CN105785954A - Manufacturing system task reliability modeling method based on quality state task network - Google Patents
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Abstract
The invention discloses a manufacturing system task reliability modeling method based on a quality state task network. The method comprises the following steps: 1, a correlation between the manufacturing system task reliability and the product reliability is built; 2, the possible quality state of a material after being processed by each related device is analyzed; 3, a manufacturing system quality state task network model is built; 4, the processing ability state distribution and the probability of each related device are analyzed; 5, the manufacturing qualified probability of each related device is estimated; 6, the task decomposes and quantizes the sub task load of each related device; 7, the lower limit for the device processing ability meeting the sub task load requirements is recognized; 8, a sub task reliability model for the related device is built, and a manufacturing system task reliability comprehensive model is built; and 9, a dynamic change curve for the manufacturing system task reliability is analyzed and discussed. The reliability model based on the quality state task network is built, an effective basis is provided for production activities such as production scheduling, quality control and device prevention and maintenance, and the enterprise production benefits and the competitiveness are enhanced.
Description
Technical field
A kind of method that the invention provides manufacture system task Reliability modeling based on quality state Task Network, belongs to Reliability modeling and analysis technical field.
Background technology
Along with China's the improving constantly of status in global manufacturing, the pressure faced and competition also constantly increase.As the carrier of output of products, reliable manufacture system is to ensure that the key factor of product quality and productivity ratio.In the face of complicated manufacture system, Reliability modeling often concentrates the failure condition of the equipment paying close attention to manufacture system itself with analysis method, the various failure modes of analytical equipment and crash rate variation tendency, and then the change according to equipment failure rate, instruct enterprise launch for the maintenance of device periodically and maintenance.
From the angle of system engineering, product is the output of manufacture process, manufacture system is the material carrier of manufacture process, therefore, reliability (the Reliability of manufacture system, R), there is the natural relation that influences each other between quality (Quality, Q) and reliability (Reliability, the R) three of product of manufacture process.The each Critical to quality of product is processed by perfection on associated process equipment, finally could be formed and have performance and the integrated qualified products of function synthesized.Therefore, a large amount of engineering practices prove: product design one timing, and product reliability depends on the height manufacturing system reliability and manufacture process quality.And from the angle of production manager, for a certain given production task, simply on the one hand, yield index is also very important to the quality and reliability index of product.Manufacture system is generally combined by multiple process equipments, has the feature of intrinsic complexity and polymorphism, and the dynamic change that production task requires more exacerbates the feature of manufacture system polymorphism, brings huge challenge for reliability assessment work.Manufacture system task reliability and refer to that manufacture system completes the ability of regulation production task with in the stipulated time under prescribed conditions.Effective foundation of the production activities such as production scheduling, quality control and equipment Preventive Maintenance is carried out as Instructing manufacture manager, the accurate estimation manufacturing system task reliability has very important status in process of production, is the premise of manufacturing enterprise's raising productivity effect and international competitiveness.How to realize manufacturing system task reliability and effectively estimate thus the dynamic dispatching that supports production activity is manufacture field and Reliability Engineering field is generally acknowledged sciences problems.
Present stage manufactures the more failure condition paying close attention to manufacture system equipment itself of research of System reliability modeling, basic reliability based on system component obtains a static modeling result, and then instructing correction maintenance, this method have ignored the requirement from production task and product quality and reliability and restriction undoubtedly.Manufacture product quality is included in manufacture system Reliability Research category by part research, but still have ignored the dynamic characteristic of manufacture system.System dynamics can not be manufactured from system engineering angle fusion for existing Research Thinking, it cannot be production scheduling, the limitation that accurate foundation is provided of the production activities such as quality control and equipment Preventive Maintenance, the this patent incidence relation analyzed between manufacture system task reliability and product reliability by system, excavate the Critical to quality data accumulated in the fabrication process, and in conjunction with mission requirements transmission between each relative stations of manufacture system, analyze current manufacturing system and complete the ability of Given task requirement, and then the quality management and control measure in conjunction with the fabrication stage ensures the carrying out that production activity can have scientific basis.Fundamentally make up the deficiency ignoring the static reliability modeling method that specific tasks require in traditional sense.The heavy losses that the day by day fierce market competition and post bring determine carries out the importance and urgency manufacturing the modeling of system dynamics mission reliability.For this, a kind of method that The present invention gives manufacture system task Reliability modeling based on quality state Task Network, for assessing the manufacture system dynamics reliability based on Given task requirement.Carry out the production activities such as integrated production scheduling, quality control and equipment Preventive Maintenance for production manager and effective foundation is provided.
