CN105785954B - Manufacture system mission reliability modeling method based on quality state Task Network - Google Patents
Manufacture system mission reliability modeling method based on quality state Task Network Download PDFInfo
- Publication number
- CN105785954B CN105785954B CN201610258394.XA CN201610258394A CN105785954B CN 105785954 B CN105785954 B CN 105785954B CN 201610258394 A CN201610258394 A CN 201610258394A CN 105785954 B CN105785954 B CN 105785954B
- Authority
- CN
- China
- Prior art keywords
- reliability
- manufacture system
- mission
- quality
- state
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Classifications
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B19/00—Programme-control systems
- G05B19/02—Programme-control systems electric
- G05B19/418—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS], computer integrated manufacturing [CIM]
- G05B19/41885—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS], computer integrated manufacturing [CIM] characterised by modeling, simulation of the manufacturing system
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B2219/00—Program-control systems
- G05B2219/30—Nc systems
- G05B2219/32—Operator till task planning
- G05B2219/32339—Object oriented modeling, design, analysis, implementation, simulation language
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/80—Management or planning
Abstract
A kind of manufacture system mission reliability modeling method based on quality state Task Network, steps are as follows：The incidence relation of 1 structure manufacture system mission reliability and product reliability；The 2 analyses quality state that material is likely to occur after the processing of each relevant device；3 establish manufacture system quality state Task Network model；The working ability state distribution of 4 each relevant devices of analysis and probability；The manufacture qualified probability of 5 each relevant devices of estimation；What 6 Task-decomposings quantified each relevant device divides mission payload；7 identifications meet the equipment processing ability lower limit for dividing mission payload requirement；8 establish relevant device divide mission reliability model, build manufacture system mission reliability collective model；9 analyses discuss the dynamic changing curve of manufacture system Task Reliability；The present invention establishes the reliability model based on quality state Task Network, is productions activity provides effective foundation, enterprise productivity effect and the competitiveness such as production scheduling, quality control and equipment Preventive Maintenance.
Description
Technical field
The method for the manufacture system mission reliability modeling based on quality state Task Network that the present invention provides a kind of, belongs to
In Reliability modeling and analysis technical field.
Background technology
The pressure faced with the continuous improvement of China status in global manufacturing and competition also constantly increase.As
The carrier of output of products, reliable manufacture system are an important factor for ensureing product quality and productivity.In face of complicated manufacture
System, Reliability modeling often concentrate the fault condition of the equipment of concern manufacture system itself with analysis method, analytical equipment
Various failure modes and crash rate variation tendency, and then according to the variation of equipment failure rate, instruct enterprise that the equipment being directed to is unfolded
Periodically repair and maintenance.
From the perspective of system engineering, product is the output of manufacturing process, and manufacture system is that the substance of manufacturing process carries
Body, therefore, the reliability (Reliability, R) of manufacture system, the quality (Quality, Q) of manufacturing process and product it is reliable
Property (Reliability, R) three between there is the natural relationship that influences each other.Each Critical to quality of product is in related process
It is processed by perfection in equipment, it is final to be formed with the integrated qualified products of performance and function synthesized.Therefore, a large amount of engineerings are real
Trample proof：One timing of product design, product reliability depend on the height of manufacture system reliability and manufacturing process quality.And from
From the point of view of production manager, for a certain given production task, the quality and reliability index of product is on the one hand production
Figureofmerit is also very important.Manufacture system is usually composed of multiple process equipments, have intrinsic complexity with it is polymorphic
Property the characteristics of, dynamic change the characteristics of more exacerbating manufacture system polymorphism that production task requires, work for reliability assessment
Bring huge challenge.Manufacture system mission reliability refers to that manufacture system is advised with completion in the stipulated time under prescribed conditions
Determine the ability of production task.The productions such as production scheduling, quality control and equipment Preventive Maintenance are carried out as Instructing manufacture manager
The accurate estimation of movable effective foundation, manufacture system mission reliability has very important status in process of production, is
The premise of productivity effect and international competitiveness improves in manufacturing enterprise.How to realize manufacture system mission reliability be effectively estimated to
It is manufacturing field and the sciences problems that Reliability Engineering field is generally acknowledged that support, which produces movable dynamic dispatching,.
