Manufacturing system selective maintenance decision optimization method considering operation robustness

Abstract

The invention provides a manufacturing system selective maintenance decision optimization method considering operation robustness, and the method comprises the steps: 1, collecting and analyzing the data of a manufacturing system after a task is finished after a task period of the manufacturing system is finished; 2, determining the equipment performance state of the manufacturing system, the deviation of the product quality characteristics and the product quality; 3, determining the current robustness level of the manufacturing system; 4, collecting data about machine maintenance, and a corresponding relationship among the maintenance cost, the maintenance time and the maintenance quality of each machine; 5, establishing a selective maintenance decision model by taking robustness maximization of the next task stage as a target and taking maintenance time and cost as constraint conditions; and 6, establishing an ASA-PSO algorithm model to obtain an optimal maintenance strategy. According to the method, the relationship between the system robustness and the machine performance reduction, task delay and product quality non-compliance risk is considered, the function risk and mechanism of the manufacturing system are analyzed, the use of maintenance resources is optimized, and a better maintenance plan is realized.

Description

Technical Field

[0001] The invention provides a selective maintenance decision optimization method for a manufacturing system considering operation robustness, and belongs to the field of system state evaluation and maintenance decision. Background Art

[0002] In the rapidly developing manufacturing industry, ensuring the reliability of multi-stage manufacturing systems is essential to produce high-quality products that always meet the requirements. These complex systems usually consist of multiple stations configured in series and parallel. In such systems, the quality of work-in-process is affected by various factors and is directly related to the performance status of the processing equipment. Optimization method for selective maintenance decisions of manufacturing systems considering operational robustness The direct interaction between processing equipment and work-in-process means that any degradation in equipment performance will directly affect the quality of the workpiece. The quality of the workpiece during the production process will also lead to the degradation of the performance of downstream equipment components. In these stages, the generation, accumulation and transmission of variations produce a coupling effect that may undermine the stability of product quality. Therefore, studying the robustness of multi-stage manufacturing systems is crucial to achieving consistency in product quality, and studying the optimization of maintenance decisions for equipment in multi-stage manufacturing systems is crucial to ensuring the robustness of manufacturing system operations.

[0003] Previous studies on the functional risk of multi-stage manufacturing systems have mainly focused on product quality and the reliability of processing equipment, and have taken them as key indicators of functional risk. However, such studies have ignored the importance of robustness as a key indicator of multi-stage manufacturing systems. The operational robustness of a manufacturing system usually further reflects the stability of the product quality of the manufacturing system and the performance status of the processing equipment. When modeling the functional risk of a multi-stage manufacturing system, the relationship between robustness and the risk of machine performance degradation, product quality, and task delay must be considered, and then the operational status of the manufacturing system must be comprehensively evaluated. At the same time, a multi-stage manufacturing system consists of many different types of processing machines, and the degradation laws and states of each machine are different. Therefore, corresponding maintenance activities should be formulated for different types of processing machines according to their degradation laws. Selective maintenance can reasonably allocate limited maintenance costs and maintenance time to each processing machine when maintenance resources are limited, so as to ensure the operational robustness and product quality of the next stage of the manufacturing system. However, most of the existing maintenance methods ignore the operating mechanism of multi-stage manufacturing systems and the relationship between task requirements and outputs. The robustness index happens to be an important indicator to measure the production capacity of multi-stage manufacturing systems. It has a significant impact on production scheduling, product qualification rate and production cost. It directly reflects the production status of the manufacturing system and helps to guide the selective maintenance of the system.