Summary of the invention
(1) purpose of the present invention:
Only paying attention to determine and Improving Equipment state for studying based on the manufacture System reliability modeling of the failure condition of production equipment own in the past, the present invention provides a kind of new manufacture a kind of manufacture system task Reliability Modeling based on quality state Task Network of system reliability estimation method.With the manufacture Reliability evaluation that performs to require based on Given task before production task for visual angle, the indexs such as mission requirements, product attribute, equipment capacity and product qualified probability are considered, take into full account and paid attention to the feature of the change of manufacture process quality of material state and the intrinsic polymorphism of manufacture system, control production activity by quantifying to meet the probability of mission requirements.Under the background of manufacturing systems engineering, consider that process quality data can characterize the dynamic reliability state of manufacture system, manufacture the incidence relation of system task reliability and product reliability from the angle analysis of system, and excavate the process quality data relevant with product Critical to quality and relevant device in fabrication stage accumulation based on this incidence relation, thus the mechanism that distinct manufacture system task is analyzed by property.Further, for characterizing quality of material state in tasks carrying process, the change of equipment processing ability state and the mission requirements reverse transmission in manufacture system, this patent proposes the quality state Task Network model of manufacture system.And then in conjunction with specific tasks requirement, it is achieved to the dynamic estimation manufacturing system task reliability.
(2) technical scheme:
The present invention is a kind of manufacture system task Reliability Modeling based on quality state Task Network, it is proposed to basic assumption as follows:
Assume that 1 production model manufacturing system is streamline processing, the production of stock formula；
Assume that 2 manufacture systems are tandem, and each process equipment is physically separate；
Assume that 3 manufacture systems only exist operation of doing over again together, and only carry out on current device；
Assume that 4 in quality state Task Network, after every process equipment, have a detection station, and testing result is cocksure；
Assume that in 5 quality state Task Networks, quality of material state is divided into three kinds: eligible state (S1)；Defective repair state (S2)；Defective scrap state (S3).Only the material of eligible state can enter next process；
Assume 6 quality state S2Only possible occur in the operation that can do over again, and only reprocess once on current device, if still defective after namely reprocessing, be then classified as and defective scrap state (S3)；
Assume that 7 manufacture qualified probabilities obey U distribution；
Based on above-mentioned it is assumed that the present invention is based on the manufacture system task Reliability Modeling of quality state Task Network, its step is as follows:
Step 1 builds the incidence relation manufacturing system task reliability with product reliability, and then identifies critical process and equipment；
Step 2 analyzes the quality state that material is likely to occur after each relevant device is processed；
Step 3 is set up and is manufactured mass of system state task network model；
Step 4 analyzes working ability distributions and the probability of each relevant device；
Step 5 estimates the manufacture qualified probability of each relevant device；
Step 6 Task-decomposing, quantifies each relevant device point mission payload based on Given task requirement；
Step 7 identifies and meets equipment processing ability lower limit (the i.e. C that point mission payload requiresiv)；
Step 8 sets up point mission reliability model of relevant device, and then builds the manufacture system task Reliability Synthesis model required based on Given task；
Step 9 is analyzed and the dynamic changing curve manufacturing system task reliability is discussed.
Wherein, " manufacturing the incidence relation of system task reliability and product reliability " described in step 1, refer to from system engineering background, set up the incidence relation manufacturing system task reliability, manufacture process quality, product reliability.As it is shown in figure 1, its main mechanism is: product reliability demand major embodiment is in the serviceability of product, and the serviceability of product is then mainly determined by product Critical to quality；Mapped by the decomposition of product Critical to quality, recognizable critical process and relevant device, and then excavate the critical process qualitative data accumulated in the fabrication process targetedly, and in batch available critical process qualitative data of product process product reliability, product qualified probability is portrayed；
Here, c represents the ratio that do-over is shared in whole certified products, RpT () represents batch product product inherent reliability, RoT () represents designed reliability, Rh(t) represent do over again after the inherent reliability of certified products, ρsr1Represent the manufacture qualified probability of operation of doing over again.