The fault condition of the more concern manufacture system equipment itself of the research of manufacture system Reliability modeling at this stage, base
A static modeling result is obtained in the basic reliability of system component, and then instructs correction maintenance, this method is undoubtedly neglected
Requirement and limitation from production task and product quality and reliability are omited.Manufacture product quality is included in manufacture by part research
System Reliability Research scope but still has ignored the dynamic characteristic of manufacture system.It can not be from being for existing Research Thinking
Engineering viewpoint of uniting merges manufacture system dynamic, can not be the productions activities such as production scheduling, quality control and equipment Preventive Maintenance
The accurate foundation of offer limitation, between analysis manufacture system mission reliability and product reliability that this patent passes through system
Incidence relation, excavate the Critical to quality data that accumulate in the fabrication process, and combine mission requirements each in manufacture system
Transmission between relative stations analyzes the ability that current manufacturing system completes Given task requirement, and then combines the fabrication stage
Quality management and control measures ensure the progress that can have scientific basis of production activity.It fundamentally makes up and ignores in traditional sense specifically
The deficiency of the static reliability modeling method of mission requirements.The heavy losses that increasingly fierce market competition is brought with post
Determine the importance and urgency for carrying out manufacture system dynamic task Reliability modeling.For this purpose, The present invention gives a kind of bases
In the method that the manufacture system mission reliability of quality state Task Network models, for assessing the system required based on Given task
Make system DYNAMIC RELIABILITY.It is raw that integrated production scheduling, quality control and equipment Preventive Maintenance etc. are carried out for production manager
Production activity provides effective foundation.
Invention content
(1) purpose of the present invention：
Only paid attention to determine for studying based on the manufacture system Reliability modeling of production equipment fault condition itself in the past
With Improving Equipment state, the present invention provides a kind of new manufacture system reliability estimation method --- and it is a kind of to be appointed based on quality state
The manufacture system mission reliability modeling method of business network.To execute the manufacture system based on Given task requirement before production task
Reliability assessment is visual angle, has considered the fingers such as mission requirements, product characteristic, equipment capacity and product qualified probability
Mark, the characteristics of fully considering and paid attention to manufacturing process quality of material state change and manufacture system intrinsic polymorphism, throughput
Change meets the probability of mission requirements to control production activity.Under the background of manufacturing systems engineering, it is contemplated that process quality data
The dynamic reliability state that manufacture system can be characterized, can with product from the angle analysis of system manufacture system mission reliability
By the incidence relation of property, and based on this incidence relation excavate the fabrication stage accumulate to product Critical to quality and related set
Standby related process quality data, to which apparent manufacture system task is by the mechanism of property analysis.Further, it is held for characterization task
The reverse transmission of quality of material state, the variation of equipment processing ability state and mission requirements in manufacture system during row,
This patent proposes the quality state Task Network model of manufacture system.And then in conjunction with specific tasks requirement, realization is to manufacture
The dynamic estimation of system mission reliability.
(2) technical solution：
The present invention is a kind of manufacture system mission reliability modeling method based on quality state Task Network, the base of proposition
This hypothesis is as follows：
Assuming that the production model of 1 manufacture system is assembly line processing, the production of stock formula；
Assuming that 2 manufacture systems are tandem, and each process equipment is physically mutual indepedent；
Assuming that 3 manufacture systems only exist process of doing over again together, and carried out only on current device；
Assuming that 4 in quality state Task Network, all there are one detection stations after every process equipment, and testing result is
It is cocksure；
Assuming that quality of material state is divided into three kinds in 5 quality state Task Networks：Eligible state (S1)；It is defective to repair
State (S2)；It is unqualified to scrap state (S3).Only the material of eligible state can enter next process；
Assuming that 6 defective repair state S2It is only possible to appear in the process that done over again, and only on current device
Reprocess it is primary, i.e., if it is still unqualified after reprocessing, be classified as unqualified scrapping state (S3)；
Assuming that 7 manufacture qualified probabilities obey U distributions；
Based on above-mentioned it is assumed that the present invention is based on the manufacture system mission reliability modeling method of quality state Task Network,
Its step are as follows：
Step 1 build manufacture system mission reliability and product reliability incidence relation, and then identify critical process and
Equipment；
The step 2 analysis quality state that material is likely to occur after the processing of each relevant device；
Step 3 establishes manufacture system quality state Task Network model；
Step 4 analyzes the distribution of working ability state 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 and divides mission payload based on Given task requirement；
Step 7 identification meets equipment processing ability lower limit (the i.e. C for dividing mission payload requirementiv)；
What step 8 established relevant device divides mission reliability model, and then builds the manufacture system required based on Given task
System mission reliability collective model；
Step 9 analysis discusses the dynamic changing curve of manufacture system Task Reliability.
