CN102982241B - The detection method of illuminator chance of failure and device - Google Patents
The detection method of illuminator chance of failure and device Download PDFInfo
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- CN102982241B CN102982241B CN201210492618.5A CN201210492618A CN102982241B CN 102982241 B CN102982241 B CN 102982241B CN 201210492618 A CN201210492618 A CN 201210492618A CN 102982241 B CN102982241 B CN 102982241B
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Abstract
The invention discloses a kind of detection method and device of illuminator chance of failure, the detection method of this illuminator chance of failure comprises: in acquisition illuminator, multiple subsystem is in the chance of failure of first time period; According to multiple subsystem in the chance of failure determination illuminator of first time period in the chance of failure of first time period; According to subsystem multiple in illuminator in the chance of failure determination illuminator of first time period multiple subsystem in the chance of failure of the second time period; According to multiple subsystem in the chance of failure determination illuminator of the second time period in the chance of failure of the second time period; And according to illuminator in the chance of failure of first time period and the illuminator chance of failure in the chance of failure determination illuminator of the second time period.By the present invention, the performance of illuminator can be understood more accurately.
Description
Technical field
The present invention relates to photoelectric field, in particular to a kind of detection method and device of illuminator chance of failure.
Background technology
In order to estimate the reliability of light fixture, need the chance of failure detecting illuminator, in the prior art, generally that the LED of illuminator is detected, think LED lost efficacy then illuminator lost efficacy, the chance of failure by means of only LED judges that the chance of failure of illuminator is inaccurate.
For the problem that accurately cannot detect the chance of failure of illuminator in prior art, at present effective solution is not yet proposed.
Summary of the invention
The invention provides a kind of detection method and device of illuminator chance of failure, at least to solve the problem that accurately cannot detect the chance of failure of illuminator.
To achieve these goals, according to an aspect of the present invention, a kind of detection method of illuminator chance of failure is provided.
Detection method according to illuminator chance of failure of the present invention comprises: in acquisition illuminator, multiple subsystem is in the chance of failure of first time period; According to multiple subsystem in the chance of failure determination illuminator of first time period in the chance of failure of first time period; According to subsystem multiple in illuminator, in the chance of failure determination illuminator of first time period, multiple subsystem is in the chance of failure of the second time period, and wherein, the initial time of the second time period is the end time of first time period; According to multiple subsystem in the chance of failure determination illuminator of the second time period in the chance of failure of the second time period; And according to illuminator in the chance of failure of first time period and the illuminator chance of failure in the chance of failure determination illuminator of the second time period.
Further, comprise in the chance of failure of first time period according to the chance of failure determination illuminator of multiple subsystem in first time period: obtain Bayesian network model; The chance of failure of multiple subsystem in first time period is sent to Bayesian network model; Obtain the illuminator determined by the Bayesian network model chance of failure in first time period.
Further, after the chance of failure determining illuminator, said method also comprises: determine the subsystem lost efficacy, wherein, comprise the corresponding relation between the chance of failure of illuminator and the subsystem of inefficacy at Bayesian network model.
Further, comprise in the chance of failure of the second time period according to subsystem multiple in illuminator multiple subsystem in the chance of failure determination illuminator of first time period: obtain Markov-chain model; The chance of failure of multiple subsystem in first time period is sent to Markov-chain model; In the illuminator that acquisition is determined by Markov-chain model, multiple subsystem is in the chance of failure of the second time period.
Further, obtain multiple subsystem in illuminator to comprise in the chance of failure of first time period: to obtain in the illuminator after fault tree model abbreviation multiple subsystem in the chance of failure of first time period.
Further, obtain Bayesian network model and comprise: obtain the Bayesian network model from internet, obtain Markov-chain model and comprise: obtain the Markov-chain model from internet.
To achieve these goals, according to another aspect of the present invention, provide a kind of pick-up unit of illuminator chance of failure, this device is for performing the detection method of any one illuminator chance of failure provided by the invention.