Wherein, " analyzing the quality state that material is likely to occur after each relevant device is processed " described in step 2, is according to the classification of quality of material state in quality state Task Network, analyzes material after each relevant device and is likely to the quality state S presentedij；Here, i represents that device numbering, j represent quality state label, desirable 1,2,3.Such as: S21Represent the state that material is qualified after equipment 2 is processed.
Wherein, " set up and manufacture mass of system state task network model " described in step 3, refer to based on the equipment identified and quality of material state thereof, manufacture system is showed with the form of quality state Task Network, as shown in Figure 2.
Wherein, " analyzing working ability distributions and the probability of each relevant device " described in step 4, refer to based on production management department's statistical data within a period of time, processing load distribution that in the unit of analysis time, equipment can bear and probability.Equipment is due to the impact of other factors such as equipment fault, local fault, maintenance, and equipment processing ability state is random, therefore choose certain group from, statistics working ability occur in the probability in each interval range.
Wherein, " estimating the manufacture qualified probability of each relevant device " described in steps of 5, refer to that utilizing each equipment outputting material state in Bayesian method estimation quality state Task Network is Si1Probability ρsi1, obtain manufacturing the expression formula of qualified probabilityHere, a, b are distributed constant, and n is test sample capacity, and x is pass the test sample number.
Wherein, " Task-decomposing quantifies each relevant device point mission payload based on Given task requirement " described in step 6, refer to the input/output relation quantifying manufacture system based on quality state Task Network modelHere, O represents the qualified products number that the raw material of the input I unit of manufacture system can export.And then be d >=O based on meeting the condition of mission requirements, obtain the minimum input load of systemHere, d is a given mission requirements, and i is equipment identity, and n is production equipment sum in quality state Task Network model, and r is the device numbering with operation of doing over again.And then, based on Given task, each relevant device requires that a point mission payload of d is represented by:
Here,Point mission payload of expression equipment i distribution.
Wherein, described in step 7 " the equipment processing ability lower limit of satisfied point of mission payload requirement of identification and a Civ", refer to that finding out equipment processing ability is distributed and meets in probability tablesMinima.
Wherein, " set up point mission reliability model of relevant device, and then build the manufacture system task Reliability Synthesis model required based on Given task " described in step 8, refer to that quantifying each equipment processing ability meets the probability R of point mission payload requirementti=Pr{Cix|Cix≥Civ, and then according to the functional structure relation between each production equipment, integrated each point of mission reliability model, obtain manufacturing system task reliability modelHere, RtiPoint Task Reliability of equipment i.
Wherein, " analyze and the dynamic changing curve manufacturing system task reliability be discussed " described in step 9, referring to and program by Matlab, analyze and the dynamic change trend manufacturing system task reliability with mission requirements, qualified probability is discussed, the decision-making for producing activity provides scientific guidance.
Pass through above step, establish the manufacture system task reliability model based on quality state Task Network required towards concrete production task, reach the engineering purpose of equipment performance combinations of states actual production task, solve the problem that traditional static Reliability modeling result can not accurately reflect system practical production status, the production scheduling of scientific system is carried out for production manager, the production activities such as quality control and equipment Preventive Maintenance provide effective foundation, thus reducing the economic loss caused in production activity due to decision-making deviation, enterprise productivity effect and competitiveness.
(3) a kind of manufacture system task Reliability Modeling based on quality state Task Network of the present invention, its using method is as follows:
Step 1 is according to product quality and the big data of reliability, it is determined that the Critical to quality of product, is then based on quality function deployment and carries out the decomposition mapping of Critical to quality, identifies related process and the equipment of production.
The step 2 technology characteristics according to each related process, analyzes possible quality of material state.
Step 3 sets up quality state Task Network model.
Step 4 builds working ability distributions and the probabilistic information table of equipment.
Step 5 estimates each device fabrication qualified probability ρsi1。
Step 6 is minimum input load needed for having determined mission requirements, and quantifies each relevant device point mission payload based on Given task requirement.