Wherein, " incidence relation of structure manufacture system mission reliability and product reliability " in step 1 be
Refer to established under system engineering background manufacture system mission reliability, manufacturing process quality, product reliability incidence relation.Such as
Shown in Fig. 1, main mechanism is：Product reliability demand major embodiment is in the performance of product, and the usability of product
It can then mainly be determined by product Critical to quality；It is mapped by the decomposition of product Critical to quality, can recognize that critical process
And relevant device, and then the critical process qualitative data accumulated in the fabrication process is targetedly excavated, and batch production process production
Product reliability can be portrayed again using product qualified probability in critical process qualitative data；
Here, c indicates do-over ratio shared in whole certified products, Rp(t) indicate that batch production product is inherently reliable
Property, Ro(t) designed reliability, R are indicatedh(t) inherent reliability of certified products after doing over again, ρ are indicatedsr1Indicate the manufacture of process of doing over again
Qualified probability.
Wherein, described " analyzing the quality state that material is likely to occur after the processing of each relevant device " in step 2 be
According to the classification of quality of material state in quality state Task Network, analysis material after each relevant device may be presented
Quality state Sij；Here, i indicates that device numbering, j indicate quality state label, can use 1,2,3.Such as：S21It indicates by setting
The state of material qualification after standby 2 processing.
Wherein, described " establishing manufacture system quality state Task Network model " in step 3, refers to based on identification
Equipment and its quality of material state are showed manufacture system, as shown in Figure 2 in the form of quality state Task Network.
Wherein, " distribution of working ability state and the probability of analyzing each relevant device " in step 4, refers to being based on
The statistical data of production management department whithin a period of time, the distribution of equipment can bear in the unit of analysis time processing load and
Probability.Equipment due to the other factors such as equipment fault, local fault, repair influence, equipment processing ability state be it is random,
Therefore certain group is chosen away from statistics working ability appears in the probability in each interval range.
Wherein, " the manufacture qualified probability for estimating each relevant device " in steps of 5, refers to utilizing the side Bayesian
Method estimates that the material state of equipment i outputs in quality state Task Network is the probability ρ of eligible statesi1, it is qualified general to obtain manufacture
The expression formula of rateHere, a, b are distributed constant, and w is test sample capacity, and x is pass the test sample number.
It is wherein, described in step 6 that " Task-decomposing quantifies each relevant device based on Given task requirement
Divide mission payload ", refer to the input/output relation for quantifying manufacture system based on quality state Task Network modelHere, O indicates the qualification that the raw material of manufacture system input I units can export
Product number.It is in turn d >=O based on the condition of mission requirements is met, obtains system minimum input loadHere, d is a given mission requirements, and i is equipment identity, and n appoints for quality state
Production equipment in network model of being engaged in is total, and r is the device numbering with process of doing over again.In turn, each relevant device is based on given appoint
Business requires d's mission payload is divided to be represented by：
Here,Indicate equipment i distribution divides mission payload.
It is wherein, described in step 7 that " identification meets the equipment processing ability lower limit i.e. C for dividing mission payload requirementiv", it is
Finger, which is found out in equipment processing ability distribution and probability tables, to be metMinimum value.