[0004] In response to this problem, the present invention develops a selective maintenance decision optimization method for manufacturing systems that considers operational robustness. It aims to explain the functional risk connotation of the manufacturing system from the perspective of operational robustness, and establish a functional risk model that considers operational robustness. The model considers the impact of process parameter deviations, machine performance degradation, work-in-progress and final product quality, and task delay risks on system robustness. In addition, the selective maintenance method for manufacturing systems that considers operational robustness analyzes the impact of parameter changes caused by the risk of machine performance degradation on the robustness of the manufacturing system, and further studies the impact on the quality of work-in-progress. The present invention is applicable to maintenance decisions of manufacturing systems. Through quantitative evaluation of robustness and the constraint of limited resources, a selective maintenance optimization method is proposed, which has guiding significance for the operation and maintenance decisions of manufacturing systems. Summary of the invention

[0005] The purpose of the present invention is to provide a selective maintenance decision optimization method for a manufacturing system considering operational robustness; the robustness of a multi-stage manufacturing system is the basis for ensuring its mission reliability and stably outputting qualified final products. Product quality is a direct reflection of mission reliability and functional risk. In addition, the operational robustness of a manufacturing system reflects the performance of processing machines and the work-in-process (WIP) condition. However, current maintenance strategies often ignore the functional risk and operational robustness of the operating manufacturing system. To this end, a selective maintenance decision optimization method for a manufacturing system considering operational robustness is proposed. Firstly, based on the definition of operational robustness of a multi-stage manufacturing system, the functional risk formation mechanism is defined and the selective maintenance principle is proposed; secondly, the synergistic effect between functional risk and operational robustness is explained, and a comprehensive evaluation model of functional risk considering operational robustness is established. Thirdly, a selective maintenance method for a multi-stage manufacturing system considering robustness parameters is proposed, and the adaptive simulated annealing particle swarm optimization (ASA-PSO) algorithm is used to obtain the optimal maintenance decision to maximize the operational robustness of the multi-stage manufacturing system. Finally, the ferrite phase shift unit manufacturing system is taken as an example to verify the effectiveness of the method.

[0006] (2) Technical solution:

[0007] The present invention proposes a selective maintenance decision optimization method for a manufacturing system considering operational robustness, based on the following assumptions:

[0008] Assumptions 1. The degradation of all processing machines in the manufacturing system follows a semi-Markov process;

[0009] Assumption 2: The performance status of each processing machine refers to the amount of work that the processing machine can process under normal circumstances;

[0010] Assumption 3: The corresponding key quality characteristics processed by each machine are considered independent of each other;

[0011] Assumption 4: There is an absolutely reliable checkpoint after each processing to verify the quality status of the output product;

[0012] Assumption 5: The processing capacity of the machine has a finite number of discrete states, and the corresponding probabilities are proportional;

[0013] Hypothesis 6. The robustness of a manufacturing system affects the probability of a processing machine being in each performance state.

[0014] Assumption 7. The robustness of the manufacturing system is not affected by human and environmental factors.

[0015] Assumption 8. The cost and time of manufacturing system maintenance are related to the status before and after maintenance, and have a one-to-one correspondence.

[0016] Based on the above assumptions, the present invention proposes a selective maintenance decision optimization method for a manufacturing system considering operational robustness, and the steps are as follows:

[0017] Step 1: After a task cycle of the manufacturing system is completed, collect and analyze the data of the manufacturing system after the task is completed;

[0018] Step 2, determining the performance status of the manufacturing system equipment, the deviation of the product quality characteristics and the product quality according to step 1;

[0019] Step 3: Determine the current robustness level of the manufacturing system according to step 2;

[0020] Step 4: Collect data on machine maintenance, including the corresponding relationship between maintenance cost, maintenance time and maintenance quality of each machine;

[0021] Step 5: Based on Step 2 and Step 4, a selective maintenance decision model is established with the goal of maximizing the robustness of the next task phase and the maintenance time and cost as constraints;

[0022] Step 6: Establish the ASA-PSO algorithm model to obtain the best maintenance strategy;

[0023] Among them, the "collect and analyze the data of the manufacturing system after a task cycle of the manufacturing system is completed" stated in step 1 means that after a task cycle, the equipment in the manufacturing system may degrade to varying degrees, and it is necessary to collect machine performance status data, work-in-process quality deviation and other data to facilitate the subsequent evaluation of the robustness of the manufacturing system.

