performance failure.

[0052] 2032: Based on the model parameters, a simulation model of a top event of a performance failure is established.

[0053] In this embodiment, the above model parameters can be used to directly establish a simulation model through software.

[0054] Exemplarily, the simulation models include simulation models of structural strength and stiffness performance, system dynamics simulation models of electromechanical-fluid coupling performance, and other fluid simulation models, dynamics simulation models, and the like.

[0055] The specific type of the simulation model is flexibly selected according to the structure of the actual corresponding performance fault.

[0056] When establishing the simulation model, the simulation model of structural strength and stiffness performance uses ANSYS software, and the system dynamic simulation model uses Amesim software.

[0057] S204: Determine the occurrence probability of the top event of the performance failure based on the simulation model.

[0058] Exemplarily, S204 includes:

[0059] 2041: Acquire sample data, where the sample data includes at least one of the size, strength, and load of the structure having the performance failure.

[0060] In this embodiment, the sample data is sample data of multiple batches of the structure with performance failure during production.

[0061] For example, for the material parameters of a certain structure in the structural strength simulation model, the theoretical value of the elastic modulus is 200 GPa. In the three batches of products produced, the elastic moduli are 205 GPa, 208 GPa, and 201 GPa, respectively. Then the sample data includes 205 GPa, 208 GPa, and 201 GPa, and the sample data capacity is 3. Here, only three samples are taken as an example. In practice, the sample data capacity can be much larger than 3, such as 300.

[0062] 2042: Based on the simulation model, determine the number of samples in the sample data that cause the top event to occur.

[0063] For example, when the electromechanical equipment is an anchor winch, the sample data includes anchor chain weight, anchor weight, oil cylinder size, constant pressure variable pump flow, pressure difference flow curve of pressure reducing valve, etc.

[0064] When calculating through the simulation model, multiple groups of sample data are used as input values, and then output values ​​corresponding to the multiple groups of samples can be obtained one by one. By analyzing each output value, it is determined whether these output values ​​cause performance failures. The number of output values ​​that can cause performance failures is determined as the number of samples in the sample data that cause the top event to occur.

[0065] 2043: Determine the probability of occurrence of the top event based on the number of samples in the sample data that lead to the occurrence of the top event and the capacity of the sample data.

[0066] In this embodiment, the probability of occurrence of the top event can be obtained by dividing the number of samples in the sample data that lead to the occurrence of the top event by the capacity of the sample data.

[0067] For example, taking the top event of performance failure "slow response to anchor start" as an example, the calculation of its occurrence probability is as follows: first call the relevant simulation software and model, use the model parameters as hydraulic components, electrical components characteristics, control logic and load characteristics, and use Amesim software to establish a gravity anchor machine electro-hydraulic coupling simulation model. Then simulate the sample data to obtain a series of output values, and then divide the number of output values ​​that cause the fault by the total number of samples to obtain the probability of occurrence of the top event.

[0068] S205: Establish a fault tree for the top event that is not a performance fault, the fault tree including a first bottom event and a second bottom event, the first bottom event is a performance fault, and the second bottom event is an occasional fault.

[0069] By establishing a fault tree for top events that are not performance faults, the top events can be analyzed through the fault tree to find different causes of the faults, and multiple performance faults and occasional faults can be obtained based on different causes.

[0070] S206: Establish a simulation model for the first bottom event.

[0071] Exemplarily, S206 includes:

[0072] 2061: Obtain model parameters, where the model parameters include at least one of size, strength, and load of the structure having performance failure.

[0073] 2062: Based on the model parameters, establish a simulation model of the first bottom event.

[0074] The detailed process of step S206 can be seen in S203 and will not be described in detail here.

[0075] S207: Determine the probability of occurrence of the first bottom event.

[0076] Exemplarily, S207 includes:

[0077] 2071: Obtain sample data, where the sample data includes at least one of size, strength, and load of the structure having performance failure.

[0078] 2072: Based on the simulation model, determine the number of samples in the sample data that cause the first bottom event to occur.

[0079] 2073: Determine the probability of occurrence of the first bottom event based on the number of samples in the sample data that cause the first bottom event to occur and the capacity of the sample data.

[0080] The detailed process of step S207 can be seen in S204 and will not be described in detail here.

[0081] S208: Obtain the occurrence probability of the second bottom event.

[0082] Exemplarily, S208 includes:

[0083] 2081: Get the historical occurrence count of the second bottom event and the usage time of the electromechanical equipment.

