CN104159245A - Indirect health factor acquisition method oriented to wireless data transmission equipment - Google Patents

Abstract

The invention discloses an indirect health factor acquisition method oriented to wireless data transmission equipment, belongs to the field of complex system diagnosis and prediction, and aims to solve the problem of reduction in the power adjusting accuracy due to poor response maintaining capability of conventional data transmission equipment in a working state. In the indirect health factor acquisition method oriented to the wireless data transmission equipment disclosed by the invention, an indirect health factor which can be used for reflecting the health state of the data transmission equipment is extracted on the aspect of test data by using a statistical theory in combination with power and an automatic control gain parameter, so that the power adjustment accuracy is increased effectively, and a feasible guidance thought is provided for the fault prediction and health management of electronic equipment. The method is suitable for a certain practical airborne wireless data communication relay unit, application verification is performed, and a feasible reference thought is provided for the degeneration state identification work of a wireless data transmission system.

Description

Indirect health factor preparation method towards radio data-transmission equipment

Technical field

The invention belongs to complication system diagnosis and prediction field, relate in particular to a kind of indirect health factor construction method of radio data-transmission equipment.

Background technology

Prognostic and health management (PHM) has become the important research content in complication system diagnosis and prediction field gradually.Especially along with the fast development of data acquisition and the communication technology, utilize Condition Monitoring Data to carry out the study hotspot that the prediction of the identification of system degradation performance and health status becomes prognostic and health management field, i.e. the research of the prognostic and health management based on data-driven.Failure prediction method based on data-driven has obtained domestic and international expert's extensive concern at present, and its research object also extends to battery, aircraft subsystem, satellite subsystem etc. from simple mechanical system.But utilize at present also rarely seen report of research that data-driven method realizes radio data-transmission equipment trend prediction, trace it to its cause: the one, because radio data-transmission equipment belongs to electronic equipment, it forms complicated, by a large amount of electronic devices and components and chip, auxiliary equipment etc., formed, the 2nd, data-driven method does not have actual background to support, and the method for data-driven is not corresponding with the concrete multiplicative model of radio data-transmission equipment.

Radio data-transmission equipment belongs to typical electronic equipment, data communication that the progress of radio data-transmission equipment is perfect.And radio data-transmission equipment has very large range of application, the data transmission service of nearly all middle low rate can be applied, and still due to the complexity of electronic equipment, the current research for radio data-transmission equipment self performance seldom.

Radio data-transmission equipment is comprised of external data equipment, wireless data transfer module conventionally, its system block diagram, and as shown in Figure 1, external equipment is the equipment such as PC or data acquisition.Wireless data transfer module is the important component part of radio data-transmission equipment, has comprised radiating circuit and receiving circuit, radio-frequency front-end, antenna etc.The self performance quality of wireless data transfer module has vital impact for the accuracy of whole data transmission set transfer of data.

Data transmission set just can carry out stable transfer of data under stable power, and under actual working state, the power of data transmission set presents certain fluctuation along with the variation of external environment and other factors.Therefore, automatic gain control being set in data transmission set regulates power.When power drops under allowed threshold value, prescribe a time limit, the gain of system automatic lifting, rises power, and correspondingly, when power rises to upper threshold, system reduces gain automatically, and power is remained in the scope of system permission.Automatic gain is controlled and is automatically adjusted along with the variation of power, and corresponding response is made in the variation that power is controlled along with automatic gain.Specifically, certain power gain can cause increased power or reduce, if degenerating appears in the performance of system, the response quality of controlling for gain so can decline to some extent, and this decline is mainly reflected in two aspects: one, the order of accuarcy of response declines, and from data angle, with regard to the power change values that shows as unit gain and can cause, does not reach the level of expection, and the power variation that unit gain causes there will be decline to a certain degree; Its two, the degree of stability of response declines, and just shows as the fluctuation increase of the power change values sequence that unit gain can cause from the angle of data, there is ascendant trend in its variance or standard deviation.No matter be from which aspect analysis, by the corresponding relation of automatic gain and power response, it is all practicable obtaining health factor performance-relevant with data transmission set and that present certain variation tendency.And the health factor of this reflection equipment degradation trend obtaining by the relation between outer survey parameter is called indirect health factor.Further analyze, for in running order data transmission set, before there is very large power response deviation, first the unsteadiness that there will be response, be that first performance degradation shows as and can access intended response, but response to maintain ability poor, there is larger fluctuation, just can continue subsequently to degenerate occurs that being difficult to reach expection adjusted value is the situation of power Adjusting accuracy decline.