[0024] Among them, in step 2, "determine the performance status of the manufacturing system equipment, the deviation of the product quality characteristics and the product quality according to step 1", in step 2, determine the deviation of the quality characteristics of the work-in-process and the product quality when the task of the previous stage is running, and count the performance status of each processing equipment at the beginning and end of the previous task. The present invention assumes that the normal working time of the processing equipment follows the Weibull distribution, Among them, β is the shape parameter, η is the scale parameter, and β ≥ 0; in addition, the failure probability function of the Weibull distribution is The cumulative failure probability of the Weibull distribution is In general, the larger the task load, the smaller the scale parameter. The more robust the process, the larger the scale parameter and the closer it is to 1. Since the stay of the processing machine in any state follows the Weibull distribution, the probability of the processing machine being in each state at any time t is expressed as:

[0025] p(t)={p1(t),p2(t),p3(t),…,p w (t)} and

[0026] The performance of the machining machine tool continues to maintain s within the Δt time x The probability of the state is:

[0027]

[0028] Here we assume that the processing machine has w performance states, namely {s1, s2, …, s w}, at any time t, the performance state of the processing machine must be in one of these w performance states, x∈{1,2,…,w}, so at any time t, assuming that the performance state of the processing machine is s x . F x (t) represents the processing machine at time t at s x The probability of the state, F x (t+Δt) means that the processing machine is in s at time t+Δt x In addition, the present invention believes that the deviation of the key quality characteristics of the workpiece in process can be expressed as where ε i,a is a constant, and are the influence vectors of machine degradation factors and noise factors on the key quality characteristics of the workpiece in process; i (t) and ψ i (t) respectively represent the vector set of controllable factors and noise factors; Θ i,a is the interaction effect between the above two effects; assuming that there are l a A key quality characteristic, when χ iWhen (t) = 0, the machine performance is in the best state, then the deviation of the key quality characteristics under the ideal state is: The deviation of the critical quality characteristic is then:

[0029]

[0030] In the operation process of the manufacturing system, due to the existence of uncertainty, i (t) obeys the Weibull distribution, that is, χ i (t)~f(t;β,η), therefore,

[0031]

[0032] in

[0033]

[0034] Λ=Α i,a T cov(χ i (t)) i,a ; The expression of the output qualified rate of workpieces in process at station i is:

[0035]

[0036] Among them, USL a and LSL a are the upper and lower limits of the key quality characteristics design of the workpiece in process, and ζ (0<ζ≤1) is the adjustment parameter based on the robustness of the manufacturing system to ensure that the qualified rate meets the requirements.

[0037] The “determining the current robustness level of the manufacturing system according to step 2” in step 3 refers to evaluating the robustness of the current manufacturing system. Therefore, the robustness of the entire manufacturing system can be obtained. Here, the random variable J represents the length of time from the start of operation to the failure of the processing machine. Robustness can be defined as the machine operation time t is less than the random variable. Therefore, the probability that the failure time length is greater than time t is defined as the robustness of the manufacturing system at that moment, that is, P(J>t).