[0084] The historical number of occurrences of the second bottom event and the usage time of the electromechanical equipment can be obtained through statistics or by requesting it from the manufacturer.

[0085] 2082: Determine the probability of the second bottom event based on the historical number of occurrences and usage duration.

[0086] First, convert the usage time of the electromechanical equipment into the number of times it is used, and then calculate the ratio of the historical number of occurrences to the number of times it is used to obtain the probability of occurrence of the second bottom event.

[0087] The usage time of the electromechanical equipment can be obtained by calculating the ratio of the usage time to the average usage time corresponding to each use to obtain the corresponding number of uses.

[0088] For example, if the electromechanical equipment is used for 10 years, with an average of 0.001 years each time, and is used 1000 times within ten years, and two damage and replacement records are found in the fault records, then the probability of the second bottom event occurring is 0.2%.

[0089] S209: Determine the occurrence probability of a top event that is not a performance failure based on the occurrence probability of the first bottom event and the occurrence probability of the second bottom event.

[0090] According to the occurrence probability of the first bottom event and the occurrence probability of the second bottom event, the occurrence probability of the top event belonging to the performance failure can be quickly obtained.

[0091] For example, for the top event, the anchoring speed is uncontrollable, and a fault tree is established for it (see Figure 4), the fault tree includes three levels, and the multiple faults in each level are mutually exclusive. Among them, motor failure, anchor chain wheel clutch failure, etc. are the second bottom events, and brake resistor failure belongs to the first bottom event. Then, according to the historical statistics and simulation model, the occurrence probability of the second bottom event and the first bottom event is obtained, and finally the occurrence probability of the top event is obtained.

[0092] S210: Determine the reliability of the electromechanical equipment based on the occurrence probabilities of the multiple top events.

[0093] Exemplarily, S210 includes:

[0094] 2101: According to the occurrence probabilities of multiple top events, the occurrence probabilities of subtasks are obtained.

[0095] According to the relationship between independent events and mutually exclusive events in mathematical relations, the occurrence probability of each subtask is calculated.

[0096] for example, Figure 5 is a schematic diagram of the failure probability of a reliability model provided by an embodiment of the present disclosure, see Figure 5 , the working condition includes two working conditions, the probability of occurrence of the first working condition is P1, and the probability of occurrence of the second working condition is P2. Then, correspondingly, there are 3 task profiles under each working condition, so the probability of occurrence of each task profile is: P 11 , P 12 , P 13 , P 21 , P 22 , P 23 Etc., each task profile corresponds to 3 subtasks, and the probability of occurrence of each subtask is P 111 , P 112 , P 113 , P 211 , P 212 , P 213 …. The probability of occurrence of the top event is P 1111 , P 1112 , P 1113 …P 1211 , P 1212 , P 1213 ….

[0097] According to the above analysis, P 121 =P 1211 +P 1212 etc.

[0098] 2043: According to the occurrence probability of the subtask, the occurrence probability of the task profile is obtained.

[0099] According to the occurrence probability of the subtask, the occurrence probability of the task profile can be obtained in the following way.

[0100] For example, P 12 =1-(1-P 121 )(1-P 122 )(1-P 123 ).

[0101] 2044: Obtain the reliability of electromechanical equipment based on the probability of occurrence of the mission profile.

[0102] The failure probability of the electromechanical equipment is calculated according to the following method.

[0103] For example, the failure probability of electromechanical equipment is P.

[0104] P1=1-(1-P 11 )(1-P 12 )(1-P 13 ); P2 = 1-(1-P 21 )(1-P 22 )(1-P 23 );

[0105] P=1-(1-P1) 2 (1-P2) 4 .

[0106] In this embodiment, in order to further illustrate the specific implementation process of the above reliability determination method, it is described below in conjunction with a specific example.

[0107] For example, the electromechanical equipment is an anchor winch. Based on the design input provided by the shipyard and other overall units, the product working conditions, single working cycle and task profile of the anchor winch are sorted out.

[0108] The working conditions of the anchor winch in a single voyage mission are divided into anchoring conditions and mooring conditions. The task profile of the anchoring condition includes dropping anchor (working time is about 4 minutes), casting anchor (working time is about 2 minutes) and collecting anchor (working time is about 24 minutes). The task profile of the mooring condition includes dropping cable (working time is about 15 minutes), collecting cable (working time is about 15 minutes) and automatic tensioning (working time is about 30 minutes). In a single working cycle, the anchoring is performed 2 times (time is about 1 hour) and the mooring is performed 4 times (time is about 4 hours). The total time of a single working cycle is 5 hours.

