Flexible manufacturing production system comprehensive reliability characterization method based on composite model

Abstract

The invention discloses a comprehensive reliability characterization method for a flexible manufacturing production system based on a composite model, relates to the technical field of system reliability, and evaluates the reliability of the flexible manufacturing production system from different dimensions by constructing a single reliability characterization model of each dimension. And after the reliability evaluation result of each single reliability representation model is obtained, performing weighted summation on the system production reliability, the system equipment reliability, the system process reliability, the system resource reliability, the system quality reliability and the system network reliability based on the weight of each dimension to obtain the system reliability of the flexible manufacturing production system. According to the method, the single reliability characterization result of each dimension serves as input data of comprehensive reliability evaluation calculation of the system, the reliability of the flexible manufacturing production system is evaluated from multiple dimensions, the reliability of the flexible manufacturing production system can be comprehensively and accurately evaluated, and therefore the flexible manufacturing production efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of system reliability, and more specifically to a system comprehensive reliability characterization method for flexible manufacturing production based on a composite model. Background Art

[0002] With the increasing demand for multi-variety and variable batch production of complex aerospace components and high-end equipment, the production model has gradually changed from the traditional model to flexible agile manufacturing, which has promoted the flexible and reconfigurable characteristics of the production process to cope with emergencies such as emergency orders and resource allocation. Therefore, for the flexible manufacturing production process, ensuring the reliability of its production system has become a crucial issue.

[0003] At present, the reliability evaluation methods for flexible manufacturing systems mainly include Petri nets, reliability block diagrams, Monte Carlo methods, fault trees and other methods. In existing research, many scholars have studied reliability from different angles. For example, in terms of production, Meng Jiwei, Wang Shaohua, Chen Xiao and other scholars from Southwest Jiaotong University published in "Reliability Study of Order Delivery Period of Serial Production System Based on Monte Carlo": Analyze the process flow chart of the production system, determine the number of batches, determine the production system network planning model, and then use the three-time estimation method and Monte Carlo method to simulate the order delivery period, and finally obtain the estimated value of the delivery period and the reliability of the expected delivery period; for another example, regarding the impact of product size quality on the reliability of the manufacturing system, Sun Jiwen, Xi Lifeng, Pan Ershun and other scholars analyzed the cross-effects of product size measurement deviation, manufacturing system element failure and performance degradation in the multi-station manufacturing system in the article "Modeling and Analysis of Manufacturing System Reliability for Product Size Quality", and established product size quality, failure, and performance degradation models respectively, and then established a manufacturing system reliability integration model, and obtained the manufacturing system reliability through solving. It can be seen that the existing reliability assessment methods of flexible manufacturing production systems mainly evaluate the reliability of flexible manufacturing production systems from a single dimension. However, in the actual production process, the adjustment and change of production plans, the failure and abnormality of production equipment, the adjustment and change of production processes, and the configuration and coordination of production resources will have a great impact on the reliability of flexible manufacturing production systems. Therefore, only evaluating the reliability of flexible manufacturing production systems from a single dimension cannot fully, accurately, and comprehensively evaluate the comprehensive reliability of flexible manufacturing production systems. Summary of the invention

[0004] In view of this, the present invention provides a comprehensive reliability characterization method for a flexible manufacturing production system based on a composite model. By constructing a single reliability characterization model for each dimension and studying a multi-dimensional composite model integration fusion method on this basis, a comprehensive reliability analysis of the flexible manufacturing production system is performed from multiple dimensions, which is beneficial to improving the flexible manufacturing production efficiency.

[0005] To achieve the above objectives, the proposed solution is as follows: A comprehensive reliability characterization method for a flexible manufacturing production system based on a composite model includes: According to the production plan, a production reliability characterization model of the flexible manufacturing production system based on Monte Carlo is constructed, and the probability of meeting the delivery date is calculated according to the simulated expected delivery date to obtain the system production reliability; According to the process flow, the equipment reliability characterization model of each subsystem of the flexible manufacturing production system based on the extended random Petri net is constructed, and the equipment reliability of each subsystem is weighted according to the weight of each subsystem to obtain the system equipment reliability; According to the process flow, a multi-level process reliability characterization model based on extended random Petri net is constructed, and the process failure rate of the flexible manufacturing production system is calculated to obtain the system process reliability; Based on the Petri net model of each process, the reliability block diagram is used to build a resource reliability representation model for the flexible manufacturing production system, and the error-free rate of each process is calculated to obtain the system resource reliability. According to the fault type and the failure rate of the underlying event, a quality reliability characterization model of the flexible manufacturing production system based on the fault tree analysis method is constructed, and the probability of occurrence of each fault type is calculated to obtain the system quality reliability; According to the communication network, a network reliability characterization model of the flexible manufacturing production system based on the extended random Petri net is constructed, and the probability of normal operation of each network component is calculated to obtain the system network reliability; The analytic hierarchy process is used to determine the weight of each dimension, and the weighted sum of the single reliability characterization results of each dimension is performed to obtain the comprehensive reliability of the flexible manufacturing production system.

