CN101272580B - Self-adapting mobile base station system reliability estimation method based on feedback - Google Patents

Self-adapting mobile base station system reliability estimation method based on feedback Download PDF

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CN101272580B
CN101272580B CN2008100204164A CN200810020416A CN101272580B CN 101272580 B CN101272580 B CN 101272580B CN 2008100204164 A CN2008100204164 A CN 2008100204164A CN 200810020416 A CN200810020416 A CN 200810020416A CN 101272580 B CN101272580 B CN 101272580B
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CN101272580A (en
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史栋杰
顾庆
冯光成
汤九斌
陈道蓄
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Nanjing University
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Abstract

The invention discloses a reliability evaluating method of a self-adaptive mobile base station system based on feedback. At first, real data of a base station system is collected; according to demands the data is preprocessed data for analyzing the failure type, failure time intervals are extracted, and the failure interval data is divided into two groups, wherein, one group is used for method studying and evaluation, and the other group is used for accuracy verification and analysis. Aiming at the first group of data, a plurality of evaluation methods are adopted for evaluating the reliability of the base station system and for pre-estimating the reliability parameters; while aiming at the second group of data, the accuracy of the results evaluated by a plurality of methods are compared, and the evaluation method with high accuracy degree is selected. The two groups of failure time intervals are combined, and the selected evaluation method is adopted for evaluating the reliability parameters of the base station system in the next phase, thus determining the routing inspection and maintenance period to guide the routing inspection and maintenance of the base station system. The method can obtain accurate evaluation results, has better openness, is applicable for the routing inspection and maintenance of the mobile base station system according to types, and is characterized by complex and variable environment.

Description

A kind of self-adapting mobile base station system reliability estimation method based on feedback
Technical field
The present invention relates to a kind of adaptive approach that mobile communication base station system in use carries out the reliability dynamic evaluation, relate to polling period and maintenance cycle that base station system is determined in the assessment of mobile communication base station system dependability parameter, assessment result analysis of the accuracy and feedback, assistance.
Background technology
Reliability is one of inherent characteristic of mass of system, is the importance of mass of system.The accurate reliability of a system of assessment can help the user accurately to grasp the good and bad degree of mass of system, and dependability parameter that can computing system is used in reference to the inspection and maintenance of guiding systems.Base station system commonly used in the mobile communication operators is distributed in wider region usually, and One's name is legion arranges that circumstance complication is various, often is subjected to the influence of factors such as weather.Regular visit and maintenance are the main means that guarantee its quantity of operation.System reliability parameter such as MTTF (MeanTime To Failure, mean time to failure, MTTF are at interval) can be used for optimizing the cycle that setting is patrolled and examined and keeped in repair, thereby finish patrolling and examining and keeping in repair of base station system with the workload of minimum, guarantee the quantity of operation of system.This shows the reliability of accurate evaluating system, calculate accurately reasonably dependability parameter and be used for instructing and patrol and examine and maintenance has economic worth and realistic meaning.
Reliability assessment has the method for fixed number truncation and fixed time truncated test method, mean value method, curve fit and regression analysis etc. usually.Wherein the fixed number truncated test appraisal procedure is that the systematic sample of getting some is tested, specify a failure number in advance, wait to test and stop test when proceeding to the failure number that specified quantity occurs, be a stochastic variable, evaluating system reliability in view of the above this moment the off-test time; The fixed time truncated test appraisal procedure also is that the systematic sample of getting some is tested, specify a test dwell time in advance, treat to stop test when test proceeds to appointment test dwell time, the failure number that occurs during off-test at this moment is a stochastic variable, evaluating system reliability in view of the above; The mean value method is that the thrashing behavior is considered as Poisson process, utilize the mean value of thrashing time sampling to come the estimating system failure rate, the reliability function that obtains system then is to obtain the thrashing distribution map by sampled data to the method that system carries out reliability assessment curve fit and regression analysis, distribution curve is carried out Fitting Analysis, and the reliability function that obtains system is finished the reliability assessment of system.More than these traditional appraisal procedures pluses and minuses are respectively arranged, separately there is limitation in actual applications in use.After base station system puts into effect; outdoor environmental factor complexity is various; different climatic factor such as sleet thunder and lightning for example, the difference of the suffered degree of protection in infield, interference effect of other electronic equipments or the like all has a significant impact the reliability of base station system on every side.Data collection also is a bigger problem in addition, and the fault of single base station system (promptly losing efficacy) record is less usually to be not enough to be used for assess and analyze.So, need the reasonable fault data recorder that uses the base station system of collecting, use more efficiently method to assess the reliability of base station system exactly.