[0109] First, based on the above information, each task profile is sorted out for each subtask and the typical faults under each subtask. The typical fault is the top event of the subsequent fault tree, and the reliability model of the anchor winch product is established according to the hierarchical relationship of "whole machine → working condition → task profile → subtask → top event". Among them, each working condition, each task profile, and each subtask are independent events, and each top event is a mutually exclusive event. For example, the subtasks of gravity anchoring are anchoring start, operation and end, and the typical faults of anchoring start are no action at start, slow start response, etc. to establish the reliability model of the anchor winch (see Figure 3 ).

[0110] Then, a fault tree is established for each of the top events of non-performance faults. For example, a fault tree is established for the top event, the uncontrollable anchor speed, such as Figure 4 shown.

[0111] Then, the occurrence probability of each top event in the reliability model of the anchor winch is determined according to the independent or mutually exclusive relationship between each event.

[0112] Among them, the probability of failure of the whole machine is P, and the probability of failure in each working condition is P1 and P2 respectively. The probability of failure of each task profile under each working condition can be expressed as, P 11 , P 12 , P 13 , P 21 , P 22 , P 23 The probability of occurrence of each subtask under each task profile can be expressed as P 111 , P 112 , P 113 …P 211 , P 212 , P 213 The probability of occurrence of the top event under each subtask is P 1111 , P 1112 , P 1113 …P 1211 , P 1212 , P 1213 …. By analyzing the relationship between the probability of occurrence of each level, the probability of failure of the whole machine P can be obtained (see Figure 5 ).

[0113] That is to say, according to the mutually exclusive relationship between the top events, the probability of failure of each subtask is obtained. For example, the probability of failure of the anchor start is P 121 =P 1211 +P 1212 Then, according to the relationship between each subtask, the probability of failure of each task profile is obtained. For example, the probability of failure of gravity anchor is P 12 =1-(1-P121 )(1-P 122 )(1-P 123 ). Then, by analyzing the relationship between each task profile, the probability of problems occurring under each working condition is obtained. P1=1-(1-P 11 )(1-P 12 )(1-P 13 ); P2 = 1-(1-P 21 )(1-P 22 )(1-P 23 ); In this way, the probability of failure of the whole machine can be obtained according to the probability of failure in each working condition. P = 1-(1-P1) 2 (1-P2) 4 .

[0114] Next, the probability of occurrence of the top event of performance failure is calculated using a quantitative simulation model. For example, the probability of occurrence of "slow response to anchor start" can be calculated based on the simulation model. The probability of occurrence of the top event of non-performance failure is analyzed according to the hierarchical probability of occurrence of the fault tree. For example, the causes of "no action after anchor start" are analyzed as the top event, which are: starter overload P 1211-1 , PLC module channel damage 1211-2 , Start button failure P 1211-3 , line loose P 1211-4 , PLC module proportional pressure reducing valve output channel is damaged 1211-5 、Motor damage P 1211-6 , Pump damage 1211-7 , Pressure reducing valve stuck P 1211-8 , Electric proportional pressure reducing valve stuck P 1211-9 , Solenoid reversing valve stuck P 1211-10 , Brake band deformation P 1211-11 , Brake pad wear P 1211-12 Etc., these failure modes are all independent events. The probability of occurrence of the first bottom event in these failures is calculated according to the simulation model (for example, P 1211-11 The probability of occurrence of the second bottom event in these faults is calculated according to historical statistical data. Finally, the probability of occurrence of the top event "anchor start without action" is P 1211 =1-(1-P 1211-1 )(1-P 1211-2 )(1-P 1211-3 )…(1-P 1211-12 ).

[0115] After calculating the probability of occurrence of all top events, the failure probability P of the whole machine in a single working cycle is calculated according to the above probability calculation formula, and then according to MTBF (Mean Time Between Failure) = product single working cycle duration T / failure probability P = 5621.6 hours > reliability design index (2000 hours), the quantitative design index evaluation of product reliability is completed. If the calculation result does not meet the reliability design index, the relevant design scheme and parameters of the anchor winch are optimized according to the weak links in the design.