[0006] Preferably, the equipment reliability characterization model of each subsystem of the flexible manufacturing production system based on the extended random Petri net is constructed according to the process flow, and the equipment reliability of each subsystem is weighted according to the weight of each subsystem to obtain the system equipment reliability, which specifically includes: The probability of the equipment being in each state at the current moment is represented by the library, and the transition represents the duration of the current state. The equipment reliability characterization model of each subsystem of the flexible manufacturing production system based on the extended random Petri net is constructed. The subsystems of the flexible manufacturing production system include the manufacturing subsystem, the software control subsystem and the logistics subsystem. Determine the weight of each subsystem through the analytic hierarchy process; The equipment reliability obtained by the reliability characterization model of each subsystem equipment and the weight of each subsystem are weighted to obtain the system equipment reliability.

[0007] Preferably, the process of determining the weight of each subsystem by the hierarchical analysis method includes: The judgment matrix S is determined according to the importance relationship between each subsystem. The judgment matrix S is: ; in, Indicates Subsystem The importance of each subsystem, i =1,2,3, j =1,2,3; Calculate the maximum eigenvalue of the judgment matrix S and the corresponding eigenvector ; According to the formula Calculate the consistency index according to the formula Calculate the average random consistency index according to the formula Calculate the consistency ratio, where Indicates the number of subsystems; like , then the feature vector Normalized weight vector .

[0008] Preferably, the process of constructing a multi-level process reliability characterization model based on an extended random Petri net according to the process flow, and calculating the process failure rate of the flexible manufacturing production system to obtain the system process reliability includes: Each place represents the probability of each process being in different states at the current moment, each transition represents the duration of the current state, and each place token represents the number of existing stocks in each place. A multi-level process reliability characterization model based on extended random Petri net is constructed according to the process flow, and the process failure rate of the flexible manufacturing production system is output, where the process failure rate is used to represent the system process reliability of the flexible manufacturing production system.

[0009] Preferably, the process of constructing a resource reliability characterization model of a flexible manufacturing production system based on the Petri net model of each process and using a reliability block diagram, and calculating the error rate of each process respectively to obtain the system resource reliability includes: Taking the process as the unit, the place represents the probability of each resource being in different states at the current moment, the transition represents the duration of the current state, and the place token represents the current inventory of each resource in the place. The Petri net model of each process is constructed; Based on the Petri net model of each process, the Petri net model of the flexible manufacturing production system is constructed using the reliability block diagram to calculate the error rate of each process. The error rate of each process is used to represent the system resource reliability of the flexible manufacturing production system.

[0010] Preferably, a production reliability characterization model of a flexible manufacturing production system based on Monte Carlo is constructed according to the production plan, and the probability of meeting the delivery date is calculated according to the simulated expected delivery date to obtain the process of system production reliability, including: Develop a multi-batch production network diagram based on the production plan, where the production plan is determined based on order requirements and process flow; According to the multi-batch production network diagram, a production reliability characterization model of the flexible manufacturing production system based on Monte Carlo is constructed. According to the start and end time of each process, the expected delivery date is determined and statistically analyzed; The probability of meeting the delivery date is calculated based on the statistically estimated delivery date, where the probability of meeting the delivery date is used to represent the system production reliability.

[0011] Preferably, the processing time of each process is distributed as follows: Among them, f(x) represents the processing time of a certain process, and obeys distributed, represents the gamma function, , a is the most pessimistic time for a process, b is the most optimistic time for a process, is the expected value, , t is the most likely time for a process, is the standard deviation, , , Two parameters can be based on , Two formulas are solved.