[0038] Among them, the "collection of data on machine maintenance, the corresponding relationship between the maintenance cost, maintenance time and maintenance quality of each machine" mentioned in step 4 collects the maintenance data of each processing equipment in the manufacturing system, and the corresponding relationship between the maintenance cost, maintenance time and machine performance status before and after maintenance of each machine. By performing maintenance activities within each task interval, the faulty or aging machines in the manufacturing system are restored to ensure the robustness of the manufacturing system in the next task. Each device has M c+1 optional maintenance activity. During the mth maintenance period, the maintenance activities available to machine i are g. i,m And g i,m ∈{0,1,…,M c}, g i,m The larger the g, the better the maintenance effect. i,m = 0, the machine is not repaired. i,m =M c When , the machine is perfectly repaired, so the maintenance cost of performing maintenance activities on machine i is: in, is the fixed maintenance cost of machine i, c i (g i,m ,s i,m ) is the variable maintenance cost of the maintenance activity, assuming that when processing machine i is in state s at the end of the mth task i,m , which depends on the maintenance level and machine status. Therefore, the total maintenance cost of the manufacturing system in the mth task cycle is The maintenance time for machine i to perform maintenance activities in the mth task interval is in, is the fixed maintenance time of machine i, t i (g i,m ,s i,m ) is the variable maintenance time of the maintenance activity of machine i, and its value depends on the maintenance level and the machine status. Therefore, assuming that the maintenance of each device of the manufacturing system is parallel in any task interval, the total maintenance time of the manufacturing system is defined as Tm = f(T1,m,T2,m,…,Tp,m), where Tp,m represents the time required to maintain machine p in the mth maintenance period. If the maintenance activity of each machine is performed once in the task interval, the maintenance time of the manufacturing system is the sum of the maintenance times. If the maintenance activity of each machine is independent and parallel, then the maintenance time of the manufacturing system is equal to the longest maintenance time of all machines.

[0039] Among them, the "according to step 2 and step 4, the selective maintenance decision model is established with the goal of maximizing the robustness of the next mission stage and the maintenance time and cost as constraints" in step 5 means that according to step 2 and step 4, the selective maintenance decision model is established with the goal of maximizing the robustness of the next mission stage and the maintenance time and cost as constraints. The KijimaⅡ model is used to characterize the maintenance effectiveness U of the maintenance behavior through service life regression. l,m+1 =b l,m ·V l,m , where U l,m+1 is the effective service life of machine l after maintenance operation in the mth task interval, V l,mis the duration of the mth task, b i,m ≥1 is the service life regression factor, which is affected by the maintenance resource investment, b i,m The smaller it is, the better the maintenance effect is. Therefore, the conditional survival probability of machine l after maintenance is:

[0040]

[0041] Among them, T m represents the duration of task m, η lx is the scale parameter of the Weibull distribution of machine l in state x, β lx is the shape parameter of the Weibull distribution of machine l in state x. The robustness of the machine in the mth task depends on the conditional survival probability r(t) and the state s of the machine at the end of the mth task break,m The robustness expression of machine l is R l (m) = r l (m)s break,m ,in, Therefore, the robustness of the entire manufacturing system is Assume that the pre-allocated maintenance resources are: maintenance cost C0, maintenance time T0. Therefore, the maintenance decision model is:

[0042] Objective function: Constraints:

[0043]

[0044]

[0045] Equations (2) and (3) indicate that the overall maintenance cost and maintenance time of the manufacturing system cannot exceed the resource constraints. Equations (4) and (5) indicate the variable constraints in the selective maintenance decision problem.

[0046] Among them, the "establishing an ASA-PSO algorithm model to obtain the best maintenance strategy" in step 6 refers to the following table which is the pseudo code of selective maintenance decision based on ASA-PSO:

[0047]

[0048]

[0049] Through the above steps, a selective maintenance decision optimization method for manufacturing systems considering operational robustness is proposed. The present invention solves the limitations of traditional manufacturing system health status prediction and maximizes the robustness of the manufacturing system by analyzing the relationship between machine performance degradation and robustness, thereby guiding selective maintenance decisions. The relationship between product qualification rate and machine performance status is examined, the functional risks and mechanisms of the manufacturing system are analyzed, and the use of maintenance resources is optimized to achieve better maintenance plans. The relationship between system robustness and the risk of machine performance degradation, task delays, and product quality failure is considered. The method can be applied to various complex systems, and the ASA-PSO optimization algorithm effectively solves multi-constraint problems.

[0050] (3) Advantages and effects:

[0051] The present invention is a selective maintenance decision optimization method for a manufacturing system considering operational robustness, and has the following advantages:

[0052] i. The present invention implements the system engineering concept, defines the connotation of functional risk, and explains the factors that affect the operational risk of the manufacturing system from the perspective of operational robustness.

[0053] ii. The present invention proposes a functional risk model based on processing machine status data, work-in-process quality data, task execution status data and operation robustness data.