[0116] Figure 6 is a schematic diagram of a reliability determination device for electromechanical equipment provided by an embodiment of the present disclosure, combined with Figure 6 The reliability determination device includes: a reliability model acquisition module 601, a judgment module 602, a fault tree establishment module 603, a simulation model establishment module 604, a second bottom event occurrence probability acquisition module 605, a probability determination module 606, and a reliability determination module 607. A reliability model acquisition module 601 is used to acquire a reliability model of an electromechanical device, wherein the reliability model includes a plurality of top events; a judgment module 602 is used to judge whether a top event is a performance failure, establish a simulation model for a top event that is a performance failure, and determine the probability of occurrence of the top event that is a performance failure based on the simulation model; a fault tree establishment module 603 is used to establish a fault tree for a top event that is not a performance failure, wherein the fault tree includes a first bottom event and a second bottom event, wherein the first bottom event is a performance failure, and the second bottom event is an occasional failure; a simulation model establishment module 604 is used to establish a simulation model for the first bottom event, and determine the probability of occurrence of the first bottom event based on the simulation model; a second bottom event probability acquisition module 605 is used to acquire the probability of occurrence of the second bottom event; a probability determination module 606 is used to determine the probability of occurrence of the top event that is not a performance failure based on the probability of occurrence of the first bottom event and the probability of occurrence of the second bottom event; a reliability determination module 607 is used to determine the reliability of the electromechanical device based on the probability of occurrence of a plurality of top events.

[0117] The judgment module 602 is also used to: obtain sample data, the sample data including at least one of the size, strength, and load of the structure where the performance failure occurs; determine the number of samples in the sample data that cause the top event to occur based on the simulation model; and determine the probability of occurrence of the top event based on the number of samples in the sample data that cause the top event to occur and the capacity of the sample data.

[0118] The judgment module 602 is also used to: obtain model parameters, the model parameters including at least one of the size, strength, and load of the structure with performance failure; and establish a simulation model of the top event of the performance failure based on the model parameters.

[0119] The simulation model building module 604 is also used to obtain sample data, the sample data including at least one of the size, strength, and load of the structure where the performance failure occurs; based on the simulation model, determine the number of samples in the sample data that cause the first bottom event to occur; and determine the probability of occurrence of the first bottom event based on the number of samples in the sample data that cause the first bottom event to occur and the capacity of the sample data.

[0120] The simulation model building module 604 obtains model parameters, which include at least one of the size, strength, and load of the structure with performance failure; and builds a simulation model of the first bottom event based on the model parameters.

[0121] The second bottom event occurrence probability acquisition module 605 is further used to acquire the historical occurrence times of the second bottom event; determine the usage times of the structure of the second bottom event; and calculate the occurrence probability of the second bottom event according to the historical occurrence times and the usage times.

[0122] The above reliability quantitative analysis device has the same technical effect as the aforementioned reliability determination method, which will not be repeated here.

[0123] Figure 7 is a schematic diagram of a computer device provided by an embodiment of the present disclosure, combined with Figure 7 , the computer device 700 may include one or more of the following components: a processor 701 , a memory 702 , a communication interface 703 , and a bus 704 .

[0124] The processor 701 includes one or more processing cores, and the processor 701 executes various functional applications and information processing by running software programs and modules. The memory 702 and the communication interface 703 are connected to the processor 701 through the bus 704. The memory 702 can be used to store at least one instruction, and the processor 701 is used to execute the at least one instruction to implement each step in the above method.

[0125] In addition, the memory 702 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, and the volatile or non-volatile storage device includes but is not limited to: a magnetic disk or an optical disk, an electrically erasable programmable read-only memory (EEPROM), an erasable programmable read-only memory (EPROM), a static random access memory (SRAM), a read-only memory (ROM), a magnetic memory, a flash memory, and a programmable read-only memory (PROM).

[0126] The embodiments of the present disclosure also provide a non-temporary computer-readable storage medium. When the instructions in the storage medium are executed by a processor of a computer device, the computer device can execute the above reliability determination method of the electromechanical device provided in the embodiments of the present application.

[0127] A computer program product containing instructions, when running on a computer, enables the computer to execute the above reliability determination method of electromechanical equipment provided in the embodiment of the present application.

[0128] The above description is only an optional embodiment of the present disclosure and is not intended to limit the present disclosure. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present disclosure shall be included in the protection scope of the present disclosure.