[0012] Preferably, the process of constructing a quality reliability characterization model of a flexible manufacturing production system based on a fault tree analysis method according to the fault type and the failure rate of the underlying event, and calculating the probability of occurrence of each fault type to obtain the system quality reliability includes: Construct a fault tree based on product failure types and underlying events, and determine the failure rate of each underlying event; Determine all minimum cut sets according to the upward and downward methods; The probability of each fault type occurring is calculated using the following formula: ; in, represents the probability of a certain fault type occurring, Represents the minimum cut set Probability of occurrence, Represents the minimum cut set and The probability of simultaneous occurrence, Represents the minimum cut set , and The probability of simultaneous occurrence, represents the probability of the first k minimum cut sets occurring simultaneously; The reliability of each top-level event is calculated using the following formula: in, Indicates the reliability of a top-level event; The system quality reliability of the flexible manufacturing production system is calculated by the following formula: ; in, System quality reliability for flexible manufacturing production systems.

[0013] Preferably, the process of constructing a network reliability characterization model of a flexible manufacturing production system based on an extended random Petri net according to the communication network, and calculating the probability of normal operation of each network component to obtain the system network reliability includes: Analyze the communication network and obtain the communication network structure; Based on the communication network structure, the extended random Petri net method is used to construct a network reliability characterization model for flexible manufacturing production systems, in which each library represents the probability that each network component is in a different state at the current moment, and each transition represents the duration of the current state. The network reliability of the flexible manufacturing production system is calculated based on the network reliability characterization model.

[0014] According to the specific embodiments provided by the present invention, the present invention discloses the following technical effects: The comprehensive reliability characterization method of a flexible manufacturing production system based on a composite model provided by the present invention evaluates the reliability of a flexible manufacturing production system from multiple dimensions by constructing a single reliability characterization model of each dimension. After obtaining the reliability evaluation results of each single reliability characterization model, based on the weight of each dimension, the system production reliability, system equipment reliability, system process reliability, system resource reliability, system quality reliability and system network reliability are weighted and summed to obtain the comprehensive reliability analysis result of the flexible manufacturing production system. The present invention uses the single reliability characterization results of each dimension as input data for the system comprehensive reliability evaluation calculation, evaluates the reliability of the flexible manufacturing production system from multiple dimensions, and can comprehensively and accurately evaluate the reliability of the flexible manufacturing production system, thereby improving the flexible manufacturing production efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0016] Figure 1 A flow chart of a comprehensive reliability characterization method for a flexible manufacturing production system based on a composite model provided by an embodiment of the present invention; Figure 2 A multi-batch production network diagram provided for an embodiment of the present invention. DETAILED DESCRIPTION

[0017] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0018] In order to conduct reliability characterization and analysis of flexible manufacturing production systems more comprehensively and accurately, the present invention conducts research on the reliability characterization method of flexible manufacturing production systems in six dimensions, namely, production reliability caused by production plan adjustment and change; equipment reliability caused by equipment failure, abnormal alarm, etc.; process reliability caused by product production process adjustment and change; resource reliability caused by allocation and coordination of resources such as materials and personnel; quality reliability caused by the defective rate in the production process; and network communication reliability caused by equipment networking communication, single-machine equipment control anomalies, etc.

[0019] First, combine Figure 1 The comprehensive reliability characterization method of a flexible manufacturing production system based on a composite model provided in an embodiment of the present invention is introduced, and the method includes: Step S1, constructing a production reliability characterization model to obtain system production reliability.

[0020] Specifically, a production reliability characterization model of a flexible manufacturing production system based on Monte Carlo is constructed according to the production plan, and the probability of meeting the delivery date is calculated according to the simulated expected delivery date to obtain the system production reliability.

[0021] Step S2, constructing a device reliability characterization model to obtain system device reliability.

[0022] Specifically, an equipment reliability characterization model of each subsystem of a flexible manufacturing production system based on an extended random Petri net is constructed according to the process flow, and the equipment reliability of each subsystem is weighted according to the weight of each subsystem to obtain the system equipment reliability.

[0023] Step S3, constructing a process reliability characterization model to obtain system process reliability.

[0024] Specifically, a multi-level process reliability characterization model based on extended random Petri nets is constructed according to the process flow, and the process failure rate of the flexible manufacturing production system is calculated to obtain the system process reliability; Step S4: construct a resource reliability characterization model to obtain system resource reliability.

