CN106249190A - Method for testing reliability t under flow line circulation detection based on Markaus model - Google Patents
Method for testing reliability t under flow line circulation detection based on Markaus model Download PDFInfo
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- CN106249190A CN106249190A CN201610578454.6A CN201610578454A CN106249190A CN 106249190 A CN106249190 A CN 106249190A CN 201610578454 A CN201610578454 A CN 201610578454A CN 106249190 A CN106249190 A CN 106249190A
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R35/00—Testing or calibrating of apparatus covered by the other groups of this subclass
- G01R35/04—Testing or calibrating of apparatus covered by the other groups of this subclass of instruments for measuring time integral of power or current
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Abstract
The invention provides a method for testing reliability t under the condition of assembly line circulation detection based on a Markov model, which comprises the following steps: propose the original hypothesis H0And alternative hypothesis H1(ii) a Actually take n samples, give a level of significance(ii) a Obtaining a sample expected estimate; push button
Description
Technical field
The present invention relates to the lower reliability t method of inspection of a kind of streamline based on Malthus Model circulation calibrating.
Background technology
In recent years, since Guo Wang company carries out " three collection five are big " reform, each net saves and realizes metering centralization calibrating, electric energy successively
Table automatic calibration streamline popularizes in provincial measurement verification field.After electric energy meter arrival in full inspection calibrating, from
Dynamicization streamline calibrating mode, while a large amount of saving human costs, also brings many new problems.First, automatization sets
Standby degree of intelligence is limited, and serious forgiveness is low, is all likely due to equipment or the design problem of system own introduces the most true at links
Determining cause is plain and causes metering device flase drop, causes whole batch calibrating disqualification rate higher.Secondly, promote at intelligent electric energy meter
Under overall background, company of most net province pipelining equipment is in overload operation state, and equipment experience debugging, break-in, stablize etc. is transported
After row order section, a large amount of consumable accessorys and position start to occur aging, deterioration, defect even fault, and this is to system Whole verification not yet
Qualification rate creates strong influence.
Automatic assembly line the most entirely examine and determine disqualification rate higher in the case of, typically take the mode manually rechecked
To reach reasonably to examine and determine disqualification rate.But still there is inefficiency in artificial reinspection, and always takies in production scheduling
Streamline or vertical library resource, form bigger waste.
Summary of the invention
For solving the problems referred to above, the invention provides a kind of automatic calibration of electric energy meter streamline circulation pattern system reliable
Property the t method of inspection, its defective table can be taked circulate calibrating mode, be effectively improved streamline assay approval level,
Under circulation calibrating pattern, cycle-index and defective table convergence rate in the circulating cycle are also concentrated and have been reacted measurement meter with automatic
The confidence level of change equipment verification result.
The present invention solves the problems referred to above, the technical scheme used is as follows:
The lower reliability t method of inspection of a kind of streamline based on Malthus Model circulation calibrating, comprises the steps:
(1) null hypothesis H is proposed0With alternative hypothesis H1；
(2) n sample of actual acquirement, given level of significance α；
(3) sample expectation valuation is obtained；
(4) pressObtain region of rejection；
(5) result of calculation decision-making system reliability is utilized；
Described H0: r '=r2, H1: r ' ≠ r2；r2For qualification rate；N is cycle-index, 1≤n≤10；R ' is to circulate n-th
The disqualified upon inspection rate obtained；K is desired value, the desired value set the most according to actual needs.
Further, if N1Defective for sample self, N2For system flase drop, r1For disqualification rate；Then defective under circulation
Table count number theoretical value relation has: N2(n+1)-N2(n)=-[1-(r2-r1r2)]N2(n)。
Further, N2N () is the convergence model of Malthus's form, N2(0)=x (r2-r1r2), N1=r1X, then do not conform to
Lattice table is calculated as:
Further, for N2T () has:
Further, following relation meets for r ':
The present invention has the following technical effect that
Under suitable operating mode, automatic calibration streamline takes reasonably to circulate calibrating pattern can be greatly improved calibrating
Qualification rate, reduces manpower intervention cost.While circulation calibrating, based on the statistical data of backstage, it is achieved to streamline system
The real-time of system running status is analyzed and is evaluated, and quantifies health status, and carrying out O&M maintenance for wire body system provides quantization
Supportive data intuitively.The inspection of circulation calibrating down-flow water wire system reliability greatly reduces the blindness of scheduled overhaul,
Improve the effect of maintenance.
Accompanying drawing explanation
Accompanying drawing 1 is that Fig. 1 of the present invention circulates calibrating schematic diagram.
Detailed description of the invention
Below in conjunction with accompanying drawing 1 and specific embodiment, the present invention is described in further detail.
Embodiment 1:
For a measurement meter sample X to be checked, if its batch disqualification rate is r1, verification system false drop rate is r2(due to
System performance, false drop rate only considers " abandoning true ", ignores " type B error "), the defective table score produced during system calibration is two
Class, a class is that sample self is defective, is counted as N1, another kind of for system flase drop, it is counted as N2.For convenience of analyzing, if sample is the completeest
The most all it is circulated after becoming once calibrating.Cyclic process is as shown in Figure 1.
Then under circulation, defective table count number theoretical value relation has:
N2(n+1)-N2(n)=-[1-(r2-r1r2)]N2(n)
Wherein n is cycle-index, from formula, N2N () is the convergence model of Malthus's form, have again N2(0)=x
(r2-r1r2), N1=r1x.The most defective table is calculated as:
For N2T () has:This means when, after repeatedly circulation, calibrating number of non-compliances N is close to actual
Defective quantity r1x.If its circulation n time, streamline is considered as tested sample, n-th is circulated the disqualified upon inspection rate obtained
R ', then corresponding true value r of observed value r '2(for r2-r1r2, general r1Within 2%, ignore r herein1r2) deviation should be not too
Greatly.If deviation is excessive, then system is unreliable, otherwise, system is reliable.
Check problem: H0: r '=r2, H1: r ' ≠ r2。
Following relation meets for r ':
IfTime then system unreliable, for the probability of unreliable generation, we allow its maximum be α (i.e.
Significance level), then:
For sample standard deviation S, due to S2It is σ2Unbiased esti-mator, replace σ with S, can obtain region of rejection:
Embodiment 2:
Certain batch three-phase electric energy meter totally 4000, secondary circulation calibrating, it is 232 that wire body examines and determine defective electric energy meter quantity for 1 time
Only, examining and determine defective quantity for 2 times is 55, and examining and determine defective quantity for 3 times is 46, and examining and determine defective quantity for 4 times is 39, warp
Examining and determine defective quantity after 5 circulations is 32, then:
Check problem: H0: r '=32, H1: r ' ≠ 32
Take α=0.05, then the region of rejection of check problem is:
Existing n=5, tα(4)=2.1318.Calculate againS=117.24.Then have:
T does not falls within region of rejection, therefore accepts H0, it is believed that equipment is reliable.
Described above to the disclosed embodiments, makes professional and technical personnel in the field be capable of or uses the present invention.
Multiple amendment to these embodiments will be apparent from for those skilled in the art, as defined herein
General Principle can realize without departing from the spirit or scope of the present invention in other embodiments.Therefore, the present invention
It is not intended to be limited to the embodiments shown herein, and is to fit to and principles disclosed herein and features of novelty phase one
The widest scope caused.
Claims (5)
1. the lower reliability t method of inspection of streamline based on Malthus Model circulation calibrating, it is characterised in that
It comprises the steps:
(1) null hypothesis H is proposed0With alternative hypothesis H1；
(2) n sample of actual acquirement, given level of significance α；
(3) sample expectation valuation is obtained；
(4) pressObtain region of rejection；
(5) result of calculation decision-making system reliability is utilized；
Described H0: r '=r2, H1: r ' ≠ r2；r2For qualification rate；N is cycle-index, 1≤n≤10；R ' is for obtaining n-th circulation
Disqualified upon inspection rate；K is desired value.
The lower reliability t method of inspection of streamline based on Malthus Model the most according to claim 1 circulation calibrating, its
It is characterised by, if N1Defective for sample self, N2For system flase drop, r1For disqualification rate；Then defective table count number under circulation
Theoretical value relation has: N2(n+1)-N2(n)=-[1-(r2-r1r2)]N2(n)。
3. the lower reliability t method of inspection of streamline based on Malthus Model circulation calibrating stated according to claim 2, it is special
Levy and be, N2N () is the convergence model of Malthus's form, N2(0)=x (r2-r1r2), N1=r1X, the most defective table is calculated as:
The lower reliability t method of inspection of streamline based on Malthus Model the most according to claim 3 circulation calibrating, its feature
It is, for N2T () has:
The lower reliability t method of inspection of streamline based on Malthus Model the most according to claim 3 circulation calibrating, its feature
It is, following relation meets for r ':
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| CN201610578454.6A CN106249190B (en) | 2016-07-21 | 2016-07-21 | Method for testing reliability t under flow line circulation detection based on Markaus model |
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| CN201610578454.6A CN106249190B (en) | 2016-07-21 | 2016-07-21 | Method for testing reliability t under flow line circulation detection based on Markaus model |
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Citations (5)
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|---|---|---|---|---|
| US20030171897A1 (en) * | 2002-02-28 | 2003-09-11 | John Bieda | Product performance integrated database apparatus and method |
| CN104101857A (en) * | 2014-07-01 | 2014-10-15 | 杭州电子科技大学 | FlexRay bus-based electric energy meter error detection system and method |
| CN104183110A (en) * | 2014-09-05 | 2014-12-03 | 国家电网公司 | Automatic production line device checking system applied to acquisition terminal and automatic production line device checking method applied to acquisition terminal |
| CN104765949A (en) * | 2015-03-05 | 2015-07-08 | 国家电网公司 | Maintenance method for electric energy meter automatic verification assembly line equipment |
| CN105510866A (en) * | 2015-11-27 | 2016-04-20 | 江苏省电力公司电力科学研究院 | Fault monitoring method of electric energy meter automatic detection line |
-
2016
- 2016-07-21 CN CN201610578454.6A patent/CN106249190B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20030171897A1 (en) * | 2002-02-28 | 2003-09-11 | John Bieda | Product performance integrated database apparatus and method |
| CN104101857A (en) * | 2014-07-01 | 2014-10-15 | 杭州电子科技大学 | FlexRay bus-based electric energy meter error detection system and method |
| CN104183110A (en) * | 2014-09-05 | 2014-12-03 | 国家电网公司 | Automatic production line device checking system applied to acquisition terminal and automatic production line device checking method applied to acquisition terminal |
| CN104765949A (en) * | 2015-03-05 | 2015-07-08 | 国家电网公司 | Maintenance method for electric energy meter automatic verification assembly line equipment |
| CN105510866A (en) * | 2015-11-27 | 2016-04-20 | 江苏省电力公司电力科学研究院 | Fault monitoring method of electric energy meter automatic detection line |
Non-Patent Citations (1)
| Title |
|---|
| 刘后平: "《统计学》", 31 March 2015, 东北财经大学出版社 * |
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Address after: 050021 No. 238 South Sports street, Yuhua District, Hebei, Shijiazhuang Patentee after: STATE GRID HEBEI ELECTRIC POWER Research Institute Patentee after: STATE GRID CORPORATION OF CHINA Patentee after: STATE GRID HEBEI ENERGY TECHNOLOGY SERVICE Co.,Ltd. Address before: 050021 No. 238 South Sports street, Yuhua District, Hebei, Shijiazhuang Patentee before: ELECTRIC POWER SCIENCES RESEARCH INSTITUTE OF STATE GRID HEBEI ELECTRIC POWER Co. Patentee before: State Grid Corporation of China Patentee before: HEBEI ELECTRIC POWER COMMISSIONING INSTITUDE |
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Effective date of registration: 20220523 Address after: 050021 No. 238 South Sports street, Hebei, Shijiazhuang Patentee after: STATE GRID HEBEI ELECTRIC POWER Research Institute Patentee after: Marketing service center of State Grid Hebei Electric Power Co.,Ltd. Patentee after: STATE GRID CORPORATION OF CHINA Patentee after: STATE GRID HEBEI ENERGY TECHNOLOGY SERVICE Co.,Ltd. Address before: 050021 No. 238 South Sports street, Yuhua District, Hebei, Shijiazhuang Patentee before: STATE GRID HEBEI ELECTRIC POWER Research Institute Patentee before: STATE GRID CORPORATION OF CHINA Patentee before: STATE GRID HEBEI ENERGY TECHNOLOGY SERVICE Co.,Ltd. |