Claims

1. A method for determining the reliability of electromechanical equipment, characterized in that: The reliability determination method comprises: Acquire a reliability model of the electromechanical device, wherein the reliability model includes a plurality of top events; Determining whether the top event is a performance failure, establishing a simulation model for the top event that is a performance failure, and determining an occurrence probability of the top event that is a performance failure based on the simulation model of the top event that is a performance failure; Establishing a fault tree for the top event that is not a performance fault, the fault tree comprising a first bottom event and a second bottom event, the first bottom event being a performance fault, and the second bottom event being an occasional fault; Establishing a simulation model for the first bottom event, and determining the occurrence probability of the first bottom event based on the simulation model of the first bottom event; Obtaining the probability of occurrence of the second bottom event; Determining the probability of occurrence of the top event that is not a performance failure based on the probability of occurrence of the first bottom event and the probability of occurrence of the second bottom event; The reliability of the electromechanical device is determined based on the occurrence probabilities of the plurality of top events.

2. The reliability determination method according to claim 1, characterized in that: The determining the occurrence probability of the top event being a performance failure based on the simulation model of the top event being a performance failure comprises: Acquiring sample data, the sample data including at least one of a size, a strength, and a load of a structure having a performance failure; Determining the number of samples in the sample data that cause the top event to occur based on a simulation model of the top event that is a performance failure; The occurrence probability of the top event is determined based on the number of samples in the sample data that cause the top event to occur and the volume of the sample data.

3. The reliability determination method according to claim 1, characterized in that: The establishing of a simulation model for the top event of the performance failure comprises: Acquiring model parameters, wherein the model parameters include at least one of size, strength, and load of the structure having the performance failure; Based on the model parameters, a simulation model of the top event which is a performance failure is established.

4. The reliability determination method according to claim 1, characterized in that: The determining the occurrence probability of the first bottom event based on the simulation model of the first bottom event includes: Acquiring sample data, the sample data including at least one of a size, a strength, and a load of a structure having a performance failure; Determining, based on the simulation model of the first bottom event, the number of samples in the sample data that cause the first bottom event to occur; The occurrence probability of the first bottom event is determined based on the number of samples in the sample data that cause the first bottom event to occur and the volume of the sample data.

5. The reliability determination method according to claim 1, characterized in that: The step of establishing a simulation model for the first bottom event includes: Acquiring model parameters, wherein the model parameters include at least one of size, strength, and load of the structure having the performance failure; A simulation model of the first bottom event is established based on the model parameters.

6. The reliability determination method according to claim 1, characterized in that: The obtaining the occurrence probability of the second bottom event includes: Obtain the historical occurrence count of the second bottom event; Determining the usage time of the electromechanical equipment; The occurrence probability of the second bottom event is determined according to the historical occurrence times and the usage duration.

7. A reliability determination device for electromechanical equipment, characterized in that: The reliability determination device is implemented based on the reliability determination method of the electromechanical equipment according to any one of claims 1 to 6, and the reliability determination device comprises: A reliability model acquisition module, used to acquire a reliability model of the electromechanical device, wherein the reliability model includes a plurality of top events; A judgment module, used to judge whether the top event is a performance failure, establish a simulation model for the top event that is a performance failure, and determine the occurrence probability of the top event that is a performance failure based on the simulation model of the top event that is a performance failure; A fault tree establishment module, used to establish a fault tree for the top event that is not a performance fault, the fault tree comprising a first bottom event and a second bottom event, the first bottom event being a performance fault, and the second bottom event being an occasional fault; A simulation model building module, used to build a simulation model for the first bottom event, and determine the occurrence probability of the first bottom event based on the simulation model of the first bottom event; An occurrence probability acquisition module of a second bottom event, used to acquire the occurrence probability of the second bottom event; A probability determination module, configured to determine the probability of occurrence of the top event that is not a performance failure based on the probability of occurrence of the first bottom event and the probability of occurrence of the second bottom event; The reliability determination module is used to determine the reliability of the electromechanical device based on the occurrence probabilities of the multiple top events.

8. The reliability determination device according to claim 7, characterized in that: The judging module is also used for: Acquiring sample data, the sample data including at least one of a size, a strength, and a load of a structure having a performance failure; Determining the number of samples in the sample data that cause the top event to occur based on a simulation model of the top event that is a performance failure; The occurrence probability of the top event is determined based on the number of samples in the sample data that cause the top event to occur and the volume of the sample data.

9. A computer device, characterized in that: The computer device comprises a processor and a memory configured to store instructions executable by the processor; the processor is configured to execute the reliability determination method of the electromechanical device according to any one of claims 1 to 6.

10. A computer storage medium having computer instructions stored thereon, characterized in that: When the computer instructions are executed by a processor, the reliability determination method of the electromechanical device according to any one of claims 1 to 6 is implemented.

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