[0025] Specifically, based on the Petri net model of each process, the reliability block diagram is used to build a resource reliability representation model of the flexible manufacturing production system, and the error-free rate of each process is calculated to obtain the system resource reliability.

[0026] Step S5, constructing a quality reliability characterization model to obtain system quality reliability.

[0027] Specifically, a quality reliability characterization model of a flexible manufacturing production system based on the fault tree analysis method is constructed according to the fault type and the failure rate of the underlying event, and the probability of occurrence of each fault type is calculated to obtain the system quality reliability.

[0028] Step S6: construct a network reliability characterization model to obtain system network reliability.

[0029] Specifically, a network reliability characterization model of a flexible manufacturing production system based on an extended random Petri net is constructed according to the communication network, and the probability of normal operation of each network component is calculated to obtain the system network reliability.

[0030] Step S7, performing weighted summation on the reliabilities of different dimensions to obtain the system reliability of the flexible manufacturing production system.

[0031] Specifically, based on the weight of each dimension, the system production reliability, system equipment reliability, system process reliability, system resource reliability, system quality reliability and system network reliability are weighted and summed to obtain the system reliability of the flexible manufacturing production system.

[0032] Since the importance of reliability in different dimensions to the overall reliability of the flexible manufacturing system is different, the hierarchical analysis method is used to calculate the weights of the six dimensions according to the importance of production plan changes, equipment failures, process changes, resource coordination and allocation, quality qualification and network communication stability to the reliability of the flexible manufacturing system. Secondly, the comprehensive reliability of the flexible manufacturing system is calculated according to the weighted method.

[0033] The comprehensive reliability characterization method of a flexible manufacturing production system based on a composite model provided in an embodiment of the present invention evaluates the reliability of a flexible manufacturing production system from different dimensions by constructing a single reliability characterization model of each dimension. After obtaining the reliability evaluation results of each single reliability characterization model, based on the weight of each dimension, the system production reliability, system equipment reliability, system process reliability, system resource reliability, system quality reliability and system network reliability are weighted and summed to obtain the system reliability of the flexible manufacturing production system. The embodiment of the present invention uses the single reliability characterization results of each dimension as input data for the system comprehensive reliability evaluation calculation, evaluates the reliability of the flexible manufacturing production system from multiple dimensions, and can comprehensively and accurately evaluate the reliability of the flexible manufacturing production system, thereby improving the production efficiency of flexible manufacturing.

[0034] The order of constructing the reliability characterization model of the flexible manufacturing production system from different dimensions in the above steps S1 to S6 can be set according to needs, that is, there is no restriction on the construction order of the reliability characterization model of the flexible manufacturing production system from different dimensions.

[0035] Next, the embodiment of the present invention further introduces the process of step S1, constructing a production reliability characterization model, and obtaining system production reliability. The process is as follows: Step S11, formulate a multi-batch production network diagram according to the production plan.

[0036] Specifically, in order to deliver the ordered quantity of products required by customers within the specified delivery period, a production plan needs to be determined. The production plan includes production batches, processing time of each process, etc. The production plan is determined according to the order requirements and process flow. Figure 2 The multi-batch production network diagram shown in Figure 1 shows the multi-batch production network diagram, where Indicates Batch process For a multi-batch production process, a certain process of the current batch of products needs to be carried out after the processing of the previous batch of products is completed. For example, it means that process 2 of the second batch needs to be carried out after process 1 of the second batch and process 2 of the first batch are completed.

[0037] Step S12, constructing a production reliability characterization model of a flexible manufacturing production system based on Monte Carlo according to the multi-batch production network diagram, and determining and statistically analyzing the expected delivery date according to the start time and end time of each process.

[0038] Specifically, first, three processing times of each process are determined, namely the most pessimistic time, the most optimistic time and the most likely time. Then, it is assumed that the processing time of each process obeys Distribution, the processing time distribution of each process is: Among them, f(x) represents the processing time of a certain process, and obeys distributed, represents the gamma function, , a is the most pessimistic time for a process, b is the most optimistic time for a process, is the expected value, , t is the most likely time for a process, is the standard deviation, , since a, b, t are all known, , Two parameters can be based on , Two formulas are solved.

[0039] According to the multi-batch production network diagram, the start and end time of each process are determined, and the estimated delivery date is finally determined. The above steps complete one simulation, and the above steps are repeated until the number of simulations is reached, and the estimated delivery date results of each simulation are counted.