According to a further aspect in the invention, a kind of pick-up unit of illuminator chance of failure is provided.The pick-up unit of this illuminator chance of failure comprises: acquiring unit, for obtaining in illuminator multiple subsystem in the chance of failure of first time period; First determining unit, for according to multiple subsystem in the chance of failure determination illuminator of first time period in the chance of failure of first time period; Second determining unit, for according to subsystem multiple in illuminator, in the chance of failure determination illuminator of first time period, multiple subsystem is in the chance of failure of the second time period, wherein, the initial time of the second time period is the end time of first time period; 3rd determining unit, for according to multiple subsystem in the chance of failure determination illuminator of the second time period in the chance of failure of the second time period; And the 4th determining unit, for according to illuminator in the chance of failure of first time period and the illuminator chance of failure in the chance of failure determination illuminator of the second time period.
Further, the first determining unit comprises: first obtains subelement, for obtaining Bayesian network model; First sends subelement, for sending the chance of failure of multiple subsystem in first time period to Bayesian network model; Second obtains subelement, for obtaining the illuminator determined by the Bayesian network model chance of failure in first time period.
Further, said apparatus also comprises: the 5th determining unit, for determining the subsystem lost efficacy, wherein, comprises the corresponding relation between the chance of failure of illuminator and the subsystem of inefficacy at Bayesian network model.
Further, the second determining unit comprises: the 3rd obtains subelement, for obtaining Markov-chain model; Second sends subelement, for sending the chance of failure of multiple subsystem in first time period to Markov-chain model; 4th obtains subelement, for obtaining in the illuminator determined by Markov-chain model multiple subsystem in the chance of failure of the second time period.
Further, acquiring unit is also for obtaining in the illuminator after fault tree model abbreviation multiple subsystem in the chance of failure of first time period.
Further, first obtains subelement also for obtaining the Bayesian network model from internet, and the 3rd obtains subelement also for obtaining the Markov-chain model from internet.
Pass through the present invention, owing to adopting the chance of failure detecting each subsystem of illuminator, and by the chance of failure of each subsystem, the chance of failure of whole illuminator in each time period is dynamically determined, thus accurately record the chance of failure of illuminator, therefore solve the problem that accurately cannot detect the chance of failure of illuminator in prior art, and then understand the performance of illuminator more accurately.
Accompanying drawing explanation
The accompanying drawing forming a application's part is used to provide a further understanding of the present invention, and schematic description and description of the present invention, for explaining the present invention, does not form inappropriate limitation of the present invention.In the accompanying drawings:
Fig. 1 is the structured flowchart of the pick-up unit of illuminator chance of failure according to the embodiment of the present invention;
Fig. 2 is the process flow diagram of the detection method of illuminator chance of failure according to the embodiment of the present invention;
Fig. 3 is the Bayesian network model schematic diagram according to the embodiment of the present invention;
Fig. 4 a is the schematic diagram of LED catastrophic failure in Markov chain model according to the embodiment of the present invention;
Fig. 4 b is lower than 70% schematic diagram in Markov chain model according to the LED lumen of the embodiment of the present invention;
Fig. 4 c is the schematic diagram of solder joint failure in Markov chain model according to the embodiment of the present invention;
Fig. 4 d is the schematic diagram of electrochemical capacitor inefficacy in Markov chain model according to the embodiment of the present invention; And
Fig. 4 e is the schematic diagram of illuminator inefficacy in Markov chain model according to the embodiment of the present invention.
Embodiment
It should be noted that, when not conflicting, the embodiment in the application and the feature in embodiment can combine mutually.Below with reference to the accompanying drawings and describe the present invention in detail in conjunction with the embodiments.
Embodiments provide a kind of pick-up unit of illuminator chance of failure, below the pick-up unit of the illuminator chance of failure that the embodiment of the present invention provides is introduced.
Fig. 1 is the structured flowchart of the pick-up unit of illuminator chance of failure according to the embodiment of the present invention.
As shown in Figure 1, the pick-up unit of this illuminator chance of failure comprises acquiring unit 11, first determining unit 12, second determining unit 13, the 3rd determining unit 14 and the 4th determining unit 15.
Acquiring unit 11 is for obtaining in illuminator multiple subsystem in the chance of failure of first time period.