It is wherein, described in step 8 that " that establishes relevant device divides mission reliability model, and then builds based on given
The manufacture system mission reliability collective model of mission requirements " refers to that each equipment processing ability satisfaction point of quantization is appointed
The probability R of business loading demandsti=Pr { Cix|Cix≥Civ, and then closed according to the functional structure between each production equipment
System integrates each point of mission reliability model, obtains manufacture system mission reliability modelHere, RtiDivide Task Reliability for equipment i.
Wherein, " analysis discusses the dynamic changing curve of manufacture system Task Reliability " in step 9, refers to borrowing
Matlab is helped to program, analysis discusses that manufacture system mission reliability with the dynamic change trend of mission requirements, qualified probability, is made a living
It produces movable decision and scientific guidance is provided.
By above step, the manufacture system based on quality state Task Network required towards specific production task is established
System mission reliability model, has reached the engineering purpose of equipment performance combinations of states actual production task, has solved traditional static
Reliability modeling result cannot accurately reflect the problem of system practical production status, and the life of scientific system is carried out for production manager
The productions activity provides effective foundation such as production scheduling, quality control and equipment Preventive Maintenance, to reduce in production activity due to
Economic loss caused by decision deviation, enterprise productivity effect and competitiveness.
(3) a kind of manufacture system mission reliability modeling method based on quality state Task Network of the present invention,
Its application method is as follows：
Step 1 determines the Critical to quality of product according to product quality and reliability big data, is then based on quality work(
It can be unfolded to carry out the decomposition mapping of Critical to quality, identify related process and production equipment.
Step 2 analyzes possible quality of material state according to the technology characteristics of each related process.
Step 3 establishes quality state Task Network model.
Step 4 builds the distribution of working ability state and the probabilistic information table of equipment.
Step 5 estimates each device fabrication qualified probability ρsi1。
Step 6, which determines, completes minimum input load needed for mission requirements, and quantifies each relevant device and wanted based on Given task
That asks divides mission payload.
Step 7 identification meets the equipment processing ability lower limit for dividing mission payload requirement.
Step 8 assessment divides Task Reliability, and then estimates manufacture system Task Reliability.
Step 9 analyzes manufacture system Task Reliability with mission requirements, the change curve of qualified probability.
(4) advantage and effect：
The present invention is a kind of manufacture system mission reliability modeling method based on quality state Task Network, advantage
It is：
I. the present invention considers emphatically the polymorphic sex chromosome mosaicism of manufacture system, breaches traditional static Reliability modeling and is difficult to standard
Really reflect the bottleneck of functions of the equipments state comprehensively.
Ii. quality state Task Network model can fully reflect that equipment state in manufacturing process, quality of material state become
Change and quantity changes, manufacturing process qualitative data can be fully integrated, manufacturing process qualitative data is solved and be difficult to make full use of
The problem of.
Iii. the present invention has high specific aim, science and practicability, makes a living using specific tasks requirement as starting point
It produces the activities such as the scheduling of manager's Instructing manufacture, quality control and preventative maintenance and scientific basis is provided.
Description of the drawings
Fig. 1 is manufacture system mission reliability and the incidence relation of product reliability.
Fig. 2 is manufacture system quality state Task Network model.
Fig. 3 is the method for the invention flow chart.
Fig. 4 is the quality state Task Network model of cylinder head manufacture system.
Fig. 5 is manufacture system mission reliability with mission requirements, the change curve of qualified probability.
Symbol description is as follows in figure：
BsijRefer to quality state SijMaterial quantity
It refer to the input load of equipment i
ρsijRefer to that equipment i exports quality state as SijProbability
Specific implementation mode
The present invention is described in further details below in conjunction with attached drawing and example.
The present invention is a kind of manufacture system mission reliability modeling method based on quality state Task Network, sees Fig. 3 institutes
Show, its step are as follows
Step 1 collects the manufacture information and related reliability information of certain four cylinder diesel engine cylinder head of model.Then base
In manufacture system mission reliability and product reliability incidence relation, such as Fig. 1, it is special to carry out Key Quality for quality function deployment
Property decomposition mapping, engine cylinder cap manufacture system related keyword technique and production equipment are identified, such as table 1.
1. Critical to quality of table and its manufacturing process information
Step 2 analyzes possible quality of material state according to the technology characteristics of each related process.Analysis through step 1 is true
5 main relevant devices are determined, according to actual process process, the analysis quality state that material may be presented after each relevant device
SijInformation, such as table 2.