Step 7 identifies and meets the equipment processing ability lower limit that point mission payload requires.
Step 8 assessment point Task Reliability, and then estimation manufacture system task reliability.
Step 9 analyzes the change curve manufacturing system task reliability with mission requirements, qualified probability.
(4) advantage and effect:
The present invention is a kind of manufacture system task Reliability Modeling based on quality state Task Network, and its advantage is:
I. the present invention considers emphatically the polymorphism problem of manufacture system, breaches traditional static Reliability modeling and is difficult to accurately reflect the bottleneck of functions of the equipments state comprehensively.
Ii. quality state Task Network model can fully reflect the change of equipment state in manufacture process, quality of material state and number change, it is possible to is fully integrated manufacture process qualitative data, solves the problem that manufacture process qualitative data is difficult to make full use of.
Iii. the present invention is with specific tasks requirement for starting point, has high specific aim, science and practicality, dispatches for production manager Instructing manufacture, quality control and preventative maintenance etc. are movable provides scientific basis.
Accompanying drawing explanation
Fig. 1 is the incidence relation manufacturing system task reliability with product reliability.
Fig. 2 manufactures mass of system state task network model.
Fig. 3 is the method for the invention flow chart.
Fig. 4 is the quality state Task Network model that cylinder head manufactures system.
Fig. 5 is the change curve manufacturing system task reliability with mission requirements, qualified probability.
In figure, symbol description is as follows:
BsijRefer to quality state SijThe quantity of material
Refer to the input load of equipment i
ρsijReferring to that equipment i exports quality state is SijProbability
Detailed description of the invention
Below in conjunction with accompanying drawing and example, the present invention is described in further details.
The present invention is a kind of manufacture system task Reliability Modeling based on quality state Task Network, and as shown in Figure 3, its step is as follows
Step 1 collects manufacturing information and the related reliability information of certain model four cylinder diesel engine cylinder head.Being then based on manufacturing system task reliability and product reliability incidence relation, such as Fig. 1, the decomposition carrying out Critical to quality for quality function deployment maps, and identifies that engine cylinder cap manufactures system related keyword technique and the equipment of production, such as table 1.
Table 1. Critical to quality and manufacturing process information thereof
The step 2 technology characteristics according to each related process, analyzes possible quality of material state.Determine 5 main relevant devices through the analysis of step 1, according to actual process process, analyze material after each relevant device and be likely to the quality state S presentedijInformation, such as table 2.
Table 2 quality of material status information analytical table
Step 3 sets up quality state Task Network model.Based on quality of material status information, with reference to generic fab system quality state Task Network model, such as Fig. 2, set up certain model four cylinder diesel engine cylinder head and manufacture the quality state Task Network model of system, as shown in Figure 4.
Step 4 is estimated and builds working ability distributions and the probabilistic information table of equipment.With every day, the quantity of cylinder head of apparatus processing analyzes the working ability state of each equipment, based on production management department's statistical result of 12 months, obtains working ability distributions and the probabilistic information of equipment, as shown in table 3.With machining center a1For example, its working ability upper limit is 300, and due to reasons such as equipment local fault, fault and maintenances, its working ability is not constant, according to statistical data, setting group from for 50, then a1Working ability distributions be represented by 0,50,100,150,200,250,300}, then add up a1The probability that occurs in each segment of working ability state, a can be obtained1Working ability distributions and probabilistic information.In like manner, the working ability distributions of other equipment and probabilistic information also can obtain.
Step 5 estimates each device fabrication qualified probability ρsi1, based on priori statistical data and test data, utilize Bayesian method estimation device respectively to manufacture qualified probability, its result is as shown in table 3.
Table 3 device fabrication qualified probability, working ability distributions and probabilistic information
Step 6 is minimum input load needed for having determined mission requirements, and quantifies each relevant device point mission payload based on Given task requirement.For mission requirements d=150/ every day, for completing manufacturing operation requirement, the minimum input load of system is:
And then quantify each relevant device point mission payload based on Given task requirement:
Step 7 identifies and meets the equipment processing ability lower limit that point mission payload requires.Data in contrast table 3, it may be determined that 5 equipment meets the equipment processing ability lower limit of point mission payload requirement respectively:
C1v=200；C2v=180；C3v=180；C4v=200；C5v=160.