[0054] iii. The present invention considers the impact of operational robustness on the product quality, machine performance status and task execution status of the manufacturing system, and proposes a selective maintenance decision model for the manufacturing system considering robustness.

[0055] iv. The modeling method is scientific and reasonable, has good processability, and can be widely promoted in the industry. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 It is a flow chart of the manufacturing system selective maintenance decision optimization method considering operation robustness described in the present invention.

[0057] Figure 2 It is a schematic diagram of the selective maintenance considering robustness proposed by the present invention.

[0058] Figure 3 It is the selective maintenance decision framework diagram proposed by the present invention.

[0059] Figure 4 It is a selective maintenance decision flow chart of the present invention using the ASA-PSO algorithm. DETAILED DESCRIPTION

[0060] The present invention will be further described in detail below with reference to the accompanying drawings and examples.

[0061] The present invention is a selective maintenance decision optimization method for a manufacturing system considering operational robustness. Figure 1 As shown, the steps are as follows:

[0062] Step 1: After a task cycle of the manufacturing system is completed, collect and analyze the data of the manufacturing system after the task is completed. Figure 2 As shown, selective maintenance decisions are made based on the collected data. Table 1 shows the key processes and key workstations for producing products in this manufacturing system, and Table 2 shows the relevant parameters of the machine.

[0063] Table 1. Key quality characteristic parameters and related information

[0064]

[0065] Table 2. Relevant data of processing machines

[0066]

[0067] Based on the collected key quality characteristic data, the production qualification rates of the four processing machine tools were obtained. In addition, the processing machine was divided into several discrete states from the best state to complete failure. The performance state set and the production qualification probability are shown in Table 3.

[0068] Step 2: Determine the deviation of the quality characteristics of the work-in-process and the product quality during the operation of the previous stage task, and count the performance status of each processing equipment at the beginning and end of the previous task. Substitute the data in Table 2 into

[0069]

[0070] The quantitative indicators of the quality characteristics of the key quality characteristics of the workpiece in the previous task are:

[0071] ν1(t)=7.6571×10 -8 t 2 +1.504×10 -5 t

[0072] ν2(t)=9.433×10 -8 t 2 +1.939×10 -5 t

[0073] ν3(t)=4.761×10 -8 t 2 +1.202×10 -5 t

[0074] ν4(t)=5.647×10 -8 t 2 +1.092×10 -5 t

[0075] Based on the collected critical quality characteristics data and formulas:

[0076]

[0077] The production qualification rate of the processing machine tool is obtained. In addition, from the best state to complete failure, the processing machine is divided into several discrete states. The performance state set and the production qualification probability are shown in Table 3.

[0078] Table 3. Machine performance status data

[0079]

[0080] According to the above data, at the end of the previous task execution phase, the probabilities corresponding to the various states of the processing machine M1 are shown in Table 4.

[0081] Table 4. State distribution and probability of processing machines M1, M2, M3, and M4.

[0082]

[0083] After one production cycle, the production qualification rates of each processing machine are q1 = 0.979, q2 = 0.975, q3 = 0.972, q4 = 0.957, and the minimum input flow rate of each processing machine in one cycle is f1. I =225.07,

[0084] Step 3: Assuming the task cycle is 40 days, the states of the four processing machines and the corresponding Weibull distribution parameters can be obtained based on the operating data.

[0085]

[0086] Therefore, the operational robustness of the manufacturing system is R(t) = r1(t)·r2(t)·r3(t)·r4(t) = 0.8193 (all processing machines are operating normally, that is, the s of the four processing machines are break,m =1)

[0087] Step 4: Collect data about machine maintenance, the corresponding relationship between maintenance cost, maintenance time and maintenance quality of each machine. The acceptable state machine and state of each processing machine are s 1,j ≥240,s 2,j ≥260,s 3,j ≥280,s 4,j ≥210, the total maintenance cost is C0=8000, and the total maintenance time is T0=4. The fixed resources and maximum resources consumed by each processing machine maintenance are shown in the table below. Figure 3The selective maintenance framework diagram determines the maintenance decision. The next task running time is t m =50day. Obviously, the total maintenance resources are not enough to restore all machines to optimal condition.