2 quality of material status information analytical table of table
Step 3 establishes quality state Task Network model.Based on quality of material status information, with reference to generic fab system matter
State task network model is measured, such as Fig. 2 establishes the quality state task of certain four cylinder diesel engine cylinder head manufacture system of model
Network model, as shown in Figure 4.
Step 4 is estimated and builds the distribution of working ability state and the probabilistic information table of equipment.The gas processed with daily equipment
The quantity of cylinder cap analyzes the working ability state of each equipment, based on 12 months statistical results of production management department, is set
Standby working ability state distribution and probabilistic information, as shown in table 3.With machining center a1For, the working ability upper limit is
300, due to equipment local fault, failure and maintenance etc., working ability is not constant, according to statistical number
According to, setting group away from being 50, then a1Working ability state distribution be represented by { 0,50,100,150,200,250,300 }, then
Count a1The probability that occurs in each segment of working ability state, you can obtain a1Working ability state distribution and probability
Information.Similarly, the working ability state distribution of other equipment and probabilistic information also can be obtained.
Step 5 estimates each device fabrication qualified probability ρsi1, it is based on priori statistical data and test data, is utilized
Bayesian methods distinguish estimation device and manufacture qualified probability, and the results are shown in Table 3.
3 device fabrication qualified probability of table, the distribution of working ability state and probabilistic information
Step 6, which determines, completes minimum input load needed for mission requirements, and quantifies each relevant device and wanted based on Given task
That asks divides mission payload.By taking mission requirements d=150/ is daily as an example, to complete manufacturing operation requirement, system minimum input load
For：
And then quantifies each relevant device and divide mission payload based on Given task requirement：
Step 7 identification meets the equipment processing ability lower limit for dividing mission payload requirement.Data in contrast table 3, it may be determined that 5
Platform equipment satisfaction divides the equipment processing ability lower limit of mission payload requirement to be respectively：
C1v=200；C2v=180；C3v=180；C4v=200；C5v=160.
Step 8 assessment divides Task Reliability, and it is as follows that each equipment divides Task Reliability model that can establish：
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 established as follows：
The result indicates daily for mission requirements d=150/, which completes the general of task under current state
Rate is 89.1%, in practical applications, can be by reducing the setting group in step 3 away to reduce modeling error.
Step 9 discusses manufacture system mission reliability with mission requirements, the change curve of qualified probability.It is compiled by Matlab
Journey, the variation for making system task reliability with mission requirements, qualified probability are as shown in Figure 5.In Fig. 5, manufactured under same condition
System task reliability is successively decreased with the increase of mission requirements, this carries out rational production scheduling for production manager and provides section
Learn foundation.
Further, there is large effect to manufacture qualified probability due to the abrasion of equipment, carry out different qualified probability feelings
Contrast difference under condition between manufacture system mission reliability, as shown in Figure 5.The reduction of qualified probability can lead to manufacture system
The downslide of mission reliability, this is because the reduction of qualified probability leads to the same amount of qualified products of production in the unit interval,
System will bear the mission payload of bigger.This carries out rational quality control for production manager and is provided effectively with preventive maintenance
Guidance.