Summary of the invention

The present invention is in order to solve existing in running order data transmission set, response to maintain ability poor, thereby the problem that causes power Adjusting accuracy to decline now provides the indirect health factor preparation method towards radio data-transmission equipment.

Towards the indirect health factor preparation method of radio data-transmission equipment, take and power up number of times as variable structure health factor, the method comprises the following steps:

Step is one by one: airborne wireless communication TU Trunk Unit is carried out powering up for n time, n is greater than 20 integer, gather respectively sequence { sequence that AGG} and relaying power data form { P}, acquisition n group relaying AGG automatic gain sequence { AGG} and n group relaying power sequence { P} that the relaying AGG automatic gain data while at every turn powering up form;

Step 1 two: respectively to n group relaying AGG automatic gain sequence AGG} and n group relaying power sequence P} carries out first-order difference, obtain differentiated n group automatic gain increment sequence AGG_d} and n group relaying power increment sequence P_d},

{AGG_d}＝AGG(i+1)-AGG(i)i＝1,2,...,n

{P_d}＝P(i+1)-P(i)

When { during AGG_d}=0, { AGG_d} and power increment sequence { in P_d}, make { relaying AGG automatic gain data and the relaying power data of AGG_d}=0 to reject this group automatic gain increment sequence;

Step 1 three: respectively to reject n group automatic gain increment sequence after data AGG_d} and n group power increment sequence P_d} carries out power increment calculating, obtain the n group sequence that power increment data y (i) corresponding to unit gain form y},

$y\left(i\right)=\frac{P\_d(i+t)}{\mathrm{AGG}\_d\left(i\right)}$

Wherein, the time span of t for postponing;

Step 1 four: the power increment sequence corresponding according to n group unit gain y} acquisition n group sequence the fluctuation characterization parameter var (y) of y}, i.e. variance:

$\mathrm{var}\left(y\right)=\mathrm{var}\left(\frac{P\_d(i+1)}{\mathrm{AGG}\_d\left(i\right)}\right)=\underset{i=1}{\overset{N}{\mathrm{\Σ}}}\frac{{(y_i-\stackrel{\‾}{y})}^2}{N-1}$

Wherein, the number that N is data point, for the sequence { average of y};

And { the fluctuation characterization parameter var (y) of y} is as the indirect health factor of radio data-transmission equipment using the n group sequence obtaining.

Towards the indirect health factor preparation method of radio data-transmission equipment, take the time as variable structure health factor, the method comprises the following steps:

Step 2 one: Yi Yuewei unit, airborne wireless communication TU Trunk Unit is powered up, gather respectively the sequence { sequence { P} that AGG} and relaying power data form of the relaying AGG automatic gain data formation in m discontinuous independent month, acquisition m group relaying AGG automatic gain sequence AGG} and m group relaying power sequence P}, wherein m is for being more than or equal to 6 integers;

Step 2 two: respectively to m group relaying AGG automatic gain sequence AGG} and m group relaying power sequence P} carries out first-order difference, obtain differentiated m group automatic gain increment sequence AGG_d} and m group relaying power increment sequence P_d},

{AGG_d}＝AGG(i+1)-AGG(i)

{P_d}＝P(i+1)-P(i)

i＝1,2,...,n

When { during AGG_d}=0, { AGG_d} and power increment sequence { in P_d}, make { relaying AGG automatic gain data and the relaying power data of AGG_d}=0 to reject this group automatic gain increment sequence;

Step 2 three: respectively to reject m group automatic gain increment sequence after data AGG_d} and m group power increment sequence P_d} carries out power increment calculating, obtain the m group sequence that power increment data y (i) corresponding to unit gain form y},

$y\left(i\right)=\frac{P\_d(i+t)}{\mathrm{AGG}\_d\left(i\right)}$

Wherein, the time span of t for postponing;

Step 2 four: Yi Yuewei unit, the power increment sequence corresponding according to m group unit gain y} obtain common m group sequence in each month the fluctuation characterization parameter var (y) of y}, i.e. variance:

$\mathrm{var}\left(y\right)=\mathrm{var}\left(\frac{P\_d(i+1)}{\mathrm{AGG}\_d\left(i\right)}\right)=\underset{i=1}{\overset{N}{\mathrm{\Σ}}}\frac{{(y_i-\stackrel{\‾}{y})}^2}{N-1}$

Wherein, the number that N is data point, for the sequence { average of y};