[0040] Step S13, calculating the probability of meeting the delivery date based on the statistically estimated delivery date.

[0041] Specifically, the probability of meeting the delivery date is calculated based on the statistical expected delivery date, and the probability of meeting the delivery date is used to represent the system production reliability.

[0042] Furthermore, this embodiment introduces the process of constructing a reliability characterization model of a flexible manufacturing production system based on a random extended petri net from different dimensions in steps S2-S4, and the process is as follows: The extended random Petri net model is expressed as: in, , Represents a finite set of places; , represents a finite set of transitions that satisfies and ; , represents the input function; represents the output function; Indicates the logo, the first Component indicates The number of tokens in a place is initially marked as ; represents an arbitrarily distributed ignition delay; Indicates the time when the model starts running from the initial state. The ignition rules are as follows: 1) In the logo In, changes is enabled if and only if , , , and if , ; 2) If In the logo The following activation rules are used to generate new identifiers: : , .

[0043] Production equipment plays a key role in the manufacturing system and is a key factor in ensuring product quality. If the equipment fails, the reliability of the entire manufacturing system will be reduced, and there is a risk of increased defective products. In order to ensure the normal operation of the flexible manufacturing production system, it is necessary to understand the operating status of the production equipment. Therefore, a flexible manufacturing system equipment reliability characterization model is constructed, and the reliability of the flexible manufacturing system is calculated by the equipment failure rate. Based on this, the above step S2, constructing the equipment reliability characterization model, and obtaining the process of system equipment reliability can include: Step S21, using the library to represent the probability of the equipment being in each state at the current moment, and the transition to represent the duration of the current state, to construct an equipment reliability characterization model for each subsystem of the flexible manufacturing production system based on the extended random Petri net.

[0044] Specifically, based on the type of equipment, the flexible manufacturing production system is divided into manufacturing subsystem, software control subsystem and logistics subsystem.

[0045] First, the meaning and quantity of the library and the meaning and quantity of the transition in the extended random Petri net are determined. The library represents the probability that the equipment is in a normal or faulty state at the current moment, and the transition represents the duration of the current state of the equipment. Then, the equipment reliability characterization model of each subsystem of the flexible manufacturing production system based on the extended random Petri net is constructed in the order of the process flow.

[0046] Step S22, determining the weight of each subsystem through the hierarchical analysis method.

[0047] Specifically, the weight of the subsystem is determined by the analytic hierarchy process. According to the importance relationship between the manufacturing subsystem, software control subsystem and logistics subsystem, the weight of the three subsystems is calculated by the analytic hierarchy process. The calculation process is as follows: 1) Determine the judgment matrix S according to the importance relationship between each subsystem. The judgment matrix S is: ; in, Indicates Subsystem The importance of each subsystem, i =1,2,3, j =1,2,3, among which 1-9 and its reciprocal can be used to indicate the importance level between two subsystems.

[0048] 2) Calculate the maximum eigenvalue of the judgment matrix S and the corresponding eigenvector .

[0049] 3) According to the formula Calculate the consistency index according to the formula Calculate the average random consistency index according to the formula Calculate the consistency ratio, where Represents the number of subsystems, which is equal to 3 here.

[0050] 4) If , then the feature vector Normalized weight vector .

[0051] Step S23 , weighting the equipment reliability obtained according to the equipment reliability characterization model of each subsystem and the weight of each subsystem to obtain the system equipment reliability of the system.

[0052] Specifically, the calculation is performed based on the constructed equipment reliability characterization model of each subsystem and the calculated weight of each subsystem. Perform weighted summation to obtain the equipment reliability of the flexible manufacturing production system, where: Indicates Subsystem weights, Indicates Reliability of each subsystem.

[0053] Flexible manufacturing systems have the characteristics of being reconfigurable. When the process flow is adjusted, it will affect the reliability of the manufacturing system. Therefore, it is considered to analyze from the process perspective, build a process reliability characterization model for the flexible manufacturing system, and finally express the reliability of the flexible manufacturing system through the process failure rate. Based on this, the above step S3, building a process reliability characterization model and obtaining the process reliability of the system, can include: Step S31, use each library to represent the probability that each process is in different states at the current moment, each transition represents the duration of the current state, and each library token represents the number of existing stocks in each library. According to the process flow, a multi-level process reliability characterization model based on the extended random Petri net is constructed to output the process failure rate of the flexible manufacturing production system.