Claims (10)
1. a kind of manufacture system mission reliability modeling method based on quality state Task Network, the basic assumption of proposition is such as
Under：
Assuming that the production model of 1 manufacture system is assembly line processing, the production of stock formula；
Assuming that 2 manufacture systems are tandem, and each process equipment is physically mutual indepedent；
Assuming that 3 manufacture systems only exist process of doing over again together, and carried out only on current device；
Assuming that 4 in quality state Task Network, all there are one detection stations after every process equipment, and testing result is absolute
Reliably；
Assuming that quality of material state is divided into three kinds in 5 quality state Task Networks：Eligible state (S1)；It is defective to repair state
(S2)；It is unqualified to scrap state (S3)；Only the material of eligible state can enter next process；
Assuming that 6 defective repair state (S2) it is only possible appear in the process that can be done over again, and only reprocessed on current device
Once, i.e., it if still unqualified after reprocessing, is classified as unqualified scrapping state (S3)；
Assuming that 7 manufacture qualified probabilities obey U distributions；
It is special based on above-mentioned it is assumed that the present invention is based on the manufacture system mission reliability modeling method of quality state Task Network
Sign is：Its step are as follows：
Step 1 builds the incidence relation of manufacture system mission reliability and product reliability, and then identifies critical process and equipment；
The step 2 analysis quality state that material is likely to occur after the processing of each relevant device；
Step 3 establishes manufacture system quality state Task Network model；
Step 4 analyzes the distribution of working ability state 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 and divides mission payload based on Given task requirement；
Step 7 identification meets the equipment processing ability lower limit i.e. C for dividing mission payload requirementiv；
What step 8 established relevant device divides mission reliability model, and then builds the manufacture system times required based on Given task
Business Reliability Synthesis model；
Step 9 analysis discusses the dynamic changing curve of manufacture system Task Reliability；
By above step, establishes the manufacture system based on quality state Task Network required towards specific production task and appoint
Business reliability model, has reached the engineering purpose of equipment performance combinations of states actual production task, it is reliable to solve traditional static
Property modeling result the problem of cannot accurately reflecting system practical production status, the production tune of scientific system is carried out for production manager
It spends, the production activity provides effective foundation of quality control and equipment Preventive Maintenance, to reduce in production activity due to decision
Economic loss caused by deviation, enterprise productivity effect and competitiveness.
2. a kind of manufacture system mission reliability modeling side based on quality state Task Network according to claim 1
Method, it is characterised in that：" incidence relation of structure manufacture system mission reliability and product reliability " in step 1,
It refer to established under system engineering background manufacture system mission reliability, manufacturing process quality, product reliability association close
System, which is embodied in the performance of product, and the performance of product is then by product Critical to quality
It determines；It is mapped by the decomposition of product Critical to quality, identifies critical process and relevant device, and then targetedly excavate
The critical process qualitative data accumulated in the fabrication process, and batch production process product reliability can utilize critical process mass number
It is portrayed according to middle product qualified probability；
Here, c indicates do-over ratio shared in whole certified products, Rp(t) batch production product inherent reliability, R are indicatedo
(t) designed reliability, R are indicatedh(t) inherent reliability of certified products after doing over again, ρ are indicatedsr1Indicate to do over again process manufacture it is qualified
Probability.
3. a kind of manufacture system mission reliability modeling side based on quality state Task Network according to claim 2
Method, it is characterised in that：Described " analyzing the quality state that material is likely to occur after the processing of each relevant device " in step 2,
It is according to the classification of quality of material state in quality state Task Network, analysis material after each relevant device may be presented
Quality state Sij；Here, i indicates that device numbering, j indicate quality state label, take 1,2,3；S21It indicates to add by equipment 2
The state of material qualification after work.
4. a kind of manufacture system mission reliability modeling based on quality state Task Network according to claim 1 or 2
Method, it is characterised in that：Described " establishing manufacture system quality state Task Network model " in step 3 refers to based on knowledge
Other equipment and its quality of material state show manufacture system in the form of quality state Task Network.
5. a kind of manufacture system mission reliability modeling based on quality state Task Network according to claim 1 or 2
Method, it is characterised in that：" distribution of working ability state and the probability of analyzing each relevant device " in step 4 refers to
Statistical data based on production management department whithin a period of time, the processing load that equipment can bear in the unit of analysis time point
Cloth and probability；Equipment due to equipment fault, local fault, repair factor influence, equipment processing ability state be it is random, because
This chooses certain group away from statistics working ability appears in the probability in each interval range.
6. a kind of manufacture system mission reliability modeling based on quality state Task Network according to claim 1 or 2
Method, it is characterised in that：" the manufacture qualified probability for estimating each relevant device " in steps of 5 refers to utilizing
Bayesian methods estimate that the material state of equipment i outputs in quality state Task Network is the probability of eligible state, are made
Make the expression formula of qualified probabilityHere, a, b are distributed constant, and w is test sample capacity, and x is pass the test
Sample number.