Fluctuation characterization parameter var (y) is numbered according to the time span of its appearance, time span is fluctuation characterization parameter var (y) place month span, if fluctuation characterization parameter var (y) numbered sequence of corresponding month is { at}, be known month, disappearance month numbered sequence is { qt};

Step 2 five: { the fluctuation characterization parameter var (y) of y} carries out matching to m group sequence, obtain the variance curve expression formula after matching, respectively by known month numbered sequence at} with disappearance month numbered sequence for { qt} brings the variance expression formula of matching into, obtains the matching fluctuation characterization parameter var_f in known month _atmatching fluctuation characterization parameter var_f with disappearance month _qt;

Step 2 six: by the matching fluctuation characterization parameter var_f in the known month of the fluctuation characterization parameter var (y) obtaining in step 2 five and step 2 six acquisitions _atcarry out error of fitting calculating, obtain the error of fitting result sequence error in known month _at:

error _at＝var(y)-var_f _at；

Step 2 seven: the error of fitting result sequence error to known month _atcarry out test of normality, obtain the error of fitting result sequence error in known month _ataverage and standard deviation sigma _error,

$\stackrel{\‾}{\mathrm{error}}=\frac{\underset{i=1}{\overset{m}{\mathrm{\Σ}}}{\mathrm{error}}_i}{m}$

${\mathrm{\σ}}_{\mathrm{error}}=\sqrt{\frac{1}{m}\underset{i=1}{\overset{m}{\mathrm{\Σ}}}{({\mathrm{error}}_i-\stackrel{\‾}{\mathrm{error}})}^2}$

Wherein, error _ithe error of fitting result sequence error that represents known month _atin i error of fitting data;

Step sixteen: in average and standard deviation sigma _errorthe random error result sequence error that generates disappearance month in the data of normal distribution _qt;

Step 2 nine: by the matching fluctuation characterization parameter var_f in the disappearance month obtaining in step 2 six _qtthe error result sequence error in the disappearance month generating with step 2 nine _qtsuperpose, obtain the reconstruct fluctuation characterization parameter sequence var in disappearance month _qt

var _qt＝var_f _qt+error _qt；

Step 2 ten: the reconstruct fluctuation characterization parameter sequence var in the disappearance month that the fluctuation characterization parameter var (y) that step 2 five is obtained and step 2 ten obtain _qtcurve plotting after arranging according to the numbering in month, and using this curve as take the indirect health factor that the time is variable.

Indirect health factor preparation method towards radio data-transmission equipment of the present invention, angle from test data, take certain airborne wireless data communication TU Trunk Unit is identifying object, take respectively time and power up number of times as variable, build respectively the health factor of its performance degradation of reflection, so that staff plans feasible maintenance policy ahead of time, for the prognostic and health management of electronic equipment provides the practicable thinking that instructs.

The present invention studies the degenerate state identification of radio data-transmission equipment, physical meaning from test parameter, utilize statistical theory, in conjunction with power and automatically ride gain parameter extraction gone out to reflect the indirect health factor of data transmission set health status, be power increment variance sequence corresponding to unit gain, effectively improved power Adjusting accuracy.Be applicable to, in certain actual airborne wireless data communication TU Trunk Unit, carry out application verification, for the degenerate state identification work of wireless system for transmitting data provides practicable with reference to thinking.

Accompanying drawing explanation

Fig. 1 is the system block diagram of radio data-transmission equipment;

Fig. 2 is the variance sequence curve figure of the corresponding power increment of unit gain;

Fig. 3 is the variance sequence curve figure of the corresponding power increment of straight control unit gain;

Fig. 4 is during with time variable, 6 curve charts that fluctuation characterization parameter is drawn;

Fig. 5 is during with time variable, the curve chart that 6 fluctuation characterization parameters that contain time delay t=20 are drawn

Fig. 6 is during with time variable, fluctuation characterization parameter fit error curve figure;

Fig. 7 is error of fitting test of normality curve chart;

Fig. 8 is during with time variable, the variance data curve chart of the continuous every month after missing data reconstruction;

Fig. 9 is during with time variable, indirectly health factor curve chart.

