CN104808154A - Street lamp fault type determining method based on fuzzy set theory - Google Patents
Street lamp fault type determining method based on fuzzy set theory Download PDFInfo
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Abstract
The invention discloses a street lamp fault type determining method based on the fuzzy set theory, and the method can effectively improve the basis for determining faults of street lamps and greatly increase the determining and report accuracy. Different electrical parameters of lamps (including normal lamps and lamps with faults) are combined on the basis of the fuzzy set theory to automatically determine different faults, and the algorithm emphasizes self correction and autonomous learning.
Description
Technical field
The present invention relates to the judgement of street lamp fault type, particularly relating to can the method for constantly self-teaching sophisticated model and automatic decision fault type.
Background technology
Street lamp fault is the phenomenon that must occur in street lamp actual motion.Along with urban sprawl and urban construction development, street lamp quantity gets more and more, and causes street lamp fault quantity and type also to grow with each passing day.Fault street lamp how is found to be one of groundwork of road lamp management department fast and accurately.Nearest single lamp control system is progressively in the field of business to occupy a tiny space, and this is just for the analysis list lamp failure of scientific system provides strong support.
Assuming that we obtain the data that single lamp runs by single lamp monitoring system, such as electric current I, voltage U, active-power P A, power factor PF etc., then we can by comprehensive data analysis (comprising normal street lamp service data, abnormal street lamp service data etc.), provide automatic analysis and the differentiation of each street lamp running status, thus can be more quick, directly and more scientific and reasonable judge this street lamp whether normal operation, or be under the jurisdiction of any fault.Typical fault known at present has lamp stand bad etc. without electricity (actual non-street lamp faults itself, but because lamp can be caused not work so also belong to street lamp fault), capacitance damage, jumping lamp (bad during street lamp fashion), street lamp serious aging and street lamp total loss.
But in the judgement of street lamp fault, still there is following problem:
1, all kinds of street lamp fault is of a great variety, and long-range and on-the-spot judgement all lacks standard and foundation accurately;
2, street lamp fault judging nicety rate is not high, causes wrong report and the ratio accounting of failing to report comparatively large, causes the decline of road lighting service level while of making road lamp tube reason work efficiency not high yet.
Summary of the invention
Below provide the brief overview of one or more aspect to provide the basic comprehension to these aspects.Detailed the combining of this not all aspect contemplated of general introduction is look at, and both not intended to be pointed out out the scope of key or decisive any or all aspect of elements nor delineate of all aspects.Its unique object is the sequence that some concepts that will provide one or more aspect in simplified form think the more detailed description provided after a while.
The object of the invention is to solve the problem, provide a kind of street lamp fault type judgement method based on Fuzzy Set Theory, effectively can improve that street lamp fault judges according to and quite the raising of amplitude judge and report accuracy rate.
Technical scheme of the present invention is: present invention is disclosed a kind of street lamp fault type judgement method based on Fuzzy Set Theory, comprising:
According to the actual operating data of normal street lamp and fault street lamp, provide the model parameter of different faults type respectively, and construct the membership function of each component data according to actual operating data and model parameter, obtain initial degree of membership model;
Single lamp data that each introducing is new, and judge based on up-to-date degree of membership model, and for being judged to be that the street lamp of fault carries out field verification;
Find correct if verify, then proceed the judgement of single lamp data next time, electrical quantity certificate in current judicious fault example is joined in corresponding fault type database simultaneously, in time being accumulated to some, again revising the model parameter of this fault type;
Find mistake if verify, then carry out manual analysis and sort out, in time being accumulated to some, again revising the model parameter of this fault type;
After verification, obtain revised model parameter, then continue the judgement of next single lamp data.
According to an embodiment of the street lamp fault type judgement method based on Fuzzy Set Theory of the present invention, verifying in the process finding mistake, if still inoperative to the correction of model parameter, then revise degree of membership model itself further.
According to an embodiment of the street lamp fault type judgement method based on Fuzzy Set Theory of the present invention, model parameter comprises typical expectation value, standard deviation.
According to an embodiment of the street lamp fault type judgement method based on Fuzzy Set Theory of the present invention, component data comprises current data, voltage data.
According to an embodiment of the street lamp fault type judgement method based on Fuzzy Set Theory of the present invention, find in wrong step in verification, if if newly-increased being verified is wrong data on-site verification lamp non-fault, then retest electrical quantity certificate, and be included in non-faulting lamp database, if be verified as other faults, then electrical quantity certificate is joined in corresponding fault type database.
According to an embodiment of the street lamp fault type judgement method based on Fuzzy Set Theory of the present invention, membership function be based on single lamp data of normal lamp and single lamp data one of trouble light syntectonic.
According to an embodiment of the street lamp fault type judgement method based on Fuzzy Set Theory of the present invention, when there is newly-increased fault type, or need discrete with when merging fault type, reclassify new fault type database, to re-construct new degree of membership model and parameter.
The present invention contrasts prior art following beneficial effect: the solution of the present invention is based on Fuzzy Set Theory and runs the multi-class fault automatic decision of multiple electrical quantity according to (comprising normal lamp and trouble light) feature in conjunction with a large amount of single lamps, and algorithm focuses on self-recision and autonomous learning simultaneously.Compared to conventional art, the present invention is based on the brand-new Fuzzy Set Theory that street lamp runs (greatly) data, effectively can improve the foundation of the breakdown judge such as road and the raising judgement of suitable amplitude and report accuracy rate.Along with road lamp system is progressively generalized to single lamp, system operation data is in swift and violent increase, and this just judges to bring new dependence and guarantee for further improving street lamp fault from now on.Through practice test, in the street lamp operational system of more than 10000 managed more than more than 300 street lamp case on 100 roads, the breakdown judge accuracy rate of algorithm of the present invention from before (without during this algorithm only with test and judge) 80% rise to current more than 95%.Meanwhile, constantly newly-increased single lamp service data is that the reasonable correction of this algorithm also brings new possibility.In actual correction process, we find, data are more, Model suitability of the present invention and the accuracy made a decision according to model and reliability also more guaranteed.
Accompanying drawing explanation
Fig. 1 shows the process flow diagram of the preferred embodiment of the street lamp fault type judgement method based on Fuzzy Set Theory of the present invention.
Embodiment
After the detailed description of reading embodiment of the present disclosure in conjunction with the following drawings, above-mentioned feature and advantage of the present invention can be understood better.In the accompanying drawings, each assembly is not necessarily drawn in proportion, and the assembly with similar correlation properties or feature may have identical or close Reference numeral.
Fig. 1 shows the flow process of the preferred embodiment of the street lamp fault type judgement method based on Fuzzy Set Theory of the present invention.Refer to Fig. 1, the detailed step of the method for the present embodiment is as follows.
Step S1: according to the actual operating data of normal street lamp and fault street lamp, provide the model parameter of different faults type respectively, and construct the membership function of each component data according to actual operating data and model parameter, obtain initial degree of membership model.
Model parameter mentioned here comprises the typical expectation value (large statistical average) of the street lamp electrical quantity certificate of a certain classification fault and the standard deviation standard deviation of n σ (under the class normal distribution).And component data refer to the data such as electric current, voltage of street lamp.
The structure of membership function is mainly based on experience and a large amount of single lamp data, and the data that wherein most fundamental sum is maximum are normal lamps.In reality, normal lamp and trouble light are separate repulsion in logic, so must consider single lamp database of normal lamp in the structure of membership function, the model of trouble light like this and parameter could be reasonable in design and can conveniently judge again the while of keeping stable in practice.Membership function form in this method can be various, just gives the simplest form based on Fuzzy Set Theory here.The form or different of concrete various lamps, also can constantly revise according to more single lamp data and change.And when occurring newly-increased fault type, or discrete with when merging fault type according to user's request.Need to reclassify new database, to re-construct new degree of membership model and parameter.
In this step, various situations during a large amount of for reality street lamp long-play are added analytical algorithm by the present embodiment.And judge in system or software at traditional street lamp fault, even if automatic decision, but all rely on the empirical value of so-called fixed single, instead of the street lamp service data of reality, saying nothing of is the data of large data and change.
Below lift an example and carry out description of step S1.
1) first according to enriching street lamp data and the simplest membership function of experience foundation in the industry in the past
μ Aij (x)=0, when x<Xij-Aij or x>Xij+Aij (formula one);
μ Aij (x)=1 – (x-Xij) * (x-Xij)/(Aij*Aij), when Xij-Aij≤x≤Xij+Aij (formula two),
Wherein the actual measurement of certain lamp x or electrical quantity to be measured (can be I, U, any one of PA and PF etc.), i represents that lamp condition (has normal, serious aging, without electricity under bar, capacitance damage, lamp damage etc.), j representation feature electricity ginseng (such as I, U, PA and PF etc.), the typical expectation value (large number is average) of Xij and Aij a certain class electricity ginseng j under being respectively and referring to a certain lamp condition i and standard deviation (here for the sake of simplicity, when initial membership function constructs, choose the normal distribution model of most standard, and with single standard deviation doubly for basic screening scope)
2) Xij and the Aij table obtained according to a large amount of street lamp Data induction statistics, shows with the high-pressure mercury lamp of the 250w of Philips model when 220V single-phase ac power supply runs, is exemplified below
3) according to above-mentioned canonical parameter table, the ownership situation now analyzing the single lamp data of judgement 2 is as follows:
Lamp 1:I=1.4, U=223, PA=260, PF=0.83, we can obtain:
The degree of membership of normal state is:
μ A1 (x)=Λ { μ A11 (1.4), μ A12 (220), μ A13 (250), μ A14 (0.83) }=Λ { 1.0,0.91,0.80,0.84}=0.80, wherein Λ algorithm symbol is the fuzzy common factor of each set element of μ Aij (x), and μ Aij (x) just calculates from formula () and (two);
The degree of membership of serious aging is:
μA2(x)＝Λ{μA21(1.4)，μA22(220)，μA23(250)，μA24(0.83)}＝Λ{0.8,0.91,0,0}＝0
For under bar without the degree of membership of electricity be:
μA3(x)＝Λ{μA31(1.4)，μA32(220)，μA33(250)，μA34(0.83)}＝Λ{0,0.91,0,0}＝0
The degree of membership bad for electric capacity is:
μA4(x)＝Λ{μA41(1.4)，μA42(220)，μA43(250)，μA44(0.83)}＝Λ{0,0.91,0.95,0}＝0
The degree of membership damaged for lamp is:
μA5(x)＝Λ{μA21(1.4)，μA52(220)，μA53(250)，μA54(0.83)}＝Λ{0,0,0,0}＝0。
Comprehensive Evaluation, lamp 1 belongs to normal condition (noting powers due to outside electronic box cannot ensure all the time at 220V, so the data of actual measurement street lamp need first to be corrected to 220V standard input).
Lamp 2:I=0, U=225, PA=0, PF=0, we can obtain:
The degree of membership of normal state is:
μA1(x)＝Λ{μA11(0)，μA12(225)，μA13(0)，μA14(0)}＝Λ{0,0.75,0,0}＝0，
Degree of membership for serious aging is:
μA2(x)＝Λ{μA21(0)，μA22(225)，μA23(0)，μA24(0)}＝Λ{0,0.75,0,0}＝0
For under bar without the degree of membership of electricity be:
μA3(x)＝Λ{μA31(0)，μA32(225)，μA33(0)，μA34(0)}＝Λ{0.75,0.75,1,1}＝0.75
The degree of membership bad for electric capacity is:
μA4(x)＝Λ{μA41(0)，μA42(225)，μA43(0)，μA44(0)}＝Λ{0,0.75,0,0}＝0
The degree of membership damaged for lamp is:
μA5(x)＝Λ{μA21(0)，μA52(225)，μA53(0)，μA54(0)}＝Λ{0.75,0,1,1}＝0
Comprehensive Evaluation, lamp 2 belongs to electroless state under bar at present
So far, we are by above-mentioned standard scale and each single lamp measured data, give the condition adjudgement of 2 lamps according to algorithm.
Step S2: single lamp data that each introducing is new, and judge based on up-to-date degree of membership model, and for being judged to be that the street lamp of fault carries out field verification.Find if verify correct, enter step S3, find mistake if verify, enter step S4.
Here field verification refers to: artificial (whether having electricity under inspection bar) and cover of the turning on light inspection light fixture of starting at the scene.By again to the powering on of lamp, check electric capacity, whether on-site verification jumps lamp and by examination criteria illumination, can differentiate to wait whether there is fault very accurately, and the respective classification of fault.
Step S3: find correct if verify, then proceed the judgement of single lamp data next time, electrical quantity certificate in current judicious fault example is joined in corresponding fault type database simultaneously, in time being accumulated to some, again revising the model parameter of this fault type.
Such as, for each fault type determined, the data of database are increased respectively.When a certain fault type increases newly when number reaches 10% of former the type number of faults, (such as former capacitive faults radix is 50, then newly-increased when determining that electric smelting fault is 5, then newly-increased 5 are together joined in 50 original data, and because these 5 is the up-to-date authentic data examined, so weight can suitably improve), start according to master mould parametric configuration mode, calculate new model parameter, the model basis that the new data as next one judges.In addition, the normal judgement of normal lamp also can sort out the treatment principle into this step.
When a kind of new fault type, especially when not sure fault type (such as light fixture is aging) is introduced, should be noted that and do not invade other known fault (and normal lamp type) scopes with determining type, not so not only cannot confirm that this new fault increases class, and also can cause validity and the stability step of the judgement of original fault type or even whole method frame.
S4: find mistake if verify, then carry out manual analysis and sort out, again revise the model parameter of this fault type in time being accumulated to some.
For false judgment, also the newly-increased new data through verifying to be joined in legacy data storehouse.But it should be noted that, due to newly-increased be false judgment data, and the reliability of original database (being also such as 50) will be given a discount, and (scope being processed into degree of membership here broadens, the simplest corresponding mode is exactly the multiple reducing such fault type standard deviation), if newly-increased is that the data of mistake are by on-site verification lamp non-fault by verifying simultaneously, then need electrical quantity certificate of resurveying, then these data need to enrich original non-faulting lamp database; If be verified as other faults, then the process of similar step S3, joined in corresponding fault type database.
Under extreme situation, a large amount of wrong reports (on-site verification), constantly reducing of degree of membership scope can be caused finally to cause determining this fault type, so just need according to the data verified (must be accumulated to certain quantity), again, as the incipient stage, brand-new model and correlation parameter thereof is constructed.
Under extreme situation, the significantly change of above-mentioned degree of membership model or re-construct, may cause new instability.Now need the judgement work first temporarily abandoned the type, safe method gets back to beginning, the True Data of this fault type accumulative, again according to rule after certain phase, re-construct degree of membership model and the design parameter thereof of this fault type, then come again to carry out actual judgement classification work to single lamp new data.
Each fault accumulative (no matter examining as correct or wrong report) (such as increases newly 10 times to certain quantity, or newly-increased ratio reaches 10%), all need separately parameter to be carried out to this kind of fault type and again to revise and even model re-constructs (the extreme situation situation in such as step S4).
As can be seen from step S2 to S4, parameter and model are all up-to-date failure condition according to actual feedback situation and newly-increased street lamp and constantly revise, the accuracy rate judged with the breakdown judge and fault type that provide more fit actual basis for estimation and Geng Gao.The existing model of this algorithm (such as degree of membership model) and correlation parameter are all obtain on the street lamp service data basis obtained at present, but along with a large amount of acquisitions of various street lamp single-lamp service data, model parameter can self-recision automatically, so just constantly can improve accuracy and the adaptability of this algorithm in practice.
In addition, the present embodiment by the algorithm of automatic decision and the mechanism of continuous self-teaching, the capability and qualification of real road illumination supervision is greatly improved.Can find out in the present embodiment, algorithm is full automatic, is based upon a certain amount of street lamp service data as long as provide, and can automatically provide empirical parameter and model parameter according to algorithm.Then according to these model and parameters, follow-up single lamp data are given and judgement automatically.The part of manual intervention is uniquely needed to be, after algorithm automatic decision, need manual site to verify.Verify accurately, just strengthen the Rational Parameters (multiple such as increasing standard deviation defines fault more accurately and do not have the boundary between fault, and the boundary between the generally aging and serious aging of lamp) of existing model.If find that mistake reaches certain ratio upper limit through verifying, just again estimate parameter (such as reducing standard deviation multiple to realize the qualitative accuracy in guestimate).In a word, this calculation can more reasonably provide parameter and Modifying model according to follow-up on-site verification and adding of a large amount of new data.The accuracy that so just can really judge for street lamp fault and reliability provide solid foundation to ensure, thus street lamp operation maintenance management department brings real help and benefit.
Step S5: obtain revised model parameter after verification, then continue the judgement of next single lamp data.
After the process of above-mentioned step S3 and S4, return step S2, receive new data detection, so the above-mentioned associated steps of circulation, also do not have new verification to find until do not have new data to enter simultaneously.
In the present embodiment, what model was corresponding is Fuzzy Set Theory, and Fuzzy Set Theory effectively describes uncertainty and the complicacy of street lamp fault.Data exception corresponding to phenomenon of the failure is not unique, and each fault and electrical quantity according to abnormal be not event separate on statistical significance, so uncertainty must be introduced, these phenomenons and the true association degree between the imagination and a large amount of measured data could be described more accurately.And Fuzzy Set Theory can conclude, defines and analyze the complicacy that these uncertain one-levels are brought thus just.
Thering is provided previous description of the present disclosure is for making any person skilled in the art all can make or use the disclosure.To be all apparent for a person skilled in the art to various amendment of the present disclosure, and generic principles as defined herein can be applied to other variants and can not depart from spirit or scope of the present disclosure.Thus, the disclosure not intended to be is defined to example described herein and design, but the widest scope consistent with principle disclosed herein and novel features should be awarded.
Claims (7)
1., based on a street lamp fault type judgement method for Fuzzy Set Theory, comprising:
According to the actual operating data of normal street lamp and fault street lamp, provide the model parameter of different faults type respectively, and construct the membership function of each component data according to actual operating data and model parameter, obtain initial degree of membership model;
Single lamp data that each introducing is new, and judge based on up-to-date degree of membership model, and for being judged to be that the street lamp of fault carries out field verification;
Find correct if verify, then proceed the judgement of single lamp data next time, electrical quantity certificate in current judicious fault example is joined in corresponding fault type database simultaneously, in time being accumulated to some, again revising the model parameter of this fault type;
Find mistake if verify, then carry out manual analysis and sort out, in time being accumulated to some, again revising the model parameter of this fault type;
After verification, obtain revised model parameter, then continue the judgement of next single lamp data.
2. the street lamp fault type judgement method based on Fuzzy Set Theory according to claim 1, is characterized in that, verifying in the process finding mistake, if still inoperative to the correction of model parameter, then revises degree of membership model itself further.
3. the street lamp fault type judgement method based on Fuzzy Set Theory according to claim 1, it is characterized in that, model parameter comprises typical expectation value, standard deviation.
4. the street lamp fault type judgement method based on Fuzzy Set Theory according to claim 1, it is characterized in that, component data comprises current data, voltage data.
5. the street lamp fault type judgement method based on Fuzzy Set Theory according to claim 1, it is characterized in that, find in wrong step in verification, if if newly-increased being verified is wrong data on-site verification lamp non-fault, then retest electrical quantity certificate, and be included in non-faulting lamp database, if be verified as other faults, then electrical quantity certificate is joined in corresponding fault type database.
6. the street lamp fault type judgement method based on Fuzzy Set Theory according to claim 1, is characterized in that, membership function be based on single lamp data of normal lamp and single lamp data one of trouble light syntectonic.
7. the street lamp fault type judgement method based on Fuzzy Set Theory according to claim 1, it is characterized in that, when there is newly-increased fault type, or need discrete with when merging fault type, reclassify new fault type database, to re-construct new degree of membership model and parameter.
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| CN110187294A (en) * | 2019-04-17 | 2019-08-30 | 安徽建筑大学 | Method for diagnosing faults and device towards piecewise linearity constant-current driving LED light source |
| CN111796200A (en) * | 2020-09-08 | 2020-10-20 | 杭州罗莱迪思科技股份有限公司 | AI algorithm for automatically identifying lamp fault based on current characteristic fingerprint curve |
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