7. a kind of manufacture system mission reliability modeling side based on quality state Task Network according to claim 6
Method, it is characterised in that：It is described in step 6 that " Task-decomposing quantifies each relevant device based on Given task requirement
Divide mission payload ", refer to the input/output relation for quantifying manufacture system based on quality state Task Network modelHere, O indicates the qualification that the raw material of manufacture system input I units can export
Product number；It is in turn d >=O based on the condition of mission requirements is met, obtains system minimum input loadHere, d is a given mission requirements, and i is equipment identity, and n appoints for quality state
Production equipment in network model of being engaged in is total, and r is the device numbering with process of doing over again；In turn, each relevant device is based on given appoint
Business requires d's mission payload is divided to be expressed as：
Here,Indicate equipment i distribution divides mission payload.
8. a kind of manufacture system mission reliability modeling side based on quality state Task Network according to claim 7
Method, it is characterised in that：" the equipment processing ability lower limit i.e. C for meeting and dividing mission payload requirement is identified described in step 7iv",
Refer to finding out in equipment processing ability distribution and probability tables to meetMinimum value.
9. a kind of manufacture system mission reliability modeling side based on quality state Task Network according to claim 8
Method, it is characterised in that：It is described in step 8 that " that establishes relevant device divides mission reliability model, and then builds base
In the manufacture system mission reliability collective model that Given task requires ", refer to that each equipment processing ability of quantization meets
The probability R for dividing mission payload to requireti=Pr { Cix|Cix≥Civ, and then according to the function knot between each production equipment
Structure relationship integrates each point of mission reliability model, obtains manufacture system mission reliability modelHere, RtiDivide Task Reliability for equipment i.
10. a kind of manufacture system mission reliability modeling side based on quality state Task Network according to claim 1
Method, it is characterised in that：" analysis discusses the dynamic changing curve of manufacture system Task Reliability " in step 9 refers to
It is programmed by Matlab, analysis discusses that manufacture system mission reliability with the dynamic change trend of mission requirements, qualified probability, is
It produces movable decision and scientific guidance is provided.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610258394.XA CN105785954B (en) | 2016-04-22 | 2016-04-22 | Manufacture system mission reliability modeling method based on quality state Task Network |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610258394.XA CN105785954B (en) | 2016-04-22 | 2016-04-22 | Manufacture system mission reliability modeling method based on quality state Task Network |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN105785954A CN105785954A (en) | 2016-07-20 |
| CN105785954B true CN105785954B (en) | 2018-10-02 |
Family
ID=56399378
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201610258394.XA Active CN105785954B (en) | 2016-04-22 | 2016-04-22 | Manufacture system mission reliability modeling method based on quality state Task Network |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN105785954B (en) |
Families Citing this family (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106249709B (en) * | 2016-07-21 | 2018-10-09 | 郑州航空工业管理学院 | Dynamic process quality control figure repairs co-design optimal control method with age is determined |
| CN107194826A (en) * | 2017-06-16 | 2017-09-22 | 北京航空航天大学 | A kind of manufacture system Gernral Check-up and Forecasting Methodology based on quality state Task Network |
| CN109799805B (en) * | 2019-01-17 | 2020-09-29 | 湖南大学 | Reliability-aware high-performance automobile electronic scheduling algorithm |
| CN109902931B (en) * | 2019-01-28 | 2021-04-13 | 北京航空航天大学 | Operation risk modeling method of multi-state manufacturing system based on operation data fusion |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102184292A (en) * | 2011-03-30 | 2011-09-14 | 北京航空航天大学 | Method for updating electronic product reliability prediction model complying with exponential distribution |
| TWI457846B (en) * | 2011-12-02 | 2014-10-21 | Univ Nat Taiwan Science Tech | System reliability evaluation method for manufacture network |