[0088] Step 5: Establish a selective maintenance decision model based on steps 2 and 4 to rationally allocate maintenance resources and maximize the robustness of the next stage.

[0089] Table 5. Fixed resources and maximum resources consumed by machine maintenance

[0090]

[0091] Step 6: After determining all the parameters of the maintenance optimization model, the optimal maintenance strategy is determined by the ASA-PSO algorithm. In addition to the ASA-PSO parameter settings mentioned above, the population size (50) and the number of iterations (1000) are also set. The final maintenance strategy of the ferrite phase shift unit manufacturing system and the robustness of the corresponding manufacturing system are given in Table 6.

[0092] Table 6. Optimal maintenance strategy and results

[0093]

[0094] Traditional selective maintenance methods usually rely on worker experience, historical data, and regular inspections. Workers use intuition to determine the condition of the equipment and the maintenance work that needs to be done. Based on the opinions of experts and production line workers collected in this study, the acceptable state of each machine is s 1,j ≥180,s 2,j ≥195,s 3,j ≥210,s 4,j ≥140. The traditional selective maintenance strategy is shown in Table 7.

[0095] Table 7. Traditional selective repair results

[0096]

[0097] In the absence of information about the intrinsic relationship between the robustness of the manufacturing system and the quality of the machine and product, the traditional selective maintenance method cannot guarantee the robustness of the manufacturing system in the next stage. In addition, the overestimation of the acceptable processing state of the processing system leads to inefficient utilization of maintenance resources. These gaps lead to unplanned downtime of the manufacturing system during task execution. Therefore, the proposed method fully considers the interaction between the robustness of the manufacturing system, machine performance and product quality, reasonably and effectively evaluates the robustness of task completion in the next stage, improves the utilization of maintenance resources, and can accurately guide maintenance activities.

[0098] Based on this, the selective maintenance decision optimization of a manufacturing system has been completed. This modeling method can be used for subsequent production plan formulation and task scheduling.

Claims

1. A selective maintenance decision optimization method for manufacturing systems considering operational robustness is set as follows: Setting 1: The degradation of all processing machines in the manufacturing system follows a semi-Markov process; Setting 2: The performance status of each processing machine refers to the workload that the processing machine can process under normal circumstances; Setting 3: The corresponding key quality characteristics processed by each machine equipment are considered independent of each other; Setting 4: After each processing is completed, there is an absolutely reliable checkpoint to check the quality status of the output product; Setting 5: The processing capacity of the machine has a finite number of discrete states, and the corresponding probabilities are proportional; Setting 6. The robustness of the manufacturing system affects the probability of the processing machine being in each performance state; Setting 7. The robustness of the manufacturing system is not affected by human and environmental factors; Setting 8. The cost and time of manufacturing system maintenance are related to the status before and after maintenance, and have a one-to-one correspondence; Based on the above configuration, it is characterized by: The steps include: Step 1: After a task cycle of the manufacturing system is completed, collect and analyze the data of the manufacturing system after the task is completed; Step 2, determining the performance status of the manufacturing system equipment, the deviation of the product quality characteristics and the product quality according to step 1; Step 3: Determine the current robustness level of the manufacturing system according to step 2; Step 4: Collect data on machine maintenance, including the corresponding relationship between maintenance cost, maintenance time and maintenance quality of each machine; Step 5: Based on Step 2 and Step 4, a selective maintenance decision model is established with the goal of maximizing the robustness of the next task phase and the maintenance time and cost as constraints; Step 6: Establish the ASA-PSO algorithm model to obtain the optimal maintenance strategy.

2. The manufacturing system selective maintenance decision optimization method considering operation robustness according to claim 1 is characterized by: Step 1 specifically means that after a task cycle is completed, the equipment in the manufacturing system may degrade to varying degrees, and it is necessary to collect machine performance status data and work-in-process quality deviation data to facilitate the subsequent evaluation of the robustness of the manufacturing system.

3. The manufacturing system selective maintenance decision optimization method considering operation robustness according to claim 1, characterized in that: In step 2, it is assumed that the normal working time of the processing equipment follows the Weibull distribution. Among them, β is the shape parameter, η is the scale parameter, and β ≥ 0; in addition, the failure probability function of the Weibull distribution is The cumulative failure probability of the Weibull distribution is: The larger the task load, the smaller the scale parameter; the more robust the process, the larger the scale parameter and the closer it is to 1; since the stay of the processing machine in any state obeys the Weibull distribution, the probability of the processing machine being in each state at any time t is expressed as: p(t) = {p1(t), p2(t), p3(t), …, p w (t)} and The performance of the machining machine tool continues to maintain s within the Δt time x The probability of the state is:

4. The manufacturing system selective maintenance decision optimization method considering operation robustness according to claim 3 is characterized by: Assume that the processing machine has w performance states, namely {s1,s2,…,s w }, at any time t, the performance state of the processing machine must be in one of these w performance states, x∈{1,2,…,w}, so at any time t, assuming that the performance state of the processing machine is s x ; F x (t) represents the processing machine at time t at s x The probability of the state, F x (t+Δt) means that the processing machine is in s at time t+Δt x The probability of; In addition, the deviation of the key quality characteristics of the workpiece in process is expressed as: where ε i,a is a constant, and are the influence vectors of machine degradation factors and noise factors on the key quality characteristics of the workpiece in process; i (t) and ψ i (t) respectively represent the vector set of controllable factors and noise factors; Θ i,a is the mutual influence effect between the above two influences; suppose there is l on the workstation i a A key quality characteristic, when χ i When (t) = 0, the machine performance is in the best state, then the deviation of the key quality characteristics under the ideal state is: The deviation of the critical quality characteristic is then:

5. The manufacturing system selective maintenance decision optimization method considering operation robustness according to claim 1, characterized in that: In step 3, the robustness of the processing equipment at time t is: The random variable J represents the length of time from the start of operation to failure of the processing machine. The robustness is defined as the machine operation time t is less than the random variable J. Therefore, the probability that the failure time is longer than time t is defined as the robustness of the manufacturing system at that moment, that is, P(J>t).

6. The manufacturing system selective maintenance decision optimization method considering operation robustness according to claim 1, characterized in that: In step 4, the maintenance data of each processing equipment in the manufacturing system is collected, and the corresponding relationship between the maintenance cost and maintenance time of each machine and the performance status of the machine before and after the maintenance is obtained; the faulty or aged machines in the manufacturing system are restored by performing maintenance activities within each task interval to ensure the robustness of the manufacturing system in the next task; Each device has M c +1 optional maintenance activity. During the mth maintenance period, the maintenance activities available to machine i are g. i,m And g i,m ∈{0,1,…,M c }, g i,m The larger the g, the better the maintenance effect; i,m = 0, the machine is not repaired. i,m =M c When , the machine is perfectly repaired, so the maintenance cost of performing maintenance activities on machine i is: in, is the fixed maintenance cost of machine i, c i (g i,m ,s i,m ) is the variable maintenance cost of repair activities.

7. The manufacturing system selective maintenance decision optimization method considering operation robustness according to claim 1, characterized in that: When processing machine i is in state s at the end of task m i,m , which depends on the maintenance degree and the machine status; therefore, the total maintenance cost of the manufacturing system in the mth task cycle is: The maintenance time for machine i to perform maintenance activities in the mth task interval is: in, is the fixed maintenance time of machine i, t i (g i,m ,s i,m ) is the variable maintenance time of the maintenance activity of machine i, whose value depends on the maintenance level and the machine status; Therefore, the maintenance of each device in the manufacturing system is parallel within any task interval, and the total maintenance time of the manufacturing system is defined as T m =f(T 1,m ,T 2,m ,…,T p,m ), where T p,m represents the time required to repair machine p during the mth maintenance period; if the maintenance activity of each machine is performed once within the task interval, the maintenance time of the manufacturing system is the sum of the individual maintenance times; if the maintenance activity of each machine is independent and parallel, then the maintenance time of the manufacturing system is equal to the longest maintenance time of all machines.

