A ship system reliability calculation method and system based on discrete events and Monte Carlo simulation

Abstract

The application belongs to the technical field of reliability evaluation, and discloses a ship system reliability calculation method and system based on discrete events and Monte Carlo simulation. The method comprises the following steps: (a) constructing a simulation model according to a reliability block diagram of a ship system to be processed; (b) setting a simulation test number N according to a total task of the ship system to be processed; (c) for the i-th simulation test, setting a simulation time, a time interval of token generation, generating a plurality of tokens with a label of 0 at a reliability entrance, transmitting each token along a corresponding directed connection line, updating the label in the transmission process, and obtaining a final label when the token reaches a reliability exit; (d) i = i + 1, repeating step (c) until i > N, obtaining the final labels of all tokens in all simulation tests, and calculating the reliability of the simulation model by using the final labels of all tokens. Through the application, the problem that the reliability of a complex system is difficult to calculate is solved.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field related to reliability evaluation, and more particularly, to a ship system reliability calculation method and system based on discrete event and Monte Carlo simulation. BACKGROUND

[0002] The traditional ship system reliability evaluation method is mainly based on empirical statistics and theoretical analysis, and relies on a large amount of historical data and strict mathematical models. However, the ship system structure is complex, containing a large number of components and subsystems, and there are series and parallel relationships between these systems, and complex dependencies and influences between them, which leads to many limitations of the traditional method in dealing with such complex systems. On the one hand, the cost of obtaining historical data is high and not necessarily comprehensive, and on the other hand, strict mathematical models often cannot completely describe the dynamic characteristics and internal complexity of the actual system.

[0003] Discrete event simulation as an effective tool for handling complex system behavior has been widely used in many fields. Its main advantage is that it can simulate the dynamic change process of the system in different states, and reflect the interaction between the system and the environment by constructing an event scheduling based on time elapse. For the reliability evaluation of the ship system, discrete event simulation can provide a more flexible and detailed analysis method, which can take into account various randomness and uncertainty factors in the system operation process. However, although discrete event simulation has the above advantages, how to accurately integrate the reliability requirements and performance indicators of the system into the simulation model, and how to efficiently extract and calculate reliability data from the simulation process, are still one of the main challenges currently faced. In addition, due to the complexity of the system, a single discrete event simulation may not be able to achieve a comprehensive evaluation of the system reliability. Therefore, a method is needed to solve the above discrete event reliability data. SUMMARY

[0004] In view of the above defects or improvement needs of the prior art, the present application provides a ship system reliability calculation method and system based on discrete event and Monte Carlo simulation, which solves the problem of difficult calculation of complex system reliability.

[0005] To achieve the above purpose, according to one aspect of the present application, a ship system reliability calculation method based on discrete event and Monte Carlo simulation is provided, which comprises the following steps:

[0006] (a) constructing a simulation model according to the reliability block diagram of the ship system to be processed, the simulation model comprising reliability objects, directed connection lines, a reliability entrance and a reliability exit, the reliability entrance being arranged at the initial position of the simulation model, the reliability exit being arranged at the end of the simulation model, and the directed connection lines being used to connect the reliability objects;

[0007] (b) setting the number of simulation tests N according to the total task of the ship system to be processed;

[0008] (c) for the i-th simulation test, setting the simulation time T, the time interval Δt of token generation, generating a plurality of tokens with label 0 at the reliability entrance according to the time interval Δt within the simulation time T, each token is transmitted along the corresponding directed connection line, and the label is updated during the transmission process, and the corresponding label when the token reaches the reliability exit is the final label, thereby obtaining the final label of all tokens in the simulation test;

[0009] (d) i=i+1, repeating step (b) until i>N, obtaining the final label of all tokens in all simulation tests, and calculating the reliability of the simulation model using the final label of all tokens, and N is the number of simulation tests set according to the total task of the ship system to be processed.

[0010] Further preferably, the reliability object includes a reliability device, a relationship component and a logic component, the reliability device is a device object in the reliability block diagram, the relationship component is arranged at the front end of a plurality of parallel reliability devices, and the logic component is arranged at the end of a plurality of parallel reliability devices.

[0011] Further preferably, the total task includes a plurality of simulation tests, one simulation test includes one or more task sequences, one task sequence includes one or more task profiles, and one task profile includes one or more task stages, and the task profile is set with all task stages included in the simulation test, and the task stage is set with the start time and end time of the current task stage.

[0012] Further preferably, when the reliability device is set in the simulation model, the task sequence, the task profile, the task stage, the failure and the maintenance time distribution type and parameters of the reliability device in the simulation test are set, and the set task sequence, the task profile, the task stage, the failure and the maintenance time distribution type and parameters are used to determine whether the reliability device is in a normal state, a failure state or a non-planned state at the current time.

[0013] The relationship component is set in a task sequence, a task profile, a task stage and a component relationship among reliability devices in a simulation test; the set task sequence, task profile and task stage are used to determine whether the relationship component is in a planned state or an unplanned state at the current time; the component relationship among reliability devices is preset as hot backup or cold backup, the hot backup means that each directed connection line from the relationship component to the subsequent reliability device is available, and the cold backup means that only one of all directed connection lines from the relationship component to the subsequent reliability device is available;

[0014] The relationship among the device objects in the reliability block diagram is set according to the logic component, when the relationship among the device objects in the reliability block diagram is in parallel, the logic component is set as OR; when the relationship among the device objects in the reliability block diagram is &, the logic component is set as AND; and when the relationship among the device objects in the reliability block diagram is r / n, the logic component is set as r / n.

[0015] Further preferably, the label of the unplanned state is 2, the planned state includes a normal state and a failure state, the label of the normal state is 0, and the label of the failure state is 1.

[0016] Further preferably, in step (b), the updating of the label in the passing process is performed in the following manner:

[0017] When the token is passed to the subsequent reliability object A, the current token is removed, m tokens are created, the labels of the tokens are set according to the preset rule, the created m tokens are passed to the subsequent reliability object B, m is the number of the subsequent reliability object B of the subsequent reliability object A, and the passing of the token is performed until the token is passed to the last reliability object in the simulation model, the token and the label in the last reliability object are directly passed to the reliability outlet, and thus one token passing cycle is completed.

[0018] Further preferably, the preset rule is performed in the following manner:

[0019] When the subsequent reliability object A is a relationship component, whether all the subsequent reliability object B of the subsequent reliability object A is unplanned is determined, if all the subsequent reliability object B is unplanned, the label of the token of the subsequent reliability object A is set as 2; otherwise, whether the relationship among the subsequent reliability devices is cold backup or hot backup is determined, when it is cold backup, at most one of all the subsequent reliability devices has a label of 0, and the rest has a label of 2, when it is hot backup, the labels of the subsequent reliability devices are all flag0, and flag0 is the label of the token currently passed to the subsequent reliability object A;

[0020] When the successor reliability object A is a reliability device, the state of the reliability device is determined according to the task sequence, the task profile, the task stage, the distribution type and the parameter of the failure and maintenance time set by the reliability device, and the label of the reliability device is set according to the state;

[0021] When the successor reliability object A is a logic component, the Num tokens of the Num predecessor objects directly connected to the logic component are all waited to arrive at the logic component, and the label of the token on the logic component is set according to the logic type set on the logic component, and the predecessor object is a reliability component or a logic component.

[0022] Further preferably, when the successor reliability object A is a reliability device, the state of the reliability device is normal, failure or non-planned, when it is non-planned, the token label is 2, when it is failure, the token label is max(1, flag0), and when it is normal, the label is flag0.

[0023] Further preferably, when the successor reliability object A is a logic component, when the logic component is OR, it is first judged whether there is 0 in the labels of the Num tokens, if there is, the label of the current logic component is set as 0, if there is no 0 in the labels of the Num tokens and there is 1 in the labels, the label of the current logic component is set as 1, and in other cases, the label of the current logic component is set as 2.

[0024] When the logic component is AND, it is first judged whether there is 1 in the labels of the Num tokens, if there is, the label of the current logic component is set as 1, if there is no 1 in the labels of the Num tokens and there is 0 in the labels, the label of the current logic component is set as 0, and in other cases, the label of the current logic component is set as 2.

[0025] When the logic component is r / n, if the number of 0 in the labels of the Num tokens is greater than or equal to r, the label of the current logic component is set as 0, if r is greater than Num or the labels of the Num tokens are not all 2, the label of the current logic component is set as 1, and in other cases, the label of the current logic component is set as 2.

[0026] According to another aspect of the present application, there is provided a ship system reliability calculation system based on discrete events and Monte Carlo simulation, which comprises an executor for executing the ship system reliability calculation method based on discrete events and Monte Carlo simulation as described above.

[0027] In general, the above technical scheme conceived by the present application has the following beneficial effects compared with the prior art:

[0028] 1. The application is a Monte Carlo simulation method based on a probability statistical numerical calculation method, which is a kind of Monte Carlo simulation method based on probability statistical numerical calculation method by constructing multiple simulation tests, calculating the reliability of the simulation model by counting all the output token labels of the simulation tests, more accurately evaluating and calculating the reliability of the ship system under different working conditions, and the value is more and more close to the theoretical value with the increase of the simulation times, effectively solving the defect that the reliability of the complex system is difficult to calculate, and providing a new idea for improving the efficiency of analyzing the reliability of the complex system;

[0029] 2. The application constructs the conversion of the reliability block diagram into the simulation model, can simulate the dynamic behavior and failure mode of each component of the system, through the conversion, the simulation model can not only specifically describe the dependency relationship between the components, but also can reflect the response of the system under different failure conditions, the process provides accurate dynamic input data for reliability evaluation, effectively supports the reliability analysis of large-scale complex system, and simplifies the modeling process of complex system;

[0030] 3. The application updates the label of the token according to the set rules, mainly captures discrete events in the system running process, dynamically adjusts the token state, reflects the change of each component in the system, through the design of specific update rules, the simulation can automatically judge the occurrence time of the event, and adjusts the label state of each token according to the random input, flexibly simulates the dynamic change and failure process of the system; in this way, not only can the state conversion of each step be refined, but also the randomness of Monte Carlo simulation can be combined, so that each simulation run has diversified results, and finally the results of a large number of simulation tests are summarized, the accuracy of system reliability calculation is improved;

[0031] 4. The application can not only make full use of the advantage of discrete event simulation in processing system dynamic characteristics, but also can take advantage of the powerful ability of Monte Carlo simulation in processing uncertainty problem, provide a more comprehensive and accurate analysis tool for ship system reliability evaluation, the method is expected to effectively improve the accuracy and efficiency of ship system reliability evaluation, and has important practical significance for ensuring the safe and stable operation of the ship system. BRIEF DESCRIPTION OF DRAWINGS

[0032] Figure 1 It is a modeling object schematic diagram according to the preferred embodiment of the application;

[0033] Figure 2 It is a topological structure schematic diagram formed by combination of various components according to the preferred embodiment of the application;

[0034] Figure 3 It is a reliability block diagram of a certain ship system according to the preferred embodiment of the application;

[0035] Figure 4is a reliability model of a certain ship system built based on a discrete event simulation model according to a preferred embodiment of the present application;

[0036] Figure 5 is an experimental result calculated based on a discrete event and Monte Carlo simulation method according to a preferred embodiment of the present application;

[0037] Figure 6 is a task profile definition diagram according to a preferred embodiment of the present application;

[0038] Figure 7 is a reliability equipment definition diagram according to a preferred embodiment of the present application, wherein (a) is a failure characteristic definition diagram of the reliability equipment, including a failure time interval distribution function and parameters, a maintenance duration distribution function and parameters, etc., and (b) is a start-stop cycle definition diagram of the reliability equipment, used for defining a working task sequence of the reliability equipment;

[0039] Figure 8 is a relationship component and logic component definition diagram according to a preferred embodiment of the present application, wherein (a) is a relationship type definition diagram of the relationship component, and (b) is a logic type definition diagram of the logic component;

[0040] Figure 9 is a reliability block diagram built according to a preferred embodiment of the present application. DETAILED DESCRIPTION

[0041] In order to make the objectives, technical solutions and advantages of the present application clearer, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and should not be used to limit the present application. In addition, the technical features involved in each embodiment of the present application described below can be combined with each other as long as they do not conflict with each other.

[0042] Monte Carlo simulation, as a numerical calculation method based on probability statistics, has also been widely used in system engineering and reliability engineering. The basic idea is to estimate the performance index of the system through a large number of random sampling, which is suitable for dealing with the uncertainty problem in complex systems. The combination of Monte Carlo simulation method and discrete event simulation can make up for the shortcomings of single method, and through random sampling and statistical analysis, the reliability of ship system under different working conditions can be more accurately evaluated and calculated.

[0043] A ship system reliability modeling and calculation method based on discrete event and Monte Carlo simulation, comprising the following steps:

[0044] Step 1: Establish a discrete event simulation model in the ship running process, and the specific steps are as follows:

[0045] Step 101: According to the operation principle of the ship system, a reliability block diagram of the system is established as a discrete event simulation model reference object.

[0046] Step 102: According to the overall task cycle of the system, a corresponding task sequence, task profile, and task phase calendar are defined in the discrete event simulation model. The task sequence is mainly used to describe the timing relationship of each typical task profile of the ship equipment in a task sequence process, mainly including task sequence name, task profile name, task profile order, relative start interval time between task profiles, and task profile allowed delay time. The task profile mainly analyzes the events and environment experienced by the main equipment in a specified task period, mainly including task profile name, task phase name, task phase order, task phase duration, and task phase allowed delay time. The task phase is mainly a basic constituent element of a task profile, including task phase name, task phase maintenance type, required equipment name, required equipment quantity, and required equipment relationship (series, parallel, voting).

[0047] Step 103: According to the equipment objects in the reliability block diagram, a corresponding "reliability equipment" object is established in the discrete event simulation model. The failure and maintenance time distribution type (exponential distribution, normal distribution, Weibull distribution) and parameters are specifically set, and the task sequence, task profile, and task phase of the reliability equipment in the start-stop cycle are set.

[0048] The set task sequence, task profile, task phase, failure and maintenance time distribution type and parameters are used to determine whether the reliability equipment is in a normal state, failure state, or non-planned state at the current time.

[0049] Step 104: According to the relationship between the equipment in the reliability block diagram, a corresponding "relationship component" object is established in the discrete event simulation model. The relationship type is specifically set, including task sequence, task profile, task phase, component relationship (hot backup / cold backup), and the involved reliability equipment.

[0050] The component relationship between the reliability equipment can be preset as hot backup or cold backup. Hot backup means that each directed connection line from the relationship component to the subsequent reliability equipment is available, and cold backup means that only one of all directed connection lines from the relationship component to the subsequent reliability equipment is available.

[0051] The set task sequence, task profile, and task phase are used to determine whether the relationship component is in a planned state or a non-planned state at the current time.

[0052] Step 105: According to the nodes in the reliability block diagram, establish corresponding "logical component" objects in the discrete event simulation model, and specifically set their logical types ("or", "and", "r / n"), logical switching rules, and the like. The "or" is the relationship of the device objects in the reliability block diagram in parallel, the "and" is the relationship of the device objects given in the reliability block diagram, and the "r / n" is "2 / 3" in the embodiment shown in the present embodiment. Figure 9 Figure 3 In the embodiment shown in the present embodiment, the "r / n" given in the reliability block diagram is "2 / 3", where r=2, r represents the number of normal device objects required at the node, and n=3, n represents the total number of predecessor reliability objects.

[0053] Step 106: According to the connections in the reliability block diagram, establish "connection line" objects in the discrete event simulation model, and connect the three components described above.

[0054] Step 107: Create a "reliability entry" object, which is used to generate a Token object. The Token object is a temporary object in the simulation model, is generated by the reliability entry object, is driven by the discrete event simulation engine, is guided according to the directed line segment, and has a time of 0 when passing through each component. The attribute of the Token object includes a flag parameter, where 0 represents normal, 1 represents failure, and 2 represents an unplanned state. Then, the Token object is connected to the starting position of the ship system through a connection line object, and a sampling time interval is defined. An "exit" object is created, which is used to collect the Token object after single sampling.

[0055] Step 108: Create a "simulation test" object, and define the simulation test, including input parameters, output parameters, test definition, and concurrency. The input parameter is defined as the index of the test, and takes a value of 1-N, where N is the total number of Monte Carlo simulations. The output parameter is the reliability of the system. The test definition is generated by the full permutation of the category of the input parameter, and the output parameter generates a full factorial test. The number of concurrent simulations is self-defined according to the hardware condition of the computing device, and is used to speed up the simulation test speed.

[0056] Step 2: Perform discrete event-based simulation reasoning and record the state, and the specific steps are as follows:

[0057] Step 201: Run the ship system simulation model based on discrete events;

[0058] Step 202: According to the token generation time interval Δt defined by the reliability entry, the simulation engine advances the simulation clock from T=0 to generate a Token object every Δt time, and initializes the flag state to 0.

[0059] ​Step 203: The token Token object moves along the directed line segment to the successor component, first acquires the current Token state as flag0 when the Token reaches the component, then removes the Token, and creates n new Token objects associated with n successor devices at the current position, and further judges:

[0060] If the current component type is a relationship component object, according to whether all the successor reliability objects B of the successor reliability object A are unplanned, if all are unplanned, the label of the Token of the successor reliability object A is set to 2; otherwise, it is judged whether the relationship between the successor reliability devices is cold backup or hot backup, when it is cold backup, at most one of the labels of the reliability devices is 0, and the labels of the rest are 2, when it is hot backup, the labels of the successor reliability devices are all flag0, and flag0 is the label of the Token currently passed to the successor reliability object A.

[0061] If the current component type is a reliability device object, according to the task sequence (judging whether it is in a planned or unplanned state) and the random failure function (judging whether it is in a normal or failure state) defined by the object, the current device state (normal, failure, or unplanned) is judged. If it is in an unplanned state, the flag of the new Token is 2 (unplanned); if it is in a failure state, the flag of the new Token is max (1, flag0); if it is in a normal state, the flag of the new Token is flag0, and the relevant custom script of the model is triggered.

[0062] If the current component type is a logical component object, first judge whether the number of Tokens contained in the current component is equal to the number of component predecessor nodes, if not, wait; if equal, judge the logic type, and then judge the logic switching time period corresponding to the current simulation time, so as to obtain the corresponding logic type and set the flag state of the component:

[0063] If it is "or", first judge whether there is 0 in the labels of the Num tokens, if there is, set the label of the current logical component to 0; if there is no 0 in the labels of the Num tokens, and there is 1 in the labels, set the label of the current logical component to 1; in other cases, the label of the current logical component is set to 2;

[0064] If it is "and", first judge whether there is 1 in the labels of the Num tokens, if there is, set the label of the current logical component to 1; if there is no 1 in the labels of the Num tokens, and there is 0 in the labels, set the label of the current logical component to 0; in other cases, the label of the current logical component is set to 2;

[0065] If it is "r / n", it corresponds to a redundant system. Redundant system refers to a system in which there are multiple identical components or subsystems, as long as a certain number of components or subsystems are working properly, the entire system can continue to run. If the number of flag state values of 0 (normal) in the current Token is greater than or equal to r, the component flag value is 0; otherwise, if r is greater than the current Token quantity or not all Token states are 2 (unplanned), the component flag value is 1 (failure), and the remaining case is 2 (unplanned).

[0066] Step 204: If there are multiple Token objects in the system at time t, iterate through all the reliability devices, relationship components, and logical components to perform step 3 to update and move the Token.

[0067] Step 205: If the Token reaches the exit or the number of subsequent reliability objects of the current component is 0, it means that the end of the system has been reached, and the Token is not deleted at this time. The flag state value of the Token represents the state of the system corresponding to this sampling (normal, failure, and unplanned).

[0068] Step 206: If the simulation end time is reached, the simulation is automatically stopped, and the reliability of the system can be obtained based on the proportion of Token values collected at the exit. The single simulation ends.

[0069] Step 3: Based on Monte Carlo simulation test and data statistical analysis, the specific steps are as follows:

[0070] Step 301: Based on the completion of the simulation model, define a temporary variable index with a value of 1-N and a step size of 1 for multiple simulation tests. In test one, index takes 1, and the test result is the reliability value R1. In test two, index takes 2, and the test result is the reliability value R2. In test N, index takes N, and the test result is the reliability value R n N tests, only the index value is different, and other variables (failure, maintenance distribution and parameters, sampling interval time, etc.) are the same.

[0071] Step 302: According to the basic principle of Monte Carlo simulation, the average value of the system reliability obtained after multiple simulations gradually approaches the accurate value as the number of simulations increases. Statistical analysis of the Monte Carlo simulation results is performed to calculate the reliability, mean time between failures (MTBF), failure probability, and other indicators of the system in different time periods, thereby calculating the system reliability. After N tests, the final average failure interval time and average maintenance time of the system are obtained.

[0072] The application will be further described below with reference to specific embodiments.

[0073] The modeling objects and corresponding icons required by the present application are shown in the following table: Figure 1 reliability entrance, reliability device, relationship component, logic component, directed connection line, reliability exit.

[0074] To further illustrate the completeness of the modeling objects, Figure 2 various logic topologies formed by the combination of various components are shown: series, parallel, shunt, voting, bridge structure. The error between the simulation test results of various topologies and the calculated values of mathematical theory is within the acceptable range, and the test process is omitted due to space constraints.

[0075] As shown in Figure 3 is an example of a reliability block diagram of a certain ship system, which is composed of a cooling water system, a condensate water system, a water making system, an oil system, an axle cooling water system, and a power system. Each system is in series, and each system has its own logic topology. The failure, repair time distribution, and start-stop cycle task sequence of the devices in the system are different, which together form a complex system.

[0076] Based on the above system reliability block diagram example and related data, the ship system reliability block diagram modeling method based on discrete event simulation described in the present application is further illustrated:

[0077] Step 1: According to the operation principle of the ship system, the reliability block diagram of the system is established as shown in Figure 3 , which is used as a reference object for modeling the discrete event simulation model.

[0078] Step 2: According to the operation process of the ship's devices, subsystems, and systems, a discrete event simulation model is established. In this example, the discrete event simulation software FactorySimulation is used:

[0079] According to the overall task cycle of the system, define the corresponding task sequence, task profile, and task stage calendar in the discrete event simulation model. In this example, a total task sequence lasting 20 days is defined, and the total task sequence is divided into four task profiles: low working condition stage 1 (0-1:0:0:0), high working condition stage 1 (1:0:0:0-10:0:0:0), low working condition stage 2 (10:0:0:0-11:0:0:0), and high working condition stage 2 (11:0:0:0-20:0:0:0) as shown in Figure 6 .

[0080] According to the device object in the reliability block diagram, a "reliability device" object is created, including a cooling water system, which is composed of 2 steam turbine circulating water pumps, 3 fresh water pumps, 2 seawater centralized cooling devices, 2 type I seawater pumps, and 2 type II seawater pumps; a condensate water system, which is composed of 1 condensate water module, 1 condensate water delivery source, 2 steam turbine feed water pump systems, and 2 auxiliary electric feed water pump units; a water making system, which is composed of 2 reciprocating salt removal pumps and 2 combined water delivery devices; an oil lubricating system, which is composed of 3 oil lubricating pumps; a shaft cooling water system, which is composed of 2 shaft cooling water pumps; and a power system, which is composed of 1 steam turbine generator unit, 2 small steam turbine generator units, 1 step-up DC converter, 1 step-up rectifier, 1 medium voltage DC distribution board, 2 variable frequency speed regulators, and 1 propulsion motor. The topological connection relationship of each device is shown in the reliability block diagram. For each device, its failure time distribution parameter and maintenance time distribution parameter are set, and the task sequence, task profile, and task stage of the reliability device in the start-stop cycle are set as shown in Figure 7 .

[0081] According to the relationship between the devices in the reliability block diagram, a "relationship component" object is created. The relationship type of the successor component is set according to the relationship, including the task sequence, task profile, task stage (customized), component relationship, and the reliability components involved as shown in Figure 8 .

[0082] According to the nodes in the reliability block diagram, a "logical component" object is created. The logical type and logical switching rule are set as shown in Figure 8 . Or indicates that one of the multiple devices is normally working, and indicates that all of the multiple devices are normally working. r / n indicates that the value is set according to the original value given in the reliability block diagram.

[0083] According to the connection lines of the reliability block diagram, a "connection line" object is established in the discrete event simulation model. The three components mentioned above are connected according to the reliability block diagram.

[0084] A "reliability entry" object is created and connected to the starting position of the ship system through the connection line object. The sampling time interval is defined as 20 minutes.

[0085] A "simulation test" object is created. The input parameter index is defined to take values from 1 to 10000 with a step of 1, and the output parameter is defined as the number of tokens in the three states (normal, failure, and non-planned) at the outlet. Finally, the experimental definition (10000 times of simulation in full factorial test) is initialized.

[0086] Step 3: Run the simulation test, perform multiple tests, and conduct data statistical analysis.

[0087] According to Token generation time, firstly, a Token is generated at T=0, i.e. the first sampling time is 0, and then a new Token is generated every 20 minutes,

[0088] The Token object moves along the directed line segment to the successor component, and when the Token reaches the component, firstly, the current Token is removed, and n new Tokens are created at the current position, where n is the number of successor reliability objects of the current device, and then further judgment is made. The ship system is described as follows:

[0089] The Token object moves along the directed connection line from the reliability entrance to the relationship component, and the successor components are determined to be the turbine circulating water pump 1 and the turbine circulating water pump 2, the relationship is the hot backup mode, and the task sequence is 0-20:0:0:0, which contains the current time, so the Tokens are created respectively, and their states are set to the current state (normal) of the component, and then the Tokens move along the connection line to the two successor components and are removed from the simulation model.

[0090] When the Token of the turbine circulating water pump 1 enters, firstly, the state of the current device is obtained according to the fault interval time and the maintenance duration, i.e. the start-stop cycle of the device: if it is a non-scheduled state (state 2), the flag value of the Token is updated to 2; if it is a fault state, it is further determined whether the successor reliability object logic component of the turbine circulating water pump is in a non-scheduled state, the state of the logic component object is determined to be normal, and the flag value of the Token is updated to 1 (fault); if it is a normal state, the flag state value is updated to 0 (normal), and the turbine circulating water pump 2 performs the same judgment process.

[0091] When the Token values on the above two devices are updated, the Tokens flow along the connection line to the logic component object, the logic type of the logic component is “or”, so as long as one of the two Tokens has a flag value of 0 (normal), the judgment is ended, and a new Token is generated on the logic component, the state value of the Token is updated to the state just obtained, and the Token moves along the connection line to the next component and removes the current two Tokens.

[0092] If there are multiple Token objects at time t, all reliability devices, relationship components, and logic components are traversed to perform step 3, to realize the updating and moving of the Token.

[0093] When the Token passes through the cooling water system, the condensate water system, the water making system, the lubricating oil system, the shaft cooling water system, and the power system, it finally reaches the outlet, and the flag value of the Token object at the outlet is the reliability of the system in this sampling process (normal, fault, non-scheduled).

[0094] Repeat the above process until the end of the simulation time (20:0:0:0), the simulation is automatically stopped, all Token at the statistics export, the proportion of the flag value is 0 Token, namely the average reliability of the system in this simulation process.

[0095] Repeat the above process until all the test, test after viewing the test data can be obtained system reliability: 99.744310%. According to the principle of Monte Carlo simulation, the value is very close to the theoretical reliability of the system, and gradually approach with the increase of the number of simulation trials.

[0096] Those skilled in the art will readily understand that the above described are only the preferred embodiments of the present application, and not to limit the present application, any modification, equivalent replacement and improvement within the spirit and principles of the present application, etc., should be included in the scope of protection of the present application.

Claims

1. A method for calculating the reliability of ship systems based on discrete events and Monte Carlo simulation, characterized in that, The method includes the following steps: (a) Construct a simulation model based on the reliability block diagram of the ship system to be processed. The simulation model includes a reliability object, a directed connection line, a reliability inlet, and a reliability outlet. The reliability inlet is set at the initial position of the simulation model, the reliability outlet is set at the end of the simulation model, and the directed connection line is used to connect the reliability object. (b) For the i-th simulation test, set the simulation time T and the time interval Δt for token generation. During the simulation time T, generate multiple tokens with a tag of 0 at the reliability entry point according to the time interval Δt. Each token is passed backward along its respective directed connection line. The tag is updated during the transmission process. When the token reaches the reliability exit point, the corresponding tag is the final tag. The final tag of all tokens in this simulation test is obtained in this way. (c) i = i + 1, repeat step (b) until i > N, obtain the final label of all tokens in all simulation tests, and use the final label of all tokens to calculate the reliability of the simulation model, where N is the number of simulation tests set according to the total task of the ship system to be processed. The reliability objects include reliability devices, relational components, and logical components. The token's tags include normal state, invalid state, and unplanned state, with corresponding values ​​of 0, 1, and 2, respectively.

2. The method for calculating the reliability of a ship system based on discrete events and Monte Carlo simulation as described in claim 1, characterized in that, The reliability device is the device object in the reliability block diagram. The relationship component is set at the front end of multiple parallel reliability devices, and the logic component is set at the end of multiple parallel reliability devices.

3. A method for calculating the reliability of a ship system based on discrete events and Monte Carlo simulation as described in claim 1 or 2, characterized in that, The overall task includes multiple simulation experiments. Each simulation experiment includes one or more task sequences. Each task sequence includes one or more task profiles. Each task profile includes one or more task stages. Each task profile sets all the task stages included in the simulation experiment. Each task stage sets the start and end times of the current task stage.

4. The method for calculating the reliability of a ship system based on discrete events and Monte Carlo simulation as described in claim 3, characterized in that, When setting up the reliability device in the simulation model, it is necessary to set the task sequence, task profile, task stage, and distribution type and parameters of failure and maintenance time of the reliability device in the simulation test. The set task sequence, task profile, task stage, and distribution type and parameters of failure and maintenance time are used to determine whether the reliability device is in a normal state, a failed state or an unplanned state at the current moment. When setting up the relationship component, it is necessary to set the task sequence, task profile, task stage, and component relationships between the reliability devices involved in the simulation test. The set task sequence, task profile, and task stage are used to determine whether the relationship component is in a planned or unplanned state at the current moment. The component relationships between reliability devices are preset to hot backup or cold backup. Hot backup means that every directed connection from the relationship component to the subsequent reliability device is feasible, while cold backup means that only one of the directed connections from the relationship component to the subsequent reliability device is feasible. The logic component is configured according to the relationship between the device objects in the reliability block diagram. When the relationship between the device objects in the reliability block diagram is parallel, the logic component is configured as OR; when the relationship between the device objects in the reliability block diagram is &, the logic component is configured as AND; when the relationship between the device objects in the reliability block diagram is r / n, the logic component is configured as r / n, where r is the number of normal device objects required at the node and n is the total number of predecessor reliability objects.

5. The method for calculating the reliability of a ship system based on discrete events and Monte Carlo simulation as described in claim 4, characterized in that, In step (b), the tag is updated during transmission in the following manner: When a token is passed to a subsequent reliability object A, the current token is removed, m tokens are created, and the tags of each token are set according to preset rules. The m tokens are then passed to the subsequent reliability object B, where m is the number of subsequent reliability objects B of the subsequent reliability object A. This process continues until the token is passed to the last reliability object in the simulation model. The token and tag in the last reliability object are directly passed to the reliability exit, thus completing one token passing cycle.

6. The method for calculating the reliability of a ship system based on discrete events and Monte Carlo simulation as described in claim 5, characterized in that, The preset rules are as follows: When the successor reliability object A is a relational component, the following checks if all successor reliability objects B of A are unplanned. If all are unplanned, the tag of the token for successor reliability object A is set to 2. Otherwise, it determines whether the relationship between the successor reliability devices is a cold backup or a hot backup. When it is a cold backup, at most one reliability device among all successor reliability devices has a tag of 0, and the rest have tags of 2. When it is a hot backup, all successor reliability devices have tags of 2. , The tag for the token currently passed to the successor reliability object A; When the subsequent reliability object A is a reliability device, the current status of the reliability device is determined according to the task sequence, task profile, task stage, distribution type and parameters of failure and maintenance time set by the reliability device, and the label of the reliability device is set according to the status. When the successor reliability object A is a logical component, all Num tokens of the Num predecessor objects directly connected to the logical component arrive at the logical component. According to the logic type set on the logical component, the tag of the token on the logical component is set. The predecessor object is a reliability component or a logical component.

7. The method for calculating the reliability of a ship system based on discrete events and Monte Carlo simulation as described in claim 5, characterized in that, When the subsequent reliability object A is a reliability device, the status of the reliability device is normal, faulty, or unplanned. When it is unplanned, the token tag is 2; when it is faulty, the token tag is... When it is normal, the label is: .

8. The method for calculating the reliability of a ship system based on discrete events and Monte Carlo simulation as described in claim 5, characterized in that, When the subsequent reliability object A is a logical component, if the logical component is either A or B, first determine whether there is a 0 in the labels of Num tokens. If there is, set the label of the current logical component to 0. If there is no 0 in the labels of Num tokens and there is a 1 in the labels, set the label of the current logical component to 1. In other cases, the label of the current logical component is set to 2. When the logic component is AND, first check if there is a 1 among the labels of Num tokens. If there is, set the label of the current logic component to 1; if there is no 1 among the labels of Num tokens, but there is a 0 among the labels, set the label of the current logic component to 0; otherwise, set the label of the current logic component to 2. When the logical component is r / n, if the number of 0s in the labels of Num tokens is greater than or equal to r, then the label of the current logical component is set to 0; if r is greater than Num or the labels of Num tokens are not all 2, then the label of the current logical component is set to 1; otherwise, the label of the current logical component is set to 2.

9. A ship system reliability calculation system based on discrete events and Monte Carlo simulation, characterized in that, The system includes an actuator for performing a ship system reliability calculation method based on discrete events and Monte Carlo simulation as described in any one of claims 1-8.

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