First determining unit 12 for according to multiple subsystem in the chance of failure determination illuminator of first time period in the chance of failure of first time period.
First determining unit 12 can by the chance of failure of number of ways determination illuminator in first time period, in the present embodiment, illuminator is determined by Bayesian network model in the chance of failure of first time period, preferably, first determining unit comprises the first acquisition subelement, first and sends subelement and the second acquisition subelement, wherein, first subelement is obtained for obtaining Bayesian network model; First sends subelement is used for sending the chance of failure of multiple subsystem in first time period to Bayesian network model; Second obtains subelement, for obtaining the illuminator determined by the Bayesian network model chance of failure in first time period.Bayesian network model comprises the relation of whole and part, by above-mentioned each subelement, can determine the chance of failure in this time period of this illuminator entirety according to the subsystem of illuminator.
Second determining unit 13 for according to subsystem multiple in illuminator in the chance of failure determination illuminator of first time period multiple subsystem in the chance of failure of the second time period, wherein, the initial time of the second time period is the end time of first time period.
Second determining unit 13 can determine its chance of failure at subsequent time period according to a certain subsystem in the chance of failure of a upper time period by number of ways, in the present embodiment, the chance of failure of subsequent time period is determined by Markov chain model, preferably, second determining unit 13 can comprise the 3rd and obtain subelement, the second transmission subelement and the 4th acquisition subelement, wherein, the 3rd subelement is obtained for obtaining Markov-chain model; Second sends subelement is used for sending the chance of failure of multiple subsystem in first time period to Markov-chain model; 4th obtains subelement, for obtaining in the illuminator determined by Markov-chain model multiple subsystem in the chance of failure of the second time period.The dynamic relationship of each chance of failure and time can be defined by Markov chain model, therefore, by above each subelement, its chance of failure in follow-up each time period can be determined according to the chance of failure of the subsystem obtained in first time period.
3rd determining unit 14 for according to multiple subsystem in the chance of failure determination illuminator of the second time period in the chance of failure of the second time period.
The chance of failure of subsystem each in this time period, in the first determining unit 12, is put into Bayesian network model, can be determined the chance of failure in this time period illuminator by the similar of the 3rd determining unit 14.
4th determining unit 15 for according to illuminator in the chance of failure of first time period and the illuminator chance of failure in the chance of failure determination illuminator of the second time period.
Just the chance of failure of illuminator in each time period intactly can be determined in the chance of failure of each time period by matching illuminator.
In the present embodiment, owing to adopting the chance of failure detecting each subsystem of illuminator, and by the chance of failure of each subsystem, the chance of failure of whole illuminator in each time period is dynamically determined, thus accurately record the chance of failure of illuminator, therefore solve the problem that accurately cannot detect the chance of failure of illuminator in prior art, and then understand the performance of illuminator more accurately.
Because Bayesian network model comprises each subsystem of illuminator and this holistic relation of illuminator, therefore, both can by the chance of failure of the chance of failure determination illuminator of each subsystem, again can when getting the chance of failure of illuminator, determine the system of current most possible inefficacy, preferably, said apparatus also comprises the 5th determining unit, 5th determining unit is for determining the subsystem lost efficacy, wherein, the corresponding relation between the chance of failure of illuminator and the subsystem of inefficacy is comprised at Bayesian network model.
Due to Bayesian network model and Markov chain model more complicated, for the ease of the process of data, first can carry out abbreviation by fault tree model to the chance of failure of first time period, chance of failure after abbreviation is sent in Bayesian network model and Markov chain model, greatly reduce required calculated amount, preferably, acquiring unit is also for obtaining in the illuminator after fault tree model abbreviation multiple subsystem in the chance of failure of first time period.
In the present embodiment, data how new in each mathematical model or algorithm is obtained in order to enable different users, this device can be connected with high in the clouds data bank, preferably, first obtains subelement also for obtaining the Bayesian network model from internet, and the 3rd obtains subelement also for obtaining the Markov-chain model from internet.
The embodiment of the present invention additionally provides a kind of detection method of illuminator chance of failure, and the method can perform based on above-mentioned device.
Fig. 2 is the process flow diagram of the detection method of illuminator chance of failure according to the embodiment of the present invention.