Embodiment

Embodiment one: the indirect health factor preparation method towards radio data-transmission equipment described in present embodiment, take and power up number of times as variable structure health factor, the method comprises the following steps:

Step is one by one: airborne wireless communication TU Trunk Unit is carried out powering up for n time, n is greater than 20 integer, gather respectively sequence { sequence that AGG} and relaying power data form { P}, acquisition n group relaying AGG automatic gain sequence { AGG} and n group relaying power sequence { P} that the relaying AGG automatic gain data while at every turn powering up form;

Step 1 two: respectively to n group relaying AGG automatic gain sequence AGG} and n group relaying power sequence P} carries out first-order difference, obtain differentiated n group automatic gain increment sequence AGG_d} and n group relaying power increment sequence P_d},

{AGG_d}＝AGG(i+1)-AGG(i)i＝1,2,...,n

{P_d}＝P(i+1)-P(i)

When { during AGG_d}=0, { AGG_d} and power increment sequence { in P_d}, make { relaying AGG automatic gain data and the relaying power data of AGG_d}=0 to reject this group automatic gain increment sequence;

Step 1 three: respectively to reject n group automatic gain increment sequence after data AGG_d} and n group power increment sequence P_d} carries out power increment calculating, obtain the n group sequence that power increment data y (i) corresponding to unit gain form y},

$y\left(i\right)=\frac{P\_d(i+t)}{\mathrm{AGG}\_d\left(i\right)},i=\mathrm{1,2},...$

Wherein, the time span of t for postponing;

Step 1 four: the power increment sequence corresponding according to n group unit gain y} acquisition n group sequence the fluctuation characterization parameter var (y) of y}, i.e. variance:

$\mathrm{var}\left(y\right)=\mathrm{var}\left(\frac{P\_d(i+1)}{\mathrm{AGG}\_d\left(i\right)}\right)=\underset{i=1}{\overset{N}{\mathrm{\Σ}}}\frac{{(y_i-\stackrel{\‾}{y})}^2}{N-1}$

Wherein, the number that N is data point, for the sequence { average of y};

And { the fluctuation characterization parameter var (y) of y} is as the indirect health factor of radio data-transmission equipment using the n group sequence obtaining.

Consider for data transmission equipment, the amount with sequential shows as the most intuitively and powers up number of times, adds the numbered sequence of electrical testing, is directed to and powers up number of times, the health factor building can be used for reacting data transmission equipment in use, can repeat the number of times of the work that powers up; Take and power up the processing that health factor that number of times builds as variable does not exist missing data, it is continually varying variable.

Take certain airborne wireless data communication TU Trunk Unit powers up number of times as variable, and the independently testing experiment of take is unit, independently carries out the statistical analysis of each test data.According to the storage feature of test data, the testing time of take powers up number of times and carries out the structure of extraction, calculating, analysis and the health factor of data as unit.

21 relaying AGG automatic gains and the relaying power of take in one group of data of certain airborne wireless communication TU Trunk Unit are surveyed data outward to being example, illustrate present embodiment:

Read by the testing time arrange amount to 21 relaying AGG automatic gains and relaying power data sequence, relaying AGG data be designated as AGG} sequence, relaying power data is designated as { P} sequence;

Two column datas are carried out respectively to the first-order difference of adjacent time point suc as formula (1), (2), obtain differentiated data { AGG_d} and { P_d};

AGG_d＝AGG(i+1)-AGG(i)i＝1,2,...,n (1)

P_d＝P(i+1)-P(i) (2)

For the situation that there is no change in gain on interval, { during AGG_d}=0, reject power increment and the gain delta data of this test point, because when gain does not change, cannot determine that unit gain changes caused flash-up;

Data after rejecting are carried out to the corresponding power increment of unit gain and calculate, choose different the delay here and count as across comparison, carry out respectively ratio calculation, according to formula (3), calculate, obtain required sequence { y};

$y\left(i\right)=\frac{P\_d(i+t)}{\mathrm{AGG}\_d\left(i\right)}---\left(3\right)$

Wherein, the time span of t for postponing.

{ the fluctuation characterization parameter of y} is that serial variance is calculated, and powers up at every turn and calculates a variance (take t=1 as example) to obtain sequence

$\mathrm{var}\left(y\right)=\mathrm{var}\left(\frac{P\_d(i+1)}{\mathrm{AGG}\_d\left(i\right)}\right)=\underset{i=1}{\overset{N}{\mathrm{\Σ}}}\frac{{(y_i-\stackrel{\‾}{y})}^2}{N-1}---\left(4\right)$

The number that in above formula, N is data point, for the sequence { average of y}.

21 according to the sequence of Time alignment, { y} calculates the variance sequence of table 1 according to formula (4), and graphed, as shown in Figure 2.