8. The manufacturing system selective maintenance decision optimization method considering operation robustness according to claim 1, characterized in that: In step 5, the selective maintenance decision model is established with the goal of maximizing the robustness of the next task phase and the maintenance time and cost as constraints. The KijimaⅡ model is used to characterize the maintenance effectiveness U of the maintenance behavior through service life regression. l,m+1 =b l,m ·V l,m , where U l,m+1 is the effective service life of machine l after maintenance operation in the mth task interval, V l,m is the duration of the mth task, b i,m ≥1 is the service life regression factor, which is affected by the maintenance resource investment, b i,m The smaller it is, the better the maintenance effect is; therefore, the conditional survival probability of machine l after maintenance is: Among them, T m represents the duration of task m, η lx is the scale parameter of the Weibull distribution of machine l in state x, β lx is the shape parameter of the Weibull distribution of machine l in state x; the robustness of the machine in the mth task depends on the conditional survival probability r(t) and the state s of the machine at the end of the mth task break,m ; The robustness expression of machine l is R l (m) = r l (m)s break,m ,in, Therefore, the robustness of the entire manufacturing system is Assume that the pre-allocated maintenance resources are: maintenance cost C0, and maintenance time T0.

9. The manufacturing system selective maintenance decision optimization method considering operation robustness according to claim 8, characterized in that: The maintenance decision model is: Objective function: Constraints: Equations (2) and (3) indicate that the overall maintenance cost and maintenance time of the manufacturing system cannot exceed the resource constraints. Equations (4) and (5) indicate the variable constraints in the selective maintenance decision problem.

10. The manufacturing system selective maintenance decision optimization method considering operation robustness according to claim 1, characterized in that: In step 6, the pseudo code of the selective maintenance decision based on ASA-PSO is as follows: Input: initial particle swarm number pop, maximum number of iterations Max_iter, lower limit of optimal solution lb, upper limit of optimal solution ub, dimension dim, objective function fobj, maximum value of velocity vector Vmax, minimum value of velocity vector Vmin; Output: the location of the optimal solution, the fitness of the optimal solution; Initialize parameters, initialize individual particle extrema and global extrema; If the upper limit of the optimal solution is a scalar, convert the upper limit of the optimal solution into a matrix with 1 row and 'dimension' column, where each element is the upper limit of the optimal solution; convert the lower limit of the optimal solution into a matrix with 1 row and 'dimension' column, where each element is the lower limit of the optimal solution; Start the loop from 1 until the maximum number of iterations is reached; update the inertia weight and acceleration constant according to the following formula V max =l×X max ,l∈[0.1,1], where k is the number of iterations, V id is the velocity of particle i in dimension d, X id is the position of the ith particle in dimension d, ω is the inertial control coefficient, r1 and r2 are random numbers in the interval [0,1], c1 is the self-learning factor, c2 is the social influence factor, X max is the maximum limit of the solution; Update the velocity and position of each particle according to the following formula: Where e is the natural logarithm, E(i) and E(j) represent the internal energy of the object in state i and state j respectively, and K is the Boltzmann constant; Start the cycle from 1 until the number of particle groups is reached, and perform the following calculation: If the fitness of the jth particle is less than the global optimal fitness of the jth dimension currently recorded; update the position of the jth particle to the global optimal position of the current dimension; update the global optimal fitness of the jth dimension; If the fitness of the jth particle is less than the currently recorded global optimal fitness; update the global optimal position; update the global optimal fitness; Record the optimal fitness of the current iteration into the curve array, and output the position of the optimal solution and the fitness of the optimal solution.

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