As shown in Figure 2, the detection method of this illuminator chance of failure comprises following step S202 to step S210.
Step S202, in acquisition illuminator, multiple subsystem is in the chance of failure of first time period.
First time period is from initial time, and the chance of failure of this time period each subsystem obtains by consulting the data such as corresponding product instructions.After starting to detect, chance of failure can change.
Step S204, according to multiple subsystem in the chance of failure determination illuminator of first time period in the chance of failure of first time period.
In this step, can by the chance of failure of number of ways determination illuminator in first time period, in the present embodiment, illuminator is determined by Bayesian network model in the chance of failure of first time period, and preferably, this step can specifically be divided into step S1 to step S3.
S1, obtains Bayesian network model (BNs).
Bayesian network is a directed acyclic graph (DirectedAcyclicGraph, referred to as DAG), forms by representing variable node and connecting these node directed edges.Node on behalf stochastic variable, internodal directed edge represents internodal cross correlation (pointing to its child node by father node), carries out relationship between expression intensity by conditional probability, and what do not have father node carries out information representation with prior probability.Node variable can be the abstract of any problem, as: test value, observation phenomenon, suggestion is seeked the opinion of.
By Bayesian network, we can infer other possibility variablees still unobservable according to known variable, and the impact of Bayesian network may be probability or determinacy.
Fig. 3 is the Bayesian network model schematic diagram according to the embodiment of the present invention.As shown in Figure 3, the chance of failure of 4 subsystems enumerated by Fig. 3, can draw the chance of failure of whole illuminator.
S2, first sends subelement is used for sending the chance of failure of multiple subsystem in first time period to Bayesian network model.
S3, second obtains subelement, for obtaining the illuminator determined by the Bayesian network model chance of failure in first time period.Bayesian network model comprises the relation of whole and part, by above-mentioned each subelement, can determine the chance of failure in this time period of this illuminator entirety according to the subsystem of illuminator.
Step S206, according to subsystem multiple in illuminator, in the chance of failure determination illuminator of first time period, multiple subsystem is in the chance of failure of the second time period, and wherein, the initial time of the second time period is the end time of first time period.
All can realize determining its chance of failure at subsequent time period according to a certain subsystem in the chance of failure of a upper time period by number of ways, in the present embodiment, the chance of failure of subsequent time period is determined by Markov chain model (MarkovChain), particularly, first, Markov-chain model can be obtained; Then, the chance of failure of multiple subsystem in first time period is sent to Markov-chain model; Finally, multiple subsystem is obtained in the illuminator determined by Markov-chain model in the chance of failure of the second time period.The dynamic relationship of each chance of failure and time can be defined by Markov chain model, therefore, by above each subelement, its chance of failure in follow-up each time period can be determined according to the chance of failure of the subsystem obtained in first time period.
Markov chain is a discrete state describing stochastic process between transit time.A dynamic bayesian network (DBN) is the expansion concept of a static Bayesian Network by combining (discrete) time.Therefore a dynamic bayesian network is the model of discrete time stochastic process.One group of static Bayesian Network that dynamic bayesian network represents is UNICOM's acnode set between timing node different in each time tangent line.
Markov chain describes a kind of status switch, and its each state value depends on limited state [1] above.Markov chain is the stochastic variable X_1 with Markov property, an ordered series of numbers of X_2, X_3....These ranges of variables, namely they the set of likely value, be called as " state space ", the value of X_n is then the state at time n.If X_{n+1} is only a function of X_n for the conditional probability distribution of past state, then
P(X_{n+1}=x|X_1=x_1,X_2=x_2,...,X_n=x_n)=P(X_{n+1}=x|X_n=x_n)。
Here x is certain state in process.This identical relation can be counted as Markov property above.
The definition of Markovian process:
If { (X (t), t ∈ T) } be a stochastic process, if { X (t), t ∈ T) } when the state residing for the t0 moment is known, its state residing before moment t>t0 has nothing to do, then claim X (t), t ∈ T) there is Markov property.
If { X (t), t ∈ T) } state space be S, if for arbitrary n≤2, arbitrary t1<t2<....<tn ∈ T, at condition X (ti)=xi, xi ∈ S, i=1,2, ..., under n-1, the conditional distribution function of X (tn) equals the conditional distribution function under condition X (tn-1)=xn-1 just, namely
P(X(tn)<=xn|X(t1)=x1,X(t2)=x2，...,X(tn-1)=xn-1)
=P(X(tn)<=xn|X(tn-1)=xn-1)
Then { (X (t), t ∈ T) } is claimed to be Markovian process.
Markovian process can be given sample text, generates rough, but sees plausible text.
Step S208, according to multiple subsystem in the chance of failure determination illuminator of the second time period in the chance of failure of the second time period.
This step is similar to step S204, the chance of failure of subsystem each in this time period is put into Bayesian network model, can determine the chance of failure in this time period illuminator.
Step S210, according to illuminator in the chance of failure of first time period and the illuminator chance of failure in the chance of failure determination illuminator of the second time period.
Just the chance of failure of illuminator in each time period intactly can be determined in the chance of failure of each time period by matching illuminator.
Fig. 4 a is the schematic diagram of LED catastrophic failure in Markov chain model according to the embodiment of the present invention, by the analysis to component failure probability in 4 time periods, draw the dynamic relationship of chance of failure and lumen, in like manner, Fig. 4 b is lower than 70% schematic diagram in Markov chain model according to the LED lumen of the embodiment of the present invention, Fig. 4 c is the schematic diagram of solder joint failure in Markov chain model according to the embodiment of the present invention, and Fig. 4 b is the schematic diagram of electrochemical capacitor inefficacy in Markov chain model according to the embodiment of the present invention.According to information included in above schematic diagram, can obtain Fig. 4 e, Fig. 4 e is the schematic diagram of illuminator inefficacy in Markov chain model according to the embodiment of the present invention.T1, t2, t3 and t4 in Fig. 4 a to Fig. 4 d have identical physical significance, t1 detects first time period after starting, the initial time of t2 is the finish time of t1, the initial time of t3 is the finish time of t2, the initial time of t4 is the finish time of t, and the duration of t1, t2, t3 and t4 is generally identical, in Fig. 4 a to Fig. 4 d, all identical by the t1 in t1 and Fig. 4 a to Fig. 4 d in Fig. 4 e.
Due to Bayesian network model and Markov chain model more complicated, for the ease of the process of data, first can carry out abbreviation by fault tree model to the chance of failure of first time period, chance of failure after abbreviation is sent in Bayesian network model and Markov chain model, greatly reduce required calculated amount, preferably, multiple subsystem can also to be obtained in the illuminator after fault tree model abbreviation in the chance of failure of first time period in step S204.
In the present embodiment, data how new in each mathematical model or algorithm is obtained in order to enable different users, this device can be connected with high in the clouds data bank, preferably, the Bayesian network model from internet can also be obtained in step S204, in like manner, in step S206, the Markov-chain model from internet can also be obtained.
As can be seen from the above description, the embodiment of the present invention accurately can record the chance of failure of illuminator.
It should be noted that, can perform in the computer system of such as one group of computer executable instructions in the step shown in the process flow diagram of accompanying drawing, and, although show logical order in flow charts, but in some cases, can be different from the step shown or described by order execution herein.
Obviously, those skilled in the art should be understood that, above-mentioned of the present invention each module or each step can realize with general calculation element, they can concentrate on single calculation element, or be distributed on network that multiple calculation element forms, alternatively, they can realize with the executable program code of calculation element, thus, they can be stored and be performed by calculation element in the storage device, or they are made into each integrated circuit modules respectively, or the multiple module in them or step are made into single integrated circuit module to realize.Like this, the present invention is not restricted to any specific hardware and software combination.
The foregoing is only the preferred embodiments of the present invention, be not limited to the present invention, for a person skilled in the art, the present invention can have various modifications and variations.Within the spirit and principles in the present invention all, any amendment done, equivalent replacement, improvement etc., all should be included within protection scope of the present invention.