Summary of the invention
Technical problem to be solved by this invention is to utilize existing magnanimity base station fault data, propose a kind of reliability self-adaptive estimation method based on feedback and assess the reliability of mobile communication base station system, solving existing mobile base station does not have accurately that the failure logging of reliability determination methods and single base station system is not enough to be used for assessment and analyzes reliability problems.
Self-adapting mobile base station system reliability estimation method based on feedback of the present invention may further comprise the steps:
(1) to the primary fault data of same many base station systems of class base station system whole-sample magnanimity, carries out the data preliminary treatment, generation interval data fault time and generation system inefficacy distribution map;
(2) base station interval data fault time is divided into two groups, uses multiple alternative reliability estimation method at first group of data base station system is carried out reliability assessment and obtained assessment result; Utilize second group of data that assessment result is carried out variance analysis, use R 2Check comes the order of accuarcy of more above-mentioned various reliability estimation methods for base station system reliability assessment result;
(3) select reliability estimation method evaluating system reliability the most accurately, merge two groups of base station interval datas fault time, the MTTF of calculation base station system customizes the polling period time according to this and is used for instructing practice.
The process that generates base station interval data fault time in the above-mentioned steps (1) is: the device numbering according to base station system divides into groups the magnanimity fault data, obtain the fault data grouping of each base station system, fault data grouping comparing processing for each base station system, obtain the time interval that corresponding base station system is normally moved each time, promptly once begin to alarm interval data fault time that ends before the beginning after the alarm end before the base station system next time to base station system; If do not have the data of termination time point in the time limit in research, once alarm the data segment that concluding time information is not alarmed time started information next time before promptly having only, also be regarded as the fault time interval data and be used for statistical estimation.
Multiple reliability estimation method comprises fixed number truncated test method, fixed time truncated test method, mean value method and curve fit and regression analysis in the step (2).
Wherein the step of fixed number truncated test method is: the systematic sample of getting some is tested, specify a failure number in advance, wait to test and stop test when proceeding to the failure number that specified quantity occurs, be a stochastic variable, evaluating system reliability in view of the above this moment the off-test time.
The step of fixed time truncated test method is: the systematic sample of getting some is tested, specify a test dwell time in advance, treat that test stops test when proceeding to appointment test dwell time, the failure number that occurs during off-test at this moment is a stochastic variable, evaluating system reliability in view of the above.
The step of mean value method is: the thrashing behavior is considered as Poisson process, utilizes the mean value of thrashing time sampling to come the estimating system failure rate, the reliability function that obtains system carries out reliability assessment to system.
The step of curve fit and regression analysis is: obtain the thrashing distribution map by sampled data, distribution curve is carried out Fitting Analysis, the reliability function that obtains system is finished the reliability assessment of system, and selected curve fit comprises the exponential distribution curve fit, improves exponential distribution curve fit and two parameters of Weibull curve fits.
R in the above-mentioned steps (2) 2=return SS/ to proofread and correct SS, wherein return the predicted value and the quadratic sum of mean bias of SS for observation, proofread and correct SS for return SS and residual error SS with, this residual error SS is the quadratic sum of measured value and predicted value deviation; Work as R 2Approached 1 o'clock, and thought that then the regression function match was accurate, simultaneously R 2Value big more, the accuracy of return estimating is big more.
The present invention is based on the mobile communication base station system operation characteristic, adopt a kind of adaptive reliability appraisal procedure to carry out whole-sample at the fault data of a class base station based on feedback, data volume is big, can obtain assessment result accurately, be suitable for the characteristics that the mobile base station system category is patrolled and examined and keeped in repair simultaneously; Secondly, the present invention can select suitable appraisal procedure to obtain result more accurately according to the actual samples data, is suitable for base station system operating environment characteristics complicated and changeable; In addition, the present invention can also fully utilize the advantage of a plurality of reliability estimation methods, can adjust the alternative set of appraisal procedure and upgrade reliability estimation method at any time, has open preferably; And the present invention can be applied to easily that the maintenance of other field such as motor vehicle, power equipment are safeguarded, the plant maintenance of industrial enterprise etc.
Be elaborated below in conjunction with accompanying drawing.