Step 8 assessment point Task Reliability, it is as follows that each equipment divides Task Reliability model to set up:
Rt1=Pr{C1x|C1x>=200}=0.978；
Rt2=Pr{C2x|C2x>=180}=0.988；
Rt3=Pr{C3x|C3x>=180}=0.983；
Rt4=Pr{C4x|C4x>=200}=0.970；
Rt5=Pr{C5x|C5x>=160}=0.967
So, the Task Reliability model of this four cylinders diesel engine cylinder head manufacture system can be set up as follows:
This result represents that it is 89.1% that this manufacture system completes the probability of task under current state for mission requirements d=150/ every day, in actual applications, the setting group that can pass through to reduce in step 3 from, to reduce modeling error.
Step 9 discusses the change curve manufacturing system task reliability with mission requirements, qualified probability.By Matlab program, manufacturing system mission reliability with mission requirements, qualified probability change as shown in Figure 5.In Fig. 5, manufacturing system task reliability and successively decrease with the increase of mission requirements under same condition, this carries out rational production scheduling for production manager and provides scientific basis.
Further, owing to the abrasion of equipment has large effect to manufacturing qualified probability, carry out manufacturing the contrast difference between system task reliability in different qualified probability situation, as shown in Figure 5.The reduction of qualified probability, can cause the downslide manufacturing system task reliability, this is because the reduction of qualified probability causes producing in the unit interval same amount of qualified products, system to bear bigger mission payload.This carries out rational quality control for production manager and provides effective guidance with preventive maintenance.
Claims (10)
1. the manufacture system task Reliability Modeling based on quality state Task Network, it is proposed to basic assumption as follows:
Assume that 1 production model manufacturing system is streamline processing, the production of stock formula；
Assume that 2 manufacture systems are tandem, and each process equipment is physically separate；
Assume that 3 manufacture systems only exist operation of doing over again together, and only carry out on current device；
Assume that 4 in quality state Task Network, after every process equipment, have a detection station, and testing result is cocksure；
Assume that in 5 quality state Task Networks, quality of material state is divided into three kinds: eligible state (S1)；Defective repair state (S2)；Defective scrap state (S3)；Only the material of eligible state can enter next process；
Assume 6 quality state S2Only possible occur in the operation that can do over again, and only reprocess once on current device, if still defective after namely reprocessing, be then classified as and defective scrap state (S3)；
Assume that 7 manufacture qualified probabilities obey U distribution；
Based on above-mentioned it is assumed that the present invention is based on the manufacture system task Reliability Modeling of quality state Task Network, it is characterised in that: its step is as follows:
Step 1 builds the incidence relation manufacturing system task reliability with product reliability, and then identifies critical process and equipment；
Step 2 analyzes the quality state that material is likely to occur after each relevant device is processed；
Step 3 is set up and is manufactured mass of system state task network model；
Step 4 analyzes working ability distributions and the probability of each relevant device；
Step 5 estimates the manufacture qualified probability of each relevant device；
Step 6 Task-decomposing, quantifies each relevant device point mission payload based on Given task requirement；
Step 7 identifies and meets equipment processing ability lower limit and the C that point mission payload requiresiv；
Step 8 sets up point mission reliability model of relevant device, and then builds the manufacture system task Reliability Synthesis model required based on Given task；
Step 9 is analyzed and the dynamic changing curve manufacturing system task reliability is discussed；
Pass through above step, establish the manufacture system task reliability model based on quality state Task Network required towards concrete production task, reach the engineering purpose of equipment performance combinations of states actual production task, solve the problem that traditional static Reliability modeling result can not accurately reflect system practical production status, the production scheduling of scientific system is carried out for production manager, the production activities such as quality control and equipment Preventive Maintenance provide effective foundation, thus reducing the economic loss caused in production activity due to decision-making deviation, enterprise productivity effect and competitiveness.
2. a kind of manufacture system task Reliability Modeling based on quality state Task Network according to claim 1, it is characterized in that: " manufacturing the incidence relation of system task reliability and product reliability " described in step 1, it refers to sets up the incidence relation manufacturing system task reliability, manufacture process quality, product reliability from system engineering background, this product reliability demand is embodied in the serviceability of product, and the serviceability of product is then determined by product Critical to quality；Mapped by the decomposition of product Critical to quality, identify critical process and relevant device, and then excavate the critical process qualitative data accumulated in the fabrication process targetedly, and batch product process product reliability can utilize product qualified probability in critical process qualitative data to portray；
Here, c represents the ratio that do-over is shared in whole certified products, RpT () represents batch product product inherent reliability, RoT () represents designed reliability, Rh(t) represent do over again after the inherent reliability of certified products, ρsr1Represent the manufacture qualified probability of operation of doing over again.
3. a kind of manufacture system task Reliability Modeling based on quality state Task Network according to claim 1, it is characterized in that: " analyzing the quality state that material is likely to occur after each relevant device is processed " described in step 2, it is according to the classification of quality of material state in quality state Task Network, analyzes material after each relevant device and be likely to the quality state S presentedij；Here, i represents that device numbering, j represent quality state label, takes 1,2,3；Such as: S21Represent the state that material is qualified after equipment 2 is processed.
4. a kind of manufacture system task Reliability Modeling based on quality state Task Network according to claim 1, it is characterized in that: " set up and manufacture mass of system state task network model " described in step 3, refer to based on the equipment identified and quality of material state thereof, manufacture system is showed with the form of quality state Task Network.
5. a kind of manufacture system task Reliability Modeling based on quality state Task Network according to claim 1, it is characterized in that: " analyzing working ability distributions and the probability of each relevant device " described in step 4, refer to based on production management department's statistical data within a period of time, processing load distribution that in the unit of analysis time, equipment can bear and probability；Equipment due to equipment fault, local fault, maintenance factor impact, equipment processing ability state is random, therefore choose certain group from, statistics working ability occur in the probability in each interval range.
6. a kind of manufacture system task Reliability Modeling based on quality state Task Network according to claim 1, it is characterized in that: " estimating the manufacture qualified probability of each relevant device " described in steps of 5, refer to that utilizing each equipment outputting material state in Bayesian method estimation quality state Task Network is Si1Probability ρsi1, obtain manufacturing the expression formula of qualified probabilityHere, a, b are distributed constant, and n is test sample capacity, and x is pass the test sample number.
7. a kind of manufacture system task Reliability Modeling based on quality state Task Network according to claim 1, it is characterized in that: " Task-decomposing; quantify each relevant device point mission payload based on Given task requirement " described in step 6, refer to the input/output relation quantifying manufacture system based on quality state Task Network modelHere, O represents the qualified products number that the raw material of the input I unit of manufacture system can export；And then be d >=O based on meeting the condition of mission requirements, obtain the minimum input load of systemHere, d is a given mission requirements, and i is equipment identity, and n is production equipment sum in quality state Task Network model, and r is the device numbering with operation of doing over again；And then, based on Given task, each relevant device requires that a point mission payload of d is expressed as:
Here,Point mission payload of expression equipment i distribution.
8. a kind of manufacture system task Reliability Modeling based on quality state Task Network according to claim 1, it is characterised in that: " the equipment processing ability lower limit of satisfied point of mission payload requirement of identification and a C described in step 7iv", refer to that finding out equipment processing ability is distributed and meets in probability tablesMinima.
9. a kind of manufacture system task Reliability Modeling based on quality state Task Network according to claim 1, it is characterized in that: described in step 8 " set up point mission reliability model of relevant device; and then build the manufacture system task Reliability Synthesis model required based on Given task ", refer to that quantifying each equipment processing ability meets the probability R of point mission payload requirementti=Pr{Cix|Cix≥Civ, and then according to the functional structure relation between each production equipment, integrated each point of mission reliability model, obtain manufacturing system task reliability modelHere, RtiPoint Task Reliability of equipment i.
10. a kind of manufacture system task Reliability Modeling based on quality state Task Network according to claim 1, it is characterized in that: " analyze and the dynamic changing curve manufacturing system task reliability is discussed " described in step 9, refer to and program by Matlab, analyzing and the dynamic change trend manufacturing system task reliability with mission requirements, qualified probability is discussed, the decision-making for producing activity provides scientific guidance.
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