Claims (8)
1. a detection method for illuminator chance of failure, is characterized in that, comprising:
In acquisition illuminator, multiple subsystem is in the chance of failure of first time period;
The chance of failure of described illuminator in described first time period is determined in the chance of failure of described first time period according to described multiple subsystem;
Determine that in described illuminator, multiple subsystem is in the chance of failure of the second time period according to subsystem multiple in described illuminator in the chance of failure of described first time period, wherein, the initial time of described second time period is the end time of described first time period;
The chance of failure of described illuminator in described second time period is determined in the chance of failure of described second time period according to described multiple subsystem; And
The chance of failure of described illuminator is determined in the chance of failure of described first time period and described illuminator in the chance of failure of described second time period according to described illuminator,
Wherein, determine that described illuminator comprises in the chance of failure of described first time period according to described multiple subsystem in the chance of failure of described first time period:
Obtain Bayesian network model;
The chance of failure of described multiple subsystem in described first time period is sent to described Bayesian network model;
Obtain the described illuminator determined by the described Bayesian network model chance of failure in described first time period,
Wherein, determine that in described illuminator, multiple subsystem comprises in the chance of failure of the second time period according to subsystem multiple in described illuminator in the chance of failure of described first time period:
Obtain Markov-chain model;
The chance of failure of described multiple subsystem in described first time period is sent to described Markov-chain model;
In the described illuminator that acquisition is determined by described Markov-chain model, multiple subsystem is in the chance of failure of the second time period.
2. method according to claim 1, is characterized in that, after the chance of failure determining described illuminator, described method also comprises:
Determine the subsystem lost efficacy, wherein, comprise the corresponding relation between the chance of failure of described illuminator and the subsystem of inefficacy at described Bayesian network model.
3. method according to claim 1, is characterized in that, obtains multiple subsystem in illuminator and comprises in the chance of failure of first time period:
In the illuminator of acquisition after fault tree model abbreviation, multiple subsystem is in the chance of failure of first time period.
4. method according to claim 3, is characterized in that,
Acquisition Bayesian network model comprises:
Obtain the Bayesian network model from internet,
Acquisition Markov-chain model comprises:
Obtain the Markov-chain model from internet.
5. a pick-up unit for illuminator chance of failure, is characterized in that, comprising:
Acquiring unit, for obtaining in illuminator multiple subsystem in the chance of failure of first time period;
First determining unit, for determining the chance of failure of described illuminator in described first time period according to described multiple subsystem in the chance of failure of described first time period;
Second determining unit, for determining that in described illuminator, multiple subsystem is in the chance of failure of the second time period according to subsystem multiple in described illuminator in the chance of failure of described first time period, wherein, the initial time of described second time period is the end time of described first time period;
3rd determining unit, for determining the chance of failure of described illuminator in described second time period according to described multiple subsystem in the chance of failure of described second time period; And
4th determining unit, for determining the chance of failure of described illuminator in the chance of failure of described first time period and described illuminator in the chance of failure of described second time period according to described illuminator,
Wherein, described first determining unit comprises:
First obtains subelement, for obtaining Bayesian network model;
First sends subelement, for sending the chance of failure of described multiple subsystem in described first time period to described Bayesian network model;
Second obtains subelement, for obtaining the described illuminator determined by the described Bayesian network model chance of failure in described first time period
Wherein, described second determining unit comprises:
3rd obtains subelement, for obtaining Markov-chain model;
Second sends subelement, for sending the chance of failure of described multiple subsystem in described first time period to described Markov-chain model;
4th obtains subelement, for obtaining in the described illuminator determined by described Markov-chain model multiple subsystem in the chance of failure of the second time period.
6. device according to claim 5, is characterized in that, described device also comprises:
5th determining unit, for determining the subsystem lost efficacy, wherein, comprises the corresponding relation between the chance of failure of described illuminator and the subsystem of inefficacy at described Bayesian network model.
7. device according to claim 5, is characterized in that, described acquiring unit is also for obtaining in the illuminator after fault tree model abbreviation multiple subsystem in the chance of failure of first time period.
8. device according to claim 5, is characterized in that,
Described first obtains subelement also for obtaining the Bayesian network model from internet,
Described 3rd obtains subelement also for obtaining the Markov-chain model from internet.
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