Description of drawings
Fig. 1 is the method flow diagram of mobile base station system reliability dynamic self-adapting assessment;
Fig. 2 is a base station system fault data temporal information schematic diagram, and wherein Fig. 2 a is that alarm opening entry and Fig. 2 b are the alarm end records;
Fig. 3 is the algorithm flow chart of implementing preliminary treatment and generating interval data fault time;
Fig. 4 is the fault time of a cumulative distribution exemplary plot at interval;
Fig. 5 is the fault time of a density distribution exemplary plot at interval;
Fig. 6 is candidate's reliability estimation method assessment result exemplary plot;
Fig. 7 uses reliability assessment instrument sectional drawing of the present invention.
Embodiment
As shown in Figure 1, experiment process of the present invention is: to the primary fault data of same many base station systems of class base station system whole-sample magnanimity; Carry out the data preliminary treatment, generation interval data section fault time and generation system inefficacy distribution map; Use multiple alternative reliability estimation method base station system is carried out reliability assessment; Use R 2Check comes the order of accuarcy of more various reliability estimation methods for base station system reliability assessment result; Select reliability estimation method evaluating system reliability the most accurately, the parameters such as MTTF of calculation base station system, customization is is according to this patrolled and examined with maintenance cycle and is used for instructing practice.
Base station system fault data temporal information as shown in Figure 2.The failure logging of base station system generally include the alarm opening entry (Fig. 2 a) with alarm end record (Fig. 2 b) two kinds, the data preliminary treatment need be sought the alarm end record of same base station system and corresponding with it alarm opening entry from the failure logging of magnanimity, thereby generates interval data fault time.The algorithm flow of implementing preliminary treatment and generating interval data fault time is as shown in Figure 3: according to the device numbering screening magnanimity fault data and the grouping of base station system, obtain the fault data grouping of each base station system.Fault data grouping comparing processing for each base station system, the uptime promptly once begins to alarm interval data fault time that ends before the beginning to base station system after the alarm end before the base station system at interval next time each time to obtain corresponding base station system; If do not have the data of termination time point in the time limit in research, once alarm the data segment that concluding time information is not alarmed time started information next time before promptly having only, also be regarded as the fault time interval data and be used for statistical estimation.With interval data whole-sample fault time of same type of base station system, resulting fault time, interval data was the basis of base station system reliability assessment.
Can further obtain each base station system fault time at interval cumulative distribution figure behind the interval data fault time that obtains each base station system, is illustrated in figure 4 as the base station system fault time of a cumulative distribution exemplary plot at interval.Can obtain each the base station system fault time of density profile at interval equally, be illustrated in figure 5 as the base station system fault time of a density distribution exemplary plot at interval.
Candidate's appraisal procedure of the present invention comprises: the method for test method, mean value method, curve fit and the regression analysis of fixed number truncation and timing truncation.
1. fixed number truncated test method
Get n separate sample respectively for every kind of base station system and test, specify a failure number r (0≤r≤n), finished test when r sample lost efficacy in advance when test has proceeded to.This moment, concluding time t was a stochastic variable.Fixed number truncated test is divided into to be had replacement and not to have two kinds of replacements.Nothing replacement fixed number truncated test promptly in process of the test, replace without normal system after occurring losing efficacy by pilot system, proceeded to test.Along with test is carried out, deactivation system increases gradually, and normal system reduces gradually.When r=n, be called complete sampling test.Have and replace fixed number truncated test promptly in process of the test, used normal system to substitute immediately after losing efficacy appears in system, proceed test.Normal operational system number remains unchanged in the entire test.
Carry out fixed number truncated test when carrying out statistical inference for the system of inefficacy obeys index distribution, need use following theorem: the exponential distribution failure rate is constant λ, and the failure density function is f (t)=λ e -λ t, the inefficacy distribution function is F (t)=1-e -λ t, the test sample number is n, preceding z out-of-service time observed value is t 1≤ t 2≤ t 3≤ ... ≤ t z(z≤n).For there not being the fixed number truncated test of replacement, total testing time is τ = Σ i = 1 z t i + ( n - z ) × t z , Then the 2 λ t obedience degree of freedom is the χ of 2z 2Distribute, promptly
Figure S2008100204164D00052
For the replacement fixed number truncated test is arranged, total testing time is τ=nt z, then the 2 λ t obedience degree of freedom is the χ of 2z 2Distribute, promptly
Figure S2008100204164D00053
The present invention adopts does not at present have the method for replacing fixed number truncated test, adopts classical way that the base station system reliability is assessed.For given confidence level γ, establish and be limited to λ on the failure rate U, C, γ=P (λ≤λ is then arranged U, C)=P (2 τ λ≤2 τ λ U, C); Because
Figure S2008100204164D00054
Have λ U , C = χ 2 z 2 2 τ , Wherein
Figure S2008100204164D00056
For the degree of freedom is the χ of 2z 2Distribution function is in the downside quantile of confidence level γ.The reliability function of system is: R ( t ) = e - λ U , C t
2. fixed time truncated test method
Fixed time truncated test and fixed number truncated test are similar, get n separate sample respectively for every kind of base station system and test, and specify a test termination time t in advance, just stop test constantly when test proceeds to this appointment.At this moment, the number of systems that occurs during off-test losing efficacy is a stochastic variable.Fixed time truncated test also is divided into to be had replacement and not to have two kinds of replacements.Nothing replacement fixed time truncated test promptly in process of the test, replace without normal system after occurring losing efficacy by pilot system, proceeded to test.Along with test is carried out, deactivation system increases gradually, and normal system reduces gradually.Have and replace fixed time truncated test promptly in process of the test, used normal system to substitute immediately after losing efficacy appears in system and proceed test.Normal operational system number remains unchanged in the entire test.
When not having the fixed time truncated test of replacement for the system of inefficacy obeys index distribution, its failure rate is constant λ, and the inefficacy distribution function is F (t)=1-e -λ t, to get n sample and test, truncated time is t, and occurring failure number during truncation is z, does not replace regularly truncation for having, and total testing time is τ = Σ i = 1 z t i + ( n - z ) × t z . For the system of life-span obeys index distribution, when not having the fixed time truncated test of replacement, the Cox method thinks that the approximate obedience of the 2 λ t degree of freedom is the χ of 2z+1 in the classical way 2Distribute, promptly
Figure S2008100204164D00062
So given confidence level γ then is limited on the failure rate λ U , C = χ 2 z + 1 , γ 2 / ( 2 τ ) , Wherein
Figure S2008100204164D00064
For the degree of freedom is the χ of 2z+1 2Distribution function is in the downside quantile of confidence level γ.The reliability function of system is: R ( t ) = e - λ U , C t .
3. mean value method
Because base station system is electronics and electric power system, its Life Distribution has memoryless property, promptly system at any time the probability of point failure all be constant, the lifetime of system obeys index distribution.Inefficacy λ (t) can be considered constant λ, and λ = 1 MTTF .
Can use the method for mean value to come simple computation MTTF thus.According to interval data fault time of sampling gained base station system, calculating mean value obtains MTTF, calculates λ then.The failure density function of exponential distribution is f (t)=λ e -λ t, distribution function is F (t)=1-e -λ t, obtain reliability function R (t)=e -λ t
4. exponential distribution curve-fitting method
Think the inefficacy distribution obeys index distribution of base station system, can use the method for curve fit and regression analysis that the Life Distribution of system is carried out the exponential distribution Fitting Analysis.According to interval data fault time of sampling gained, utilize the relation of R (t)=1-F (t), earlier the inefficacy distribution map is converted to the time dependent functional arrangement of reliability, carry out Fitting Analysis then, obtain reliability function, the computed reliability assessment result.
5. improve the exponential distribution curve-fitting method
Because the fault data of sampling belongs to a plurality of base station systems, the fitting precision error is general bigger in the curve fitting process.Directly reliability function is thought R (t)=e -λ tForm may be improper.The present invention finely tunes reliability function, proposes R (t)=a * e respectively -λ tForm and R (t)=b+a * e -λ tForm reliability function is carried out Fitting Analysis, the reliability assessment result who is improved.
6. two parameters of Weibull curve-fitting methods
Think and obey Weibull distribution from the fault data of a plurality of base station systems.Use the reliability function of two parameters of Weibull forms at present R ( t ) = e - ( t / η ) m , The time dependent functional arrangement of base station system reliability is carried out Fitting Analysis, obtain the reliability assessment result.
The present invention is divided into two groups with interval data fault time of base station system, and one group is used for method study (calculation of parameter) and estimates that another group is used for the accuracy checking and analyzes.At first group fault time interval data, adopt above-mentioned multiple systems reliability estimation method that the reliability of base station system is assessed and the dependability parameter pre-estimation.Be to use various alternative reliability estimation methods carry out reliability assessment to a class base station system assessment result exemplary plot as shown in Figure 6.
For the assessment result that above-mentioned several reliability estimation methods obtain, utilize second group of data that a plurality of assessment results are carried out variance analysis, use R 2Check comes the assessment levels of precision of more various appraisal procedures.At first functional expression is converted into linear forms y=ax+b.The reliability function of said method 1～4 is R (t)=e -λ tForm can be converted into linear forms with it: lnR (t)=-λ t.Two improved form R (t)=a * e of method 5 -λ tAnd R (t)=b+a * e -λ tAlso can be converted into linear forms, be respectively lnR (t)=lna-λ t and ln (R (t)-b)=lna-λ t.In like manner the linear forms of method 6 are ln (ln (1/R (t)))=mlnt-mln η.For the judgement of linear forms recurrence precision, can be by calculating R 2Obtain.
Σ i = 1 n ( y i - y ‾ ) 2 = Σ i = 1 n ( y ^ i - y ‾ ) 2 + Σ i = 1 n ( y i - y ^ i ) 2
Y in the following formula iBe the i time observed value,
Figure S2008100204164D00073
Be the observed value average,
Figure S2008100204164D00074
It is the predicted value of the i time observation.The following formula left side is the corrected sum or squares of variable Y, is abbreviated as to proofread and correct SS.
Figure S2008100204164D00075
Be the predicted value of the i time observation and the deviation of average, its quadratic sum is a regression sum of square, is abbreviated as to return SS.
Figure S2008100204164D00076
Be the deviation (residual error) of the i time measured value and predicted value, its quadratic sum becomes residual sum of squares (RSS), is abbreviated as residual error SS.Following equation is arranged like this:
Corrected sum or squares=regression sum of square+residual sum of squares (RSS)
This just is decomposed into two parts with the Y estimated value deviation (representing with corrected sum or squares) of sampling average together: a preceding part is caused by regression function itself, has reflected the intensity of functional relation; A back part is not dropped on the tropic by sampled value and causes, has reflected the degree of random deviation.This two-part ratio has reflected the degree of differentiating tropic fitting degree quality: in proofreading and correct SS, if it is big more than residual error SS to return SS, i.e. and R 2=(returning SS)/(proofreading and correct SS) approached 1, thought that then the regression function match is accurate.While R 2Value big more, the precision of then return estimating is big more.For the method for various reliability assessments, its R 2Be worth greatly more, reliability function is just accurate more, for the result of base station system reliability assessment approaching more reality just.
R with above-mentioned various reliability estimation methods 2Be aggregated into R 2(see Table 1) in the summary sheet as a result.According to R 2Can relatively to draw which kind of method higher to the reliability assessment accuracy of base station system for summary sheet as a result.Be the R that judges candidate's reliability estimation method accuracy as shown in Figure 7 2The summary sheet example.The R of two parameters of Weibull curve-fitting methods as can be seen from the table 2Maximum illustrates that this reliability estimation method of this stage is to this type of base station system appraisal procedure the most accurately, can be selected for the calculating of next stage dependability parameter.
Table 1
Reliability estimation method Linear relation R 2Example
The mean value method ln?R(t)＝-λt 0.93024
The fixed number truncated test method ln?R(t)＝-λt 0.92947
The fixed time truncated test method ln?R(t)＝-λt 0.87661
Curve-fitting method ln?R(t)＝-λt 0.94444
Improved exponential distribution curve-fitting method one ln?R(t)＝ln?a-λt 0.97720
Improved exponential distribution curve-fitting method two ln(R(t)-b)＝ln?a-λt 0.98009
Two parameters of Weibull curve-fitting methods ln(ln(1/R(t)))＝mlnt-mlnη 0.99297
After the selected appraisal procedure, use whole fault time of the interval data of this stage sampling, the assessment base station system is determined to patrol and examine and maintenance cycle at the dependability parameter of next stage in view of the above, and the direct base station system patrolling and examining and keeping in repair.An important indicator of reflection base station system reliability is MTTF, i.e. the mean time between failures (MTBF) of base station system; Again according to the restriction of patrolling and examining with maintenance manpower, and the base station loss of service that breaks down and cause, optimize the time cycle of all kinds of base station system pollings of customization and maintenance, rationally arrange to patrol and examine and the carrying out of maintenance work.A kind of method is to set the target reliabilities lower limit of base station system, finishes before this system reliability index drops to the target reliabilities lower limit and patrols and examines and maintenance work.
Technology contents according to the present invention has been developed a base station system fault data and has been safeguarded and the reliability decision instrument.Fig. 7 is the instrument sectional drawing, can require according to the given reliability lower limit of base station system to calculate to patrol and examine and cycle maintenance time.Instrument realizes based on Java language, and Java language has complete object-oriented, portable characteristics such as strong, by corresponding Java Virtual Machine is installed, can guarantee that this method can operate on the operating system platform of present main flow.This instrument uses in practice and has obtained good repercussion.

Claims (6)

  1. One kind based on the feedback self-adapting mobile base station system reliability estimation method, it is characterized in that may further comprise the steps:
    (1) to the primary fault data of same many base station systems of class base station system whole-sample magnanimity, carry out the data preliminary treatment, generation interval data fault time and generation system inefficacy distribution map, the process that wherein generates base station interval data fault time is: the device numbering according to base station system divides into groups the magnanimity fault data, obtain the fault data grouping of each base station system, fault data grouping comparing processing for each base station system, obtain the time interval that corresponding base station system is normally moved each time, promptly once begin to alarm interval data fault time that ends before the beginning after the alarm end before the base station system next time to base station system; If do not have the data of termination time point in the time limit in research, once alarm the data segment that concluding time information is not alarmed time started information next time before promptly having only, also be regarded as the fault time interval data and be used for statistical estimation;
    (2) base station interval data fault time is divided into two groups, uses multiple alternative reliability estimation method at first group of data base station system is carried out reliability assessment and obtained assessment result; Utilize second group of data that assessment result is carried out variance analysis, use R 2Check comes the order of accuarcy of more above-mentioned various reliability estimation methods for base station system reliability assessment result;
    (3) select reliability estimation method evaluating system reliability the most accurately, merge two groups of base station interval datas fault time, the MTTF of calculation base station system customizes the polling period time according to this and is used for instructing practice.
  2. 2. the self-adapting mobile base station system reliability estimation method based on feedback according to claim 1 is characterized in that using in the step (2) multiple reliability estimation method and comprises fixed number truncated test method, fixed time truncated test method, mean value method and curve fit and regression analysis.
  3. 3. the self-adapting mobile base station system reliability estimation method based on feedback according to claim 2, the step that it is characterized in that the fixed number truncated test method is: the systematic sample of getting some is tested, specify a failure number in advance, wait to test and stop test when proceeding to the failure number that specified quantity occurs, off-test time this moment is a stochastic variable, evaluating system reliability in view of the above.
  4. 4. the self-adapting mobile base station system reliability estimation method based on feedback according to claim 2, the step that it is characterized in that the fixed time truncated test method is: the systematic sample of getting some is tested, specify a test dwell time in advance, treat that test stops test when proceeding to appointment test dwell time, the failure number that occurs during off-test at this moment is a stochastic variable, evaluating system reliability in view of the above.
  5. 5. the self-adapting mobile base station system reliability estimation method based on feedback according to claim 2, the step that it is characterized in that the mean value method is: the thrashing behavior is considered as Poisson process, utilize the mean value of thrashing time sampling to come the estimating system failure rate, the reliability function that obtains system carries out reliability assessment to system.
  6. 6. the self-adapting mobile base station system reliability estimation method based on feedback according to claim 2, the step that it is characterized in that curve fit and regression analysis is: obtain the thrashing distribution map by sampled data, distribution curve is carried out Fitting Analysis, the reliability function that obtains system is finished the reliability assessment of system, and selected curve fit comprises the exponential distribution curve fit, improves exponential distribution curve fit and two parameters of Weibull curve fits.
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Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP0819361A1 (en) * 1995-04-03 1998-01-21 Telefonaktiebolaget Lm Ericsson Transceiver tester for cellular radio
CN1447546A (en) * 2002-03-27 2003-10-08 华为技术有限公司 Multi-user detection device based on prior information in base station and its detection method
CN101014173A (en) * 2006-12-31 2007-08-08 姜宏伟 Method and system for transmitting monitoring data of communication base station

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP0819361A1 (en) * 1995-04-03 1998-01-21 Telefonaktiebolaget Lm Ericsson Transceiver tester for cellular radio
CN1447546A (en) * 2002-03-27 2003-10-08 华为技术有限公司 Multi-user detection device based on prior information in base station and its detection method
CN101014173A (en) * 2006-12-31 2007-08-08 姜宏伟 Method and system for transmitting monitoring data of communication base station

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