[0054] Specifically, first, the process flow of the flexible manufacturing system and the workshop where each process is located are clarified. Then, the number of places and their meanings, the number of transitions and their meanings, the place tokens, and the cache capacity of each cache area in the process reliability characterization model are determined. Among them, each place represents the probability of the state of each process, each transition represents the duration of the state, and each place token represents the number of existing stocks in each place. Secondly, a multi-level process reliability characterization model based on extended stochastic Petri nets is constructed according to the process flow, and finally the process failure rate of the flexible manufacturing system, that is, the process reliability, is output.

[0055] In the process of resource allocation, if the allocation time is too long or the allocation is unreasonable, it will not only prolong the production time, but also increase the defective rate and increase the production cost. Therefore, consider analyzing the allocation of resources such as people and materials, and calculate the probability that personnel and materials will not make mistakes in a certain process, and calculate the resource reliability of the entire flexible manufacturing production system according to the process flow. Based on this, the above step S4, constructing a resource reliability characterization model, and obtaining the process of system resource reliability can include: Step S41, taking the process as the unit, the place represents the probability of each resource being in different states at the current moment, the transition represents the duration of the current state, the place token represents the current inventory of each resource in the place, and the Petri net model of each process is constructed.

[0056] Specifically, taking the process as a unit, each process is composed of resources such as people and materials, and different resources are in series. First, determine the number of places and the meaning of each place, the number of transitions and the meaning of each transition, and the meaning of place tokens, so as to build a Petri net model. Among them, the place represents the probability that people, materials and other resources are in a normal or abnormal state at the current time, the transition represents the duration of the action, and the place token represents the current inventory of the place, for example: the material inventory at the current time, etc.

[0057] Step S42, based on the Petri net model of each process, a Petri net model of the flexible manufacturing production system is constructed using a reliability block diagram to calculate the error rate of each process.

[0058] Specifically, based on the Petri net model constructed in process units in step S42, a Petri net model of the flexible manufacturing production system is constructed by using a reliability block diagram to calculate the error rate of each process, that is, to calculate the probability that personnel and materials will not make mistakes in a certain process. The error rate of each process is used to represent the resource reliability of the flexible manufacturing production system.

[0059] Furthermore, a high product failure rate is a direct manifestation of low reliability of the manufacturing production system, so the reliability of the flexible manufacturing production system is analyzed from the perspective of quality reliability. The embodiment of the present invention uses a fault tree analysis method to calculate the probability of top-level events, i.e., product failure, from both qualitative and quantitative perspectives through bottom-level events and their failure rates, to achieve a characterization analysis of the quality reliability of the flexible manufacturing system. Therefore, step S5, constructing a quality reliability characterization model to obtain the process of system quality reliability, may include: Step S51, constructing a fault tree based on product failure types and underlying events, and determining the failure rate of each underlying event.

[0060] Step S52: determine all minimum cut sets according to the upward method and the downward method.

[0061] Step S53, the probability of each fault type occurring is calculated by the following formula: ; in, represents the probability of a certain fault type occurring, Represents the minimum cut set Probability of occurrence, Represents the minimum cut set and The probability of simultaneous occurrence, Represents the minimum cut set , and The probability of simultaneous occurrence, represents the probability of the first k minimum cut sets occurring simultaneously.

[0062] Step S54, the reliability of each top-level event is calculated by the following formula: in, Indicates the reliability of a top-level event.

[0063] Step S55, calculating the system quality reliability of the flexible manufacturing production system.

[0064] Specifically, since the top-level events are in a series relationship, the system quality reliability of the flexible manufacturing production system is calculated according to the following formula based on the reliability of each top-level event. The formula is as follows: ; in, Quality reliability of flexible manufacturing production systems.

[0065] Since the stability, efficiency and security of the network will affect the reliability of the flexible manufacturing production system, based on this, step S6 of the embodiment of the present invention is to construct a network reliability characterization model to introduce the process of obtaining the system network reliability, and the process is as follows: Step S61, analyzing the communication network to obtain the communication network structure.

[0066] Specifically, the communication network is analyzed to obtain the communication network structure. The communication network consists of nodes and transmission links. The nodes are divided into terminal nodes and switching nodes. Various nodes have both master nodes and backup nodes. The transmission links have both main links and backup links. When the master node or the main link fails, the backup node or the backup link will play a role.

[0067] Step S62, constructing a network reliability characterization model of the flexible manufacturing production system based on the communication network structure by using an extended random Petri net method.

[0068] Specifically, first, the obtained network structure is analyzed to determine the number of places and their meanings, the number of transitions and their meanings. Then, the network reliability representation model of the flexible manufacturing production system is constructed using the Petri net method based on the network structure. Among them, each place represents the probability that the current network component is in the OK or Failure state, and each transition represents the corresponding time of the action.

[0069] Step S63, calculating the network reliability of the flexible manufacturing production system according to the network reliability characterization model.

[0070] The embodiment of the present invention proposes a comprehensive reliability characterization method for a flexible manufacturing production system based on a composite model, which conducts a comprehensive reliability analysis of the flexible manufacturing production system in six dimensions: production reliability caused by production plan adjustment and change; equipment reliability caused by equipment failure, abnormal alarm, etc.; process reliability caused by product production process adjustment and change; resource reliability caused by resource allocation and coordination of materials, personnel, etc.; quality reliability caused by defective rate in the production process; network communication reliability caused by equipment networking communication, single-machine equipment control abnormality, etc. By constructing a single reliability characterization model for each dimension, and on this basis studying the integrated fusion method of multi-dimensional composite models, a comprehensive reliability analysis is conducted on the flexible manufacturing production system to improve the production efficiency of flexible manufacturing.

[0071] Finally, it should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the presence of other identical elements in the process, method, article or device including the elements.

[0072] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

[0073] The above description of the disclosed embodiments enables one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A comprehensive reliability characterization method for a flexible manufacturing production system based on a composite model, characterized in that: include: According to the production plan, a production reliability characterization model of the flexible manufacturing production system based on Monte Carlo is constructed, and the probability of meeting the delivery date is calculated according to the simulated expected delivery date to obtain the system production reliability; According to the process flow, the equipment reliability characterization model of each subsystem of the flexible manufacturing production system based on the extended random Petri net is constructed, and the equipment reliability of each subsystem is weighted according to the weight of each subsystem to obtain the system equipment reliability; According to the process flow, a multi-level process reliability characterization model based on extended random Petri net is constructed, and the process failure rate of the flexible manufacturing production system is calculated to obtain the system process reliability; Based on the Petri net model of each process, the reliability block diagram is used to build a resource reliability representation model for the flexible manufacturing production system, and the error-free rate of each process is calculated to obtain the system resource reliability. According to the fault type and the failure rate of the underlying event, a quality reliability characterization model of the flexible manufacturing production system based on the fault tree analysis method is constructed, and the probability of occurrence of each fault type is calculated to obtain the system quality reliability; According to the communication network, a network reliability characterization model of the flexible manufacturing production system based on the extended random Petri net is constructed, and the probability of normal operation of each network component is calculated to obtain the system network reliability; The analytic hierarchy process is used to determine the weight of each dimension, and the weighted sum of the single reliability characterization results of each dimension is performed to obtain the comprehensive reliability of the flexible manufacturing production system.

2. The comprehensive reliability characterization method for flexible manufacturing production system based on composite model according to claim 1 is characterized in that: The equipment reliability characterization model of each subsystem of the flexible manufacturing production system based on the extended random Petri net is constructed according to the process flow, and the equipment reliability of each subsystem is weighted according to the weight of each subsystem to obtain the system equipment reliability, which specifically includes: The probability of the equipment being in each state at the current moment is represented by the library, and the transition represents the duration of the current state. The equipment reliability characterization model of each subsystem of the flexible manufacturing production system based on the extended random Petri net is constructed. The subsystems of the flexible manufacturing production system include the manufacturing subsystem, the software control subsystem and the logistics subsystem. Determine the weight of each subsystem through the analytic hierarchy process; The equipment reliability obtained by the reliability characterization model of each subsystem equipment and the weight of each subsystem are weighted to obtain the system equipment reliability.

3. The comprehensive reliability characterization method for flexible manufacturing production system based on composite model according to claim 2 is characterized in that: The process of determining the weight of each subsystem through the hierarchical analysis method includes: The judgment matrix S is determined according to the importance relationship between each subsystem. The judgment matrix S is: ； in, Indicates Subsystem The importance of each subsystem, i =1,2,3, j =1,2,3; Calculate the maximum eigenvalue of the judgment matrix S and the corresponding eigenvector ; According to the formula Calculate the consistency index according to the formula Calculate the average random consistency index according to the formula Calculate the consistency ratio, where Indicates the number of subsystems; like , then the feature vector Normalized weight vector .

4. The comprehensive reliability characterization method for flexible manufacturing production system based on composite model according to claim 1 is characterized in that: The process of constructing a multi-level process reliability characterization model based on an extended random Petri net according to the process flow, calculating the process failure rate of the flexible manufacturing production system, and obtaining the system process reliability includes: Each place represents the probability of each process being in different states at the current moment, each transition represents the duration of the current state, and each place token represents the number of existing stocks in each place. A multi-level process reliability characterization model based on extended random Petri net is constructed according to the process flow, and the process failure rate of the flexible manufacturing production system is output, where the process failure rate is used to represent the system process reliability of the flexible manufacturing production system.

5. The comprehensive reliability characterization method for flexible manufacturing production system based on composite model according to claim 1 is characterized in that: The process of building a resource reliability characterization model of a flexible manufacturing production system based on the Petri net model of each process and using a reliability block diagram, and calculating the error-free rate of each process to obtain the system resource reliability, includes: Taking the process as the unit, the place represents the probability of each resource being in different states at the current moment, the transition represents the duration of the current state, and the place token represents the current inventory of each resource in the place. The Petri net model of each process is constructed; Based on the Petri net model of each process, the Petri net model of the flexible manufacturing production system is constructed using the reliability block diagram to calculate the error rate of each process. The error rate of each process is used to represent the system resource reliability of the flexible manufacturing production system.

6. The method for characterizing the comprehensive reliability of a flexible manufacturing production system according to claim 1, characterized in that: The production reliability characterization model of the flexible manufacturing production system based on Monte Carlo is constructed according to the production plan, and the probability of meeting the delivery date is calculated according to the simulated expected delivery date to obtain the process of system production reliability, including: Develop a multi-batch production network diagram based on the production plan, where the production plan is determined based on order requirements and process flow; According to the multi-batch production network diagram, a production reliability characterization model of the flexible manufacturing production system based on Monte Carlo is constructed. According to the start and end time of each process, the expected delivery date is determined and statistically analyzed; The probability of meeting the delivery date is calculated based on the statistically estimated delivery date, where the probability of meeting the delivery date is used to represent the system production reliability.

7. The method for characterizing the comprehensive reliability of a flexible manufacturing production system according to claim 6 is characterized in that: The processing time distribution of each process is: Among them, f(x) represents the processing time of a certain process, and obeys distributed, represents the gamma function, , a is the most pessimistic time for a process, b is the most optimistic time for a process, is the expected value, , t is the most likely time for a process, is the standard deviation, , , Two parameters can be based on , Two formulas are solved.

8. The method for characterizing the comprehensive reliability of a flexible manufacturing production system according to claim 1 is characterized in that: The process of constructing a quality reliability characterization model of a flexible manufacturing production system based on a fault tree analysis method according to the fault type and the failure rate of the underlying event, and calculating the probability of occurrence of each fault type to obtain the system quality reliability includes: Construct a fault tree based on product failure types and underlying events, and determine the failure rate of each underlying event; Determine all minimum cut sets according to the upward and downward methods; The probability of each fault type occurring is calculated using the following formula: ； in, represents the probability of a certain fault type occurring, Represents the minimum cut set Probability of occurrence, Represents the minimum cut set and The probability of simultaneous occurrence, Represents the minimum cut set , and The probability of simultaneous occurrence, represents the probability of the first k minimum cut sets occurring simultaneously; The reliability of each top-level event is calculated using the following formula: in, Indicates the reliability of a top-level event; The system quality reliability of the flexible manufacturing production system is calculated by the following formula: ； in, System quality reliability for flexible manufacturing production systems.

9. The comprehensive reliability characterization method for flexible manufacturing production system based on composite model according to claim 1 is characterized in that: The process of constructing a network reliability characterization model of a flexible manufacturing production system based on an extended random Petri net according to a communication network, and calculating the probability of normal operation of each network component to obtain the system network reliability includes: Analyze the communication network and obtain the communication network structure; Based on the communication network structure, the extended random Petri net method is used to construct a network reliability characterization model for flexible manufacturing production systems, in which each library represents the probability that each network component is in a different state at the current moment, and each transition represents the duration of the current state. The network reliability of the flexible manufacturing production system is calculated based on the network reliability characterization model.

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