The corresponding power increment variance of table 1 relaying unit gain sequence-result

According to above-mentioned thinking, extract the outer parameter of surveying of another group of airborne wireless data communication TU Trunk Unit---directly control AGG automatic gain and straight control power sequence, carry out identical processing, it is { the straight control of the AGG} sequence representative AGG automatic gain in above-mentioned steps, { the straight control of P} representative power, sets and postpones t=3 result as shown in Table 2 and Figure 3.

The corresponding power increment variance of the straight control of table 2 unit gain sequence-result

After delay disposal, the variance sequence of obtaining is the trend of rising overally, and curve smoothing, has also shown that power increment variance sequence corresponding to unit gain presents ascendant trend along with the use of airborne wireless communication unit, can be used in the health status of reflection equipment.

Embodiment two: the indirect health factor preparation method towards radio data-transmission equipment described in present embodiment, take the time as variable structure health factor, the method comprises the following steps:

Step 2 one: Yi Yuewei unit, airborne wireless communication TU Trunk Unit is powered up, gather respectively the sequence { sequence { P} that AGG} and relaying power data form of the relaying AGG automatic gain data formation in m discontinuous independent month, acquisition m group relaying AGG automatic gain sequence AGG} and m group relaying power sequence P}, wherein m is for being more than or equal to 6 integers;

Step 2 two: respectively to m group relaying AGG automatic gain sequence AGG} and m group relaying power sequence P} carries out first-order difference, obtain differentiated m group automatic gain increment sequence AGG_d} and m group relaying power increment sequence P_d},

{AGG_d}＝AGG(i+1)-AGG(i)

{P_d}＝P(i+1)-P(i)

When { during AGG_d}=0, { AGG_d} and power increment sequence { in P_d}, make { relaying AGG automatic gain data and the relaying power data of AGG_d}=0 to reject this group automatic gain increment sequence;

Step 2 three: respectively to reject m group automatic gain increment sequence after data AGG_d} and m group power increment sequence P_d} carries out power increment calculating, obtain the m group sequence that power increment data y (i) corresponding to unit gain form y},

$y\left(i\right)=\frac{P\_d(i+t)}{\mathrm{AGG}\_d\left(i\right)}$

Wherein, the time span of t for postponing;

Step 2 four: Yi Yuewei unit, the power increment sequence corresponding according to m group unit gain y} obtain common m group sequence in each month the fluctuation characterization parameter var (y) of y}, i.e. variance:

$\mathrm{var}\left(y\right)=\mathrm{var}\left(\frac{P\_d(i+1)}{\mathrm{AGG}\_d\left(i\right)}\right)=\underset{i=1}{\overset{N}{\mathrm{\Σ}}}\frac{{(y_i-\stackrel{\‾}{y})}^2}{N-1}$

i＝1,2,...

Wherein, the number that N is data point, for the sequence { average of y};

Fluctuation characterization parameter var (y) is numbered according to the time span of its appearance, and time span is fluctuation characterization parameter var (y) place month span,

If fluctuation characterization parameter var (y) numbered sequence of corresponding month is that { at}, i.e. known month, disappearance month numbered sequence was { qt};

Step 2 five: { the fluctuation characterization parameter var (y) of y} carries out matching to m group sequence, obtain the variance curve expression formula after matching, respectively by known month numbered sequence at} with disappearance month numbered sequence for { qt} brings the variance expression formula of matching into, obtains the matching fluctuation characterization parameter var_f in known month _atmatching fluctuation characterization parameter var_f with disappearance month _qt;

Step 2 six: by the matching fluctuation characterization parameter var_f in the known month of the fluctuation characterization parameter var (y) obtaining in step 2 five and step 2 six acquisitions _atcarry out error of fitting calculating, obtain the error of fitting result sequence error in known month _at:

error _at＝var(y)-var_f _at；

Step 2 seven: the error of fitting result sequence error to known month _atcarry out test of normality, obtain the error of fitting result sequence error in known month _ataverage and standard deviation sigma _error,

$\stackrel{\‾}{\mathrm{error}}=\frac{\underset{i=1}{\overset{m}{\mathrm{\Σ}}}{\mathrm{error}}_i}{m}$

${\mathrm{\σ}}_{\mathrm{error}}=\sqrt{\frac{1}{m}\underset{i=1}{\overset{m}{\mathrm{\Σ}}}{({\mathrm{error}}_i-\stackrel{\‾}{\mathrm{error}})}^2}$

Wherein, error _ithe error of fitting result sequence error that represents known month _atin i error of fitting data;

Step sixteen: in average and standard deviation sigma _errorthe random error result sequence error that generates disappearance month in the data of normal distribution _qt;

Step 2 nine: by the matching fluctuation characterization parameter var_f in the disappearance month obtaining in step 2 six _qtthe error result sequence error in the disappearance month generating with step 2 nine _qtsuperpose, obtain the reconstruct fluctuation characterization parameter sequence var in disappearance month _qt

var _qt＝var_f _qt+error _qt；

Step 2 ten: the reconstruct fluctuation characterization parameter sequence var in the disappearance month that the fluctuation characterization parameter var (y) that step 2 five is obtained and step 2 ten obtain _qtcurve plotting after arranging according to the numbering in month, and using this curve as take the indirect health factor that the time is variable.