| CN104268392A (en) * | 2014-09-23 | 2015-01-07 | 北京航空航天大学 | Manufacturing process product reliability decline risk evaluating method based on quality deviation |
| CN104537134A (en) * | 2014-06-16 | 2015-04-22 | 北京空间飞行器总体设计部 | Method for reliability modeling and evaluation of dynamic cascade coupling electromechanical system |
| CN105160074A (en) * | 2015-08-07 | 2015-12-16 | 北京航空航天大学 | Product early failure rate estimation method considering manufacturing quality deviation data |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US9633323B2 (en) * | 2013-03-14 | 2017-04-25 | Lockheed Martin Corporation | Integrated modeling and analysis in a discrete event simulation |
-
2016
- 2016-04-22 CN CN201610258394.XA patent/CN105785954B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102184292A (en) * | 2011-03-30 | 2011-09-14 | 北京航空航天大学 | Method for updating electronic product reliability prediction model complying with exponential distribution |
| TWI457846B (en) * | 2011-12-02 | 2014-10-21 | Univ Nat Taiwan Science Tech | System reliability evaluation method for manufacture network |
| CN104537134A (en) * | 2014-06-16 | 2015-04-22 | 北京空间飞行器总体设计部 | Method for reliability modeling and evaluation of dynamic cascade coupling electromechanical system |
| CN104268392A (en) * | 2014-09-23 | 2015-01-07 | 北京航空航天大学 | Manufacturing process product reliability decline risk evaluating method based on quality deviation |
| CN105160074A (en) * | 2015-08-07 | 2015-12-16 | 北京航空航天大学 | Product early failure rate estimation method considering manufacturing quality deviation data |
Non-Patent Citations (2)
| Title |
|---|
| 基于产品可靠性的工艺系统可靠性模型;付桂翠，上官云;《北京航空航天大学学报》;20090115;第35卷(第1期);第9-12页 * |
| 基于关键质量特性的产品保质设计;何益海，唐晓青;《航空学报》;20071115;第28卷(第6期);第1468-1480页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN105785954A (en) | 2016-07-20 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN105785954B (en) | Manufacture system mission reliability modeling method based on quality state Task Network | |
| Li et al. | Clarifications on the definitions of elementary siphons in Petri nets | |
| CN108665164A (en) | Polymorphic manufacture system Evaluation Methods for Mission Reliability based on running quality data | |
| Joseph et al. | Effects of routing flexibility, sequencing flexibility and scheduling decision rules on the performance of a flexible manufacturing system | |
| Heimicke et al. | Comparison of existing agile approaches in the context of mechatronic system development: potentials and limits in implementation | |
| Yu et al. | Exploit the value of production data to discover opportunities for saving power consumption of production tools | |
| CN112200806A (en) | Wafer defect analysis method and system | |
| Reuter et al. | Improving data consistency in production control by adaptation of data mining algorithms | |
| CN105701691A (en) | Client-feature-database-based real-time recommendation system | |
| CN111815191A (en) | Intelligent workshop data model construction method for ship body segment | |
| Lyu et al. | A data-driven approach for identifying possible manufacturing processes and production parameters that cause product defects: A thin-film filter company case study | |
| Massaro et al. | Intelligent processes in automated production involving Industry 4.0 technologies and artificial intelligence | |
| US7164959B2 (en) | Method and system of classifying demand data | |
| CN110210531A (en) | Based on the fuzzy polymorphism manufacture system Evaluation Methods for Mission Reliability for extending random drift net | |
| Park et al. | Fab simulation with recipe arrangement of tools | |
| Pheng et al. | Artificial intelligence in back-end semiconductor manufacturing: A case study | |
| Filz et al. | Data-driven analysis of product property propagation to support process-integrated quality management in manufacturing systems | |
| Haghi et al. | Existing challenges and the corresponding approach towards a smart complaint and failure management process | |
| Joseph et al. | Simulation modelling and analysis of routing flexibility of a flexible manufacturing system | |
| CN113610575B (en) | Product sales prediction method and prediction system | |
| Indrawati et al. | Development of supply chain risks interrelationships model using interpretive structural modeling and analytical network process | |
| Sim | A study on the development and effect of smart manufacturing system in PCB line | |
| Chernov et al. | The Usage of Artificial Intelligence in Strategic Decision Making in Terms of Fourth Industrial Revolution | |
| US11763446B2 (en) | Wafer bin map based root cause analysis | |
| Renna | Evaluation of improvement programmes in a job-shop context by simulation models |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |