CN113139676A - Complex system selective maintenance decision method and device based on resource constraint - Google Patents
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Abstract
The invention provides a complex system selective maintenance decision method based on resource constraint, which comprises a serial-parallel system for executing tasks, wherein the serial-parallel system is formed by connecting a plurality of independent subsystems in series, and each subsystem is formed by connecting a plurality of components in parallel; determining decision calculation variables of selective maintenance components during the tasks of the series-parallel system; giving a decision calculation variable value, and calculating to obtain the state and age of each part after the current task is completed; and determining the length of the next task and the interval time between the next task and the current task, and solving the optimal solution of a selective maintenance decision model established by taking the reliability as the maximum target by adopting a preset component maintenance priority strategy by combining the state and the age of each component after the current task is completed to obtain the selective maintenance component and the corresponding maintenance content thereof. By implementing the invention, the defects of the existing intelligent algorithm can be overcome, and the cost for verifying the correctness of the result can be saved.
Description
Technical Field
The invention relates to the technical field of modern manufacturing, in particular to a complex system selective maintenance decision method and a complex system selective maintenance decision device based on resource constraint.
Background
With the development of the modern manufacturing system towards the complexity and the intellectualization and the trend of the flexible production mode, the manufacturing system has high function integration, variable working load and complex operation environment, and the great challenge is faced to ensure the high reliability of the system under the constraint of limited maintenance resources. In recent years, the problem of selective maintenance of complex systems has begun to be of interest to experts in academia and industry, with great success.
As early as half a century ago, the academia and industry were aware of the importance of system and equipment maintenance. Heretofore, maintenance decisions have undergone an evolution from post-maintenance/remedial maintenance to periodic, episodic, and predictive maintenance. In these maintenance decision studies, the decision model with the least total maintenance cost expected, the least average maintenance cost per unit time expected, or the most economic benefit objective during the service period of the system is most common. In fact, because maintenance resources (such as maintenance times, total maintenance cost, total maintenance time and maintenance personnel) are often limited, a maintenance decision should play the role of the limited maintenance resources to the maximum extent, so that 'good steel is used on a blade', and a system can be operated reliably and safely and can complete expected tasks all the time.
Because maintenance of all aged or failed components is often not done during the interval between adjacent tasks due to the constraints of maintenance resources (e.g., time, expense, maintenance equipment and personnel) between adjacent tasks, a decision maker may selectively perform maintenance on only a portion of the equipment or components in the system, and such maintenance decisions are referred to as "selective maintenance".
In essence, selective maintenance decisions are a class of visual maintenance decision problems for multi-component systems with limited resources. Compared with other types of maintenance decision problems, the selective maintenance decision has the particularity that: (1) selectivity of maintenance component: on one hand, due to limited maintenance resources, it is often necessary to strategically select some components for maintenance, rather than maintaining all aged or failed components; on the other hand, since there are a plurality of alternative maintenance actions (e.g., cleaning, lubrication, or replacement), and implementing different maintenance actions consumes different maintenance resources and achieves different maintenance effects, the decision maker needs to select the most appropriate maintenance action from the alternative maintenance actions to perform. (2) Limitation of maintenance time: maintenance actions of the system or component are limited by task execution time and can only be scheduled to be executed during the task interval, so that selective maintenance decisions are limited in time. (3) Task-oriented success: unlike most maintenance decision models that target minimizing the total cost of desired maintenance, in the selective maintenance decision model, limited maintenance resources are known constraints and the maintenance strategy is targeted to maximize the probability of system task success, .
Due to the importance and specificity of selective maintenance decisions, the theory and method are well-established by the industry and academia. A close concern of the military sector. Since Rice equals 1998 put forward the basic concept of selective maintenance decision, many scholars both at home and abroad deeply explore the problem of selective maintenance decision from multiple aspects and get a lot of remarkable results. Therefore, selective maintenance decision research based on resource constraint has important significance.
As the complexity of selective maintenance models increases, the maintenance strategy optimization problem cannot be converted to simple mathematical programming like Cassady et al. Thus, a variety of advanced intelligent algorithms are applied to the solution of selective maintenance.
However, the disadvantages of these advanced intelligent algorithms have to be considered. For example, particle swarm algorithms are difficult to converge to local optima; for another example, the parameters of the simulated annealing algorithm are difficult to control, and the optimal value cannot be guaranteed to be converged once; as another example, the selection of three operator parameters of a genetic algorithm can severely impact the quality of the solution, and most of these parameters are empirically based. Furthermore, the workload is not imaginable if an enumeration solution is used to verify the correctness of the results.
Disclosure of Invention
The technical problem to be solved by the embodiments of the present invention is to provide a resource constraint-based method and apparatus for selectively making a maintenance decision for a complex system, which can not only overcome the disadvantages of the existing intelligent algorithm, but also save the cost for verifying the correctness of the result.
In order to solve the above technical problem, an embodiment of the present invention provides a complex system selective maintenance decision method based on resource constraint, where the method includes the following steps:
s1, determining a series-parallel system for executing the task; the series-parallel system is formed by connecting a plurality of independent subsystems in series, and each subsystem is formed by connecting a plurality of components in parallel;
s2, determining decision calculation variables of the selective maintenance component during the serial-parallel system task; the decision calculation variables comprise Boolean quantity parameters of a part life model subjected to Weber distribution, minimum maintenance time of the part, correction replacement time of the part and preventive replacement time of the part;
s3, providing decision calculation variable values of the components selectively maintained by the series-parallel system in the current task, and calculating to obtain the states and ages of the components after the current task is completed;
s4, determining the length of the next task and the interval time between the next task and the current task, and solving the optimal solution of a selective maintenance decision model established by taking reliability as the maximum target by adopting a preset component maintenance priority strategy according to the state and age of each component after the current task is completed to obtain a selective maintenance component and corresponding maintenance content thereof; wherein the maintenance content is corrective replacement or preventive replacement.
Wherein the method further comprises:
and obtaining the value of the reliability of the series-parallel system in the next task according to the optimal solution obtained by the selective maintenance decision model.
The embodiment of the invention also provides a complex system selective maintenance decision system based on resource constraint, which comprises:
a system configuration determining unit for determining a series-parallel system for executing a task; the series-parallel system is formed by connecting a plurality of independent subsystems in series, and each subsystem is formed by connecting a plurality of components in parallel;
the system parameter determining unit is used for determining decision calculation variables of the selective maintenance component during the serial-parallel system task; the decision calculation variables comprise Boolean quantity parameters of a part life model subjected to Weber distribution, minimum maintenance time of the part, correction replacement time of the part and preventive replacement time of the part;
the decision calculation unit is used for providing decision calculation variable values of the components selectively maintained by the serial-parallel system in the current task and calculating to obtain the states and ages of the components after the current task is completed;
the selective maintenance component and content output unit is used for determining the length of the next task and the interval time between the next task and the current task, and solving the optimal solution of a selective maintenance decision model which is constructed by taking the reliability as the maximum target by adopting a preset component maintenance priority strategy by combining the state and the age of each component after the current task is completed to obtain the selective maintenance component and the corresponding maintenance content thereof; wherein the maintenance content is corrective replacement or preventive replacement.
Wherein, still include: a reliability calculation unit; wherein,
and the reliability calculation unit is used for obtaining the value of the reliability of the series-parallel system in the next task according to the optimal solution obtained by the selective maintenance decision model.
The embodiment of the invention has the following beneficial effects:
in order to ensure the accuracy of the selective maintenance result of the complex series-parallel system under the resource constraint, the invention constructs a selective maintenance decision model considering the component maintenance priority, and adopts the component maintenance priority strategy to solve the optimal solution, thereby overcoming the defects of the existing intelligent algorithm and saving the cost for verifying the correctness of the result.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is within the scope of the present invention for those skilled in the art to obtain other drawings based on the drawings without inventive exercise.
Fig. 1 is a flowchart of a complex system selective maintenance decision method based on resource constraints according to an embodiment of the present invention;
fig. 2 is a diagram of a selective maintenance decision process in step S4 in a complex system selective maintenance decision method based on resource constraints according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of a series-parallel system in an application scenario of a complex system selective maintenance decision method based on resource constraint according to an embodiment of the present invention;
fig. 4 is a schematic structural diagram of a series-parallel system in another application scenario of the complex system selective maintenance decision method based on resource constraint according to the embodiment of the present invention;
FIG. 5 is a task reliability graph simulated by the series-parallel system in FIG. 4 when a task is executed 20 times;
fig. 6 is a schematic structural diagram of a complex system selective maintenance decision based on resource constraint according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in further detail with reference to the accompanying drawings.
As shown in fig. 1, a method for selectively maintaining and deciding a complex system based on resource constraint according to an embodiment of the present invention includes the following steps:
step S1, determining a series-parallel system for executing the task; the series-parallel system is formed by connecting a plurality of independent subsystems in series, and each subsystem is formed by connecting a plurality of components in parallel;
a specific procedure is that, in an industrial environment, a series-parallel system is intended to perform a series of tasks with a certain spacing between two adjacent tasks. These interruptions between successive tasks provide an opportunity to perform maintenance on the components of the series-parallel system.
The series-parallel system is formed by connecting i (i ═ 1, 2.., m) independent subsystems in series, and each subsystem i is formed by ni(j＝1,2,...,ni) The components are connected in parallel, and the states of each component, each subsystem and the series-parallel system all run normally or have faults.
Each component in the series-parallel system is composed of CijWhere i and j represent the location of the component in the subsystem. Let Xij,kAnd Yij,kThe states of the component at the start of the task k ( k 1, 2..) and at the end of the task k ( k 1, 2. -) are indicated.
The state of the component at the beginning of task k may be represented as:
similarly, the state of the subsystem and the entire series-parallel system at the start of task k is also denoted by {0,1 }.
Likewise, the state of the component at the end of task k may be written as:
similarly, the state of the subsystem and the entire series-parallel system at the end of task k is also denoted by {0,1 }.
Step S2, determining decision calculation variables for selectively maintaining the components during the task of the series-parallel system; the decision calculation variables comprise Boolean quantity parameters of a part life model subjected to Weber distribution, minimum maintenance time of the part, correction replacement time of the part and preventive replacement time of the part;
specifically, in order to ensure smooth and reliable execution of each task, it is crucial to take the best maintenance measure within a limited time by taking full advantage of time, so that after each task is executed, the state of each component needs to be selected according to the maintenance measure, as shown in table 1 below.
TABLE 1
Wherein, Bij,kRepresents the lifetime of a component j of subsystem i at the start of task k; hij,kIndicates part C in maintenance cycle kijAnd represents it using a unique constant; l iskIs the length of task k.
When H is presentij,kAt values of 0 and 1, the component life is not affected, and therefore the reliability of the component does not change; when H is presentij,kAt values of 2 or 3, the replacement operation will result in the component being restarted to a "good as new" state.
Order Sij,kMeans C for indicating the end of task kijAssuming an initial service life of 0, for each part, it is calculated by the formula:
as can be seen from the formula (2-1), after k-1 maintenance operations, the partLife will be from Sij,k-1Update to eij,k-1Sij,k-1. Wherein e is the improvement coefficient of effective age, when e ═ 1 denotes minimal repair; e-0 indicates substitution.
The variation of the different post-maintenance-operation fault rate functions may be determined as:
in the formula (2-2), Hij,k-1E {0,1} represents no maintenance or minimal maintenance operation; hij,k-1E {2,3} represents that replacement operation is executed, and the service life of the part is repaired as new; x is the number ofkIs a component CijEffective life of, lambdaij,kIs a component CijThe failure rate of (c).
Ideally, betaiFailed parts > 1 should be replaced before the next job, betaiFailed parts ≦ 1 should be maintained minimally, βiNormal parts > 1 should be replaced. However, due to the limited time, all maintenance operations may not be performed.
By expressing these times as known constants, component C can be representedijTime elapsed Tij(Hij,k) Expressed as:
in the above formula (2-3), Hij,k0 means no time spent; hij,kDenotes performing a minimum maintenance operation, where tij,mrRepresenting a time to perform a minimum maintenance operation; h ij,k2 denotes the replacement operation of the faulty component, where tij,frIndicating a time to perform a corrective replacement; h ij,k3 denotes a preventive replacement operation, where tij,prIs the time to perform the preventive replacement. Thus, for decision variable Hij,kThe associated maintenance time for the component may be calculated, and the total maintenance time for the entire system may be determined as:
as is apparent from the formula (2-4), for the maintenance level Hij,kCan determine the corresponding time involved in the system maintenance, i.e. the involved parameters include the minimum maintenance time t of the componentij,mrTime t for correcting and replacing partsij,fr and time t for preventive replacement of componentij,pr。
Meanwhile, because the service life model of the component j in the subsystem i obeys the Weber distribution, the decision-making calculation variables for selectively maintaining the component during the task of the series-parallel system also comprise the shape parameter beta of the Weber distributioniAnd a proportional parameter ηi。
Step S3, providing decision calculation variable values of the components selectively maintained by the serial-parallel system in the current task, and calculating to obtain the states and ages of the components after the current task is completed;
the specific process is that a calculation formula of the Maintenance Priority (MPOC) of the part is given:
in the formula (2-5), P' (C)ij,k0) indicates that the fault component is repaired at the end of the k task, thereby improving the expected value of the system reliability of the k +1 task; p (C)ij,k0) represents the reliability value of the k +1 task when the failed component is not performing any maintenance activity; p' (C)ij,k1) indicating that a normal working part replaces a k task at the end of the task, and improving the system reliability expected value of the k +1 task; p (C)ij,k1) represents the reliability value of the k +1 task when the normally working part is not doing any maintenance activities.
Giving the shape parameter β during the current taskiProportional parameter etaiMinimum maintenance time t of componentij,mrTime t for correcting and replacing partsij,frAnd a preventive replacement time t of the componentij,prAssignment is made in combination with equation (2-5)) Calculating to obtain the state and age of each part after the current task is completed, such as Sij,k、Yij,k。
Step S4, determining the length of the next task and the interval time between the next task and the current task, and combining the state and age of each component after the current task is completed, and solving the optimal solution of a selective maintenance decision model which is constructed by taking reliability as the maximum target by adopting a preset component maintenance priority strategy to obtain a selective maintenance component and corresponding maintenance content thereof; wherein the maintenance content is corrective replacement or preventive replacement.
The specific process is that firstly, a reliability model is constructed in advance, and specifically: each subsystem is composed of niThe components are connected in parallel, so that their state at the start of the task can be determined as:
the state of the whole system at the start of the task is defined as:
likewise, each subsystem consists of niThe components are connected in parallel, so that their state at the end of the task can be determined as:
the state of the whole series-parallel system at the end of the task can be determined as follows:
the reliability of the component over the mission period can be expressed as:
the reliability of subsystem i during a task may be determined as:
also, the reliability of the system during the task can be expressed as:
the probability of completing the next task can be recursively determined for each component based on the age at the start of the next task, its initial state, and the duration of the task. Therefore, the reliability of the entire series-parallel system can be determined by using the formula (2-12).
Secondly, a selective maintenance decision model is constructed with reliability as the maximum target, and the decision objective function and the relevant constraints can be expressed as:
Objective:
Subject to:
in this formula, constraints (2-14) indicate that the system maintenance time cannot exceed the maximum interval, and constraints (2-15) and (2-16) indicate that the state of the component at the start of the next task depends on the corresponding maintenance operation being performed and the end of the previous task.
Finally, as shown in equations (2-13) - (2-16), the proposed selective maintenance model is a complex, non-linear, discrete problem, and the number of solutions will increase exponentially with the number of elements in the system. Due to their ease of use and adaptability to problems, evolutionary algorithms (e.g., Genetic Algorithms (GA), Differential Evolution (DE), etc.) are widely used in maintenance optimization. In order to ensure the accuracy of the selective maintenance result, the method based on MPOC is adopted herein to solve the selective maintenance problem, and the decision process is shown in fig. 2; wherein, TrDenotes remaining maintenance time, KmaxIndicating the number of tasks specified.
When each task ends, the age of each part is updated and the status of each part is simulated by generating a random number. Calculating corresponding value of MPOC according to corresponding state of each element in the system by the formula (2-5), and using rmAnd (4) acting a selection preference set, namely taking the maintenance interval as a constraint condition, wherein the MPOC time ratio plays a decisive role in the effect of selective maintenance. Suppose the first step in the maintenance action set is { a, b, c, d, e }, where c represents the reference maintenance (MPOC time vs. maximum maintenance action r)mIndicating), then maintenance actions are reserved for which MPOC is greater than or equal to. In this way, the first choice of maintenance operations is reduced, that is, the final set of solutions is reduced from { { a, K }, { b, K }, { c, L }, { d, K }, { e, K } } to { { a, K }, { b, K }, { c, K } }. And the remaining option will make the next selection alone until no time remains for further maintenance operations.
It is noted that the reserved maintenance operations described above satisfy the constraints. After all maintenance solutions are unable to perform the next maintenance operation, the best solution is selected from the excellent solution set.
The calculation is finished until the system time reaches the specified KmaxAnd obtains a set C of maintenance components and a corresponding set H of maintenance options, i.e. selective maintenance components and their corresponding maintenance content.
In an embodiment of the invention, the method further comprises:
and obtaining the value of the reliability of the series-parallel system in the next task according to the optimal solution obtained by the selective maintenance decision model.
As shown in fig. 3, an application scenario of the complex system selective maintenance decision method based on resource constraint according to the embodiment of the present invention is further described:
in fig. 3, a series-parallel system is considered in which two subsystems are connected in series, and each subsystem has two components connected in parallel.
The system parameters and the time required for various maintenance operations are given and the status and age of the components are calculated as shown in table 2 below:
TABLE 2
Suppose the length of the next task is Lk+1The time interval between current tasks is T8 max8. A decision to increase reliability must be made for the next task. From the existing data, MPOC for maintenance measures was calculated as equation (2-5), MOPC is shown in Table 3 below.
TABLE 3
As can be seen from table 3, the priority for replacing the failed component 3 is highest (1.8767), but the MPOC time ratio for the component 3 is small. Since the MPOC time ratio of the preventive replacement part 4 is the largest, it is used as a reference operation, and a maintenance operation in which the MPOC is smaller than this operation is eliminated. In this way, the first choice of maintenance operations is reduced, i.e. the final solution set is reduced. And the remaining option will make the next selection alone until no time remains for further maintenance operations. Finally, in the excellent solution set, the maintenance component set C ═ {1,2,3,4} and the maintenance option set H ═ 3,3,2,3} are obtained. According to the maintenance strategy described above, the system can obtain maximum reliability on the next task (0.8925).
In order to verify the correctness of the method, the method is proved by adopting an enumeration method. Since each component has four components and two binary decision variables, there are 256 theoretical maintenance options, only 24 of which are feasible. The details are shown in table 4 below.
TABLE 4
As can be seen from the data in table 4, the method proposed herein results in the optimal solution of feasible solutions, with maintenance time satisfying the constraints. The above examples demonstrate the feasibility and correctness of the decision model approach presented herein.
As shown in fig. 4 and fig. 5, another application scenario of the complex system selective maintenance decision method based on resource constraint according to the embodiment of the present invention is further described:
in fig. 4, the series-parallel system is formed by connecting subsystems 1,2 and 3 in series; the subsystem 1 is formed by connecting components 1,2 and 3 in parallel, the subsystem 2 is formed by connecting components 4 and 5 in parallel, and the subsystem 3 is formed by connecting components 6, 7, 8 and 9 in parallel.
The system parameters and the time required for various maintenance operations are given and the status and age of the components are calculated as shown in table 5 below:
TABLE 5
Suppose the length of the next task is Lk+1100, the time interval between current tasks is T max8. The main problem to be solved is to determine the best maintenance combination by maximizing the reliability of the system in the next task within a limited task time. The MPOC for the maintenance measure is calculated according to equation (2-5) from the prior art and is shown in Table 6 below.
TABLE 6
As can be seen from table 6, the replacement priority of the failed component 4 is highest, and the MPOC time ratio of the component 4 is also largest. Therefore, the replacement of the malfunctioning part 4 is the first operation. The selection concept has been described in detail in the above section and case verification. The program is executed in Matlab2014, resulting in a set of maintenance components C ═ {1,2,4,5} and a corresponding set of options H ═ 2,3,2,3 }. With the above maintenance strategy, the system can obtain maximum reliability for the next task (0.9474).
Assuming that the task is executed 20 times, the task length and task interval remain unchanged. The task reliability curves are shown in fig. 5 by multiple simulations. As can be seen from fig. 5, the selective maintenance decision model proposed herein enables the system to remain within a stable reliability range, indicating that a selective maintenance strategy is feasible. The initial reliability of the system was 0.9251, and after four tasks, the reliability stabilized at 0.8936.
As shown in fig. 6, an embodiment of the present invention provides a complex system selective maintenance decision system based on resource constraint, including:
a system configuration determining unit 110 for determining a series-parallel system for performing a task; the series-parallel system is formed by connecting a plurality of independent subsystems in series, and each subsystem is formed by connecting a plurality of components in parallel;
a system parameter determining unit 120, configured to determine a decision calculation variable for performing selective maintenance on components during a task of the serial-parallel system; the decision calculation variables comprise Boolean quantity parameters of a part life model subjected to Weber distribution, minimum maintenance time of the part, correction replacement time of the part and preventive replacement time of the part;
the decision calculation unit 130 is configured to provide a decision calculation variable value of the component selectively maintained by the serial-parallel system at the current task, and calculate to obtain the state and age of each component after the current task is completed;
the selective maintenance component and content output unit 140 is configured to determine a length of a next task and an interval time between the next task and a current task, and, in combination with a state and an age of each component after the current task is completed, find an optimal solution for a selective maintenance decision model constructed with reliability as a maximum target by using a preset component maintenance priority policy, so as to obtain a selective maintenance component and maintenance content corresponding to the selective maintenance component; wherein the maintenance content is corrective replacement or preventive replacement.
Wherein, still include: a reliability calculation unit; wherein,
and the reliability calculation unit is used for obtaining the value of the reliability of the series-parallel system in the next task according to the optimal solution obtained by the selective maintenance decision model.
The embodiment of the invention has the following beneficial effects:
in order to ensure the accuracy of the selective maintenance result of the complex series-parallel system under the resource constraint, the invention constructs a selective maintenance decision model considering the component maintenance priority, and adopts the component maintenance priority strategy to solve the optimal solution, thereby overcoming the defects of the existing intelligent algorithm and saving the cost for verifying the correctness of the result.
It should be noted that, in the above device embodiment, each included unit is only divided according to functional logic, but is not limited to the above division as long as the corresponding function can be achieved; in addition, specific names of the functional units are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present invention.
It will be understood by those skilled in the art that all or part of the steps in the method for implementing the above embodiments may be implemented by relevant hardware instructed by a program, and the program may be stored in a computer-readable storage medium, such as ROM/RAM, magnetic disk, optical disk, etc.
The above disclosure is only for the purpose of illustrating the preferred embodiments of the present invention, and it is therefore to be understood that the invention is not limited by the scope of the appended claims.
Claims (5)
1. A resource constraint-based complex system selective maintenance decision method is characterized by comprising the following steps:
s1, determining a series-parallel system for executing the task; the series-parallel system is formed by connecting a plurality of independent subsystems in series, and each subsystem is formed by connecting a plurality of components in parallel;
s2, determining decision calculation variables of the selective maintenance component during the serial-parallel system task; the decision calculation variables comprise Boolean quantity parameters of a part life model subjected to Weber distribution, minimum maintenance time of the part, correction replacement time of the part and preventive replacement time of the part;
s3, providing decision calculation variable values of the components selectively maintained by the series-parallel system in the current task, and calculating to obtain the states and ages of the components after the current task is completed;
s4, determining the length of the next task and the interval time between the next task and the current task, and solving the optimal solution of a selective maintenance decision model established by taking reliability as the maximum target by adopting a preset component maintenance priority strategy according to the state and age of each component after the current task is completed to obtain a selective maintenance component and corresponding maintenance content thereof; wherein the maintenance content is corrective replacement or preventive replacement.
2. The resource constraint-based complex system selective repair decision method of claim 1, the method further comprising:
and obtaining the value of the reliability of the series-parallel system in the next task according to the optimal solution obtained by the selective maintenance decision model.
3. The resource constraint-based complex system selective repair decision method as claimed in claim 1, wherein the complex system is a modular press battery system.
4. A resource constraint based complex system selective maintenance decision system, comprising:
a system configuration determining unit for determining a series-parallel system for executing a task; the series-parallel system is formed by connecting a plurality of independent subsystems in series, and each subsystem is formed by connecting a plurality of components in parallel;
the system parameter determining unit is used for determining decision calculation variables of the selective maintenance component during the serial-parallel system task; the decision calculation variables comprise Boolean quantity parameters of a part life model subjected to Weber distribution, minimum maintenance time of the part, correction replacement time of the part and preventive replacement time of the part;
the decision calculation unit is used for providing decision calculation variable values of the components selectively maintained by the serial-parallel system in the current task and calculating to obtain the states and ages of the components after the current task is completed;
the selective maintenance component and content output unit is used for determining the length of the next task and the interval time between the next task and the current task, and solving the optimal solution of a selective maintenance decision model which is constructed by taking the reliability as the maximum target by adopting a preset component maintenance priority strategy by combining the state and the age of each component after the current task is completed to obtain the selective maintenance component and the corresponding maintenance content thereof; wherein the maintenance content is corrective replacement or preventive replacement.
5. The resource constraint-based complex system selective repair decision system of claim 4, further comprising: a reliability calculation unit; wherein,
and the reliability calculation unit is used for obtaining the value of the reliability of the series-parallel system in the next task according to the optimal solution obtained by the selective maintenance decision model.
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