Take relaying AGG and relaying power is example, illustrates present embodiment, selects 7 discontinuous independent months of certain airborne wireless communication TU Trunk Unit, builds indirect health factor and the data reconstruction of based on the time, degenerating:

The structure of health factor and a upper joint are similar indirectly, and the extraction that comprises increment sequence, ratio are asked for and variance is calculated three links.Different, variable is the time here, therefore, considers data Yi Yuewei unit to divide into groups, and the ratio calculation data of every month are carried out to asking for of variance.Same, the computational methods of ratio are introduced the delay of time.Specific as follows:

Read by the testing time arrange amount to 21 relaying AGG automatic gains and relaying power, relaying AGG automatic gain be designated as AGG} sequence, relaying power is designated as { P} sequence;

According to test data place month data being cut apart, by 21 data sequence allocation 6 discontinuous independent months;

The relaying AGG automatic gain comprising in every group of data and relaying power are carried out respectively to the calculating of consecutive points first-order difference, suc as formula (1) and (2), obtain 6 pairs of { AGG_d} and { the P_d} sequences of correspondence mutually;

For the situation that there is no change in gain on interval, { during AGG_d}=0, reject power increment and the gain delta data of this test point, because when gain does not change, cannot determine that unit gain changes caused flash-up;

Data after rejecting are carried out to the corresponding power increment of unit gain and calculate, suc as formula (3).

If t gets 0, represent that correspondence is asked for one by one, if t gets non-zero positive integer, represent to introduce the time delay unit of account corresponding power increment sequence that gains, thereby obtain corresponding sequence { y};

Yi Yuewei unit, calculate the variance of the corresponding power increment sequence of unit gain in this month, it is fluctuation characterization parameter, can obtain altogether 6 data points, according to this data point curve plotting figure, as shown in Figure 4, as shown in Figure 5, the result of calculation of the two is as shown in table 3 for the datagraphic that contains time delay t=20:

Table 3 time variable indirect factor extraction-relaying AGG and relaying power

From Fig. 4 and Fig. 5, can intuitively find out, As time goes on, the fluctuation of the corresponding power increment of unit gain every month rises gradually, and variance presents certain ascendant trend, consistent with the expected results of analyzing.Meanwhile, contrasting two figure can find out, even more ideal and level and smooth through the ascendant trend of the variance sequence after delay disposal.The situation of ordering according to t=20 in Fig. 5, can find out, overall trend is comparatively desirable, and integral body is comparatively level and smooth, there is no the appearance of abnormity point.

Through above-mentioned steps, obtained 6 the variance result of calculations separately under different calculation methods, yet these data are far from being enough for prediction modeling.Meanwhile, time variable is continually varying, and therefore, it is connected before and after should being.So, after known test data is carried out to parameter extraction, need to during disappearance data be reconstructed and generate, namely during hypothesis disappearance test data, airborne wireless data communication TU Trunk Unit is under the prerequisite of normal work, according to Statistics, rebuild the data of disappearance part.Concrete steps are as follows:

The relaying AGG automatic gain data that adopt and relaying power data place within 6 months, be discrete, in March, 08, in April, 08 and in April, 10, in August, 10, in September, 10, in October, 10, its span is 31 months.By these data of 31 months respectively label be 1 to 31.Known test data corresponds respectively to label 1,2,25,29,30,31;

Abscissa is [1 2 25 29 30 31], 6 the variance data sequences of ordinate for obtaining in a upper link, based on least square method, realize curve, here, selection index form is carried out matching to the trend curve of the indirect factor, uses suc as formula the exponential function of (5) and portrays indirect health factor rule over time;

var_f＝a·e ^b·t (5)

In formula, var_f is the variance sequence that matching obtains, and t represents Time labeling, and value 1 to 31, a and b are undetermined parameter, according to the principle of least square, realizes and estimating.For the sequence of variance shown in Fig. 4, can obtain its fitting result as shown in Figure 6.

The expression formula that matching obtains is suc as formula shown in (6):

var_f＝0.50·e ^0.06·t (6)

Wherein, the confidential interval that parameter a fiducial probability is 95% is (0.45,1.45), parameter b fiducial probability is that 95% confidential interval is (0.01,0.12), the matching quality evaluation parameter of matched curve: SSE=1.255, R2=0.8524, RMSE=0.5601.Wherein R2 is more better close to 1 explanation fitting effect.

After matching, by formula (7) digital simulation error result.

error＝var-var_f (7)

Wherein var_f is the variance result that matching is obtained, and var is the true variance numerical value calculating.Calculate error result as shown in table 4.

The indirect factor matching of table 4 time variable and error

Obtain after error result of calculation, the statistical law of error is analyzed, first error information is carried out to test of normality, can obtain result shown in Fig. 7.Can find out, data point is distributed near straight line substantially, that is to say, substantially meets normal distribution law.Therefore, error of fitting is regarded as to the error sequence of Normal Distribution rule, as the foundation in follow-up error information generative process.

Further, the statistical law of error of calculation sequence, can obtain its standard deviation is 0.4989, average is-0.0419, follow-up by the error of fitting value of the Normal random sequence of this distribution of structure obedience data point as a supplement.

Obtain after the statistical law of error, all the other 25 data points of intercalary delection are reconstructed.The method of reconstruct is mainly the error term that superposes on the basis of matched curve, and this error term will be obtained according to the Statistical Distribution of matched curve and authentic testing data error.That is to say, generating and obeying average is-0.0419, and the normal random number certificate that standard deviation is 0.4989 carries out the error that stochastical sampling is obtained missing data part, generates result as shown in table 5.

The indirect factor error of table 5 time variable generates result

Obtain the error result of missing data part, this result and matched curve corresponding points are added to the supplementary data that can obtain disappearance part, finally can obtain the variance data of the continuous every month after missing data reconstruction, as shown in Figure 8.

After above-mentioned steps, can construct the complete data point of 31 months, as seen in Figure 8, the indirect factor of structure is along with the time presents ascendant trend, trend is obeyed exponential form, and in disappearance partial reconfiguration process, relying on the Statistical Distribution of ascendant trend and the error of fitting data of True Data, is the feasible simulation for the time of day of supposition continuous operation, therefore, the strategy of this structure is reasonable and effective.

For delay 20 data points, carry out the situation of ratio calculation, carry out identical processing, can obtain posttectonic indirect health factor curve chart; As shown in Figure 9.

By above-mentioned analysis, can know no matter whether postpone the processing of being divided by, all can obtain the indirect health factor with good degradation trend, and after certain delay disposal, the fluctuation of the health factor of structure declines to some extent, smoothness makes moderate progress.

Embodiment three: present embodiment is further illustrating the indirect health factor preparation method towards radio data-transmission equipment described in embodiment two, in present embodiment, variance curve expression formula after matching described in step 2 six, fluctuation characterization parameter is:

var_f＝a·e ^b·t

Wherein, var_f represents all month matching variances, and t represents the label in initial month to final month, and a and b are parameter.

Claims (3)

1. towards the indirect health factor preparation method of radio data-transmission equipment, it is characterized in that, the method be take and powered up number of times and build health factor as variable, and the method comprises the following steps:

Step is one by one: airborne wireless communication TU Trunk Unit is carried out powering up for n time, n is greater than 20 integer, gather respectively sequence { sequence that AGG} and relaying power data form { P}, acquisition n group relaying AGG automatic gain sequence { AGG} and n group relaying power sequence { P} that the relaying AGG automatic gain data while at every turn powering up form;

Step 1 two: respectively to n group relaying AGG automatic gain sequence AGG} and n group relaying power sequence P} carries out first-order difference, obtain differentiated n group automatic gain increment sequence AGG_d} and n group relaying power increment sequence P_d},

{AGG_d}＝AGG(i+1)-AGG(i) i＝1,2,...,n

{P_d}＝P(i+1)-P(i)

When { during AGG_d}=0, { AGG_d} and power increment sequence { in P_d}, make { relaying AGG automatic gain data and the relaying power data of AGG_d}=0 to reject this group automatic gain increment sequence;

Step 1 three: respectively to reject n group automatic gain increment sequence after data AGG_d} and n group power increment sequence P_d} carries out power increment calculating, obtain the n group sequence that power increment data y (i) corresponding to unit gain form y},

$y\left(i\right)=\frac{P\_d(i+t)}{\mathrm{AGG}\_d\left(i\right)}$

Wherein, the time span of t for postponing;

Step 1 four: the power increment sequence corresponding according to n group unit gain y} acquisition n group sequence the fluctuation characterization parameter var (y) of y}, i.e. variance:

$\mathrm{var}\left(y\right)=\mathrm{var}\left(\frac{P\_d(i+1)}{\mathrm{AGG}\_d\left(i\right)}\right)=\underset{i=1}{\overset{N}{\mathrm{\Σ}}}\frac{{(y_i-\stackrel{\‾}{y})}^2}{N-1}$

Wherein, the number that N is data point, for the sequence { average of y};

And { the fluctuation characterization parameter var (y) of y} is as the indirect health factor of radio data-transmission equipment using the n group sequence obtaining.

2. towards the indirect health factor preparation method of radio data-transmission equipment, it is characterized in that, the method be take the time as variable structure health factor, and the method comprises the following steps:

Step 2 one: Yi Yuewei unit, airborne wireless communication TU Trunk Unit is powered up, gather respectively the sequence { sequence { P} that AGG} and relaying power data form of the relaying AGG automatic gain data formation in m discontinuous independent month, acquisition m group relaying AGG automatic gain sequence AGG} and m group relaying power sequence P}, wherein m is for being more than or equal to 6 integers;

Step 2 two: respectively to m group relaying AGG automatic gain sequence AGG} and m group relaying power sequence P} carries out first-order difference, obtain differentiated m group automatic gain increment sequence AGG_d} and m group relaying power increment sequence P_d},

{AGG_d}＝AGG(i+1)-AGG(i)

{P_d}＝P(i+1)-P(i)

i＝1,2,...,n

When { during AGG_d}=0, { AGG_d} and power increment sequence { in P_d}, make { relaying AGG automatic gain data and the relaying power data of AGG_d}=0 to reject this group automatic gain increment sequence;

Step 2 three: respectively to reject m group automatic gain increment sequence after data AGG_d} and m group power increment sequence P_d} carries out power increment calculating, obtain the m group sequence that power increment data y (i) corresponding to unit gain form y},

$y\left(i\right)=\frac{P\_d(i+t)}{\mathrm{AGG}\_d\left(i\right)}$

Wherein, the time span of t for postponing;

Step 2 four: Yi Yuewei unit, the power increment sequence corresponding according to m group unit gain y} obtain common m group sequence in each month the fluctuation characterization parameter var (y) of y}, i.e. variance:

$\mathrm{var}\left(y\right)=\mathrm{var}\left(\frac{P\_d(i+1)}{\mathrm{AGG}\_d\left(i\right)}\right)=\underset{i=1}{\overset{N}{\mathrm{\Σ}}}\frac{{(y_i-\stackrel{\‾}{y})}^2}{N-1}$

Wherein, the number that N is data point, for the sequence { average of y};

Fluctuation characterization parameter var (y) is numbered according to the time span of its appearance, time span is fluctuation characterization parameter var (y) place month span, if fluctuation characterization parameter var (y) numbered sequence of corresponding month is { at}, be known month, disappearance month numbered sequence is { qt};

Step 2 five: { the fluctuation characterization parameter var (y) of y} carries out matching to m group sequence, obtain the variance curve expression formula after matching, respectively by known month numbered sequence at} with disappearance month numbered sequence for { qt} brings the variance expression formula of matching into, obtains the matching fluctuation characterization parameter var_f in known month _atmatching fluctuation characterization parameter var_f with disappearance month _qt;

Step 2 six: by the matching fluctuation characterization parameter var_f in the known month of the fluctuation characterization parameter var (y) obtaining in step 2 five and step 2 six acquisitions _atcarry out error of fitting calculating, obtain the error of fitting result sequence error in known month _at:

error _at＝var(y)-var_f _at；

Step 2 seven: the error of fitting result sequence error to known month _atcarry out test of normality, obtain the error of fitting result sequence error in known month _ataverage and standard deviation sigma _error,

$\stackrel{\‾}{\mathrm{error}}=\frac{\underset{i=1}{\overset{m}{\mathrm{\Σ}}}{\mathrm{error}}_i}{m}$

${\mathrm{\σ}}_{\mathrm{error}}=\sqrt{\frac{1}{m}\underset{i=1}{\overset{m}{\mathrm{\Σ}}}{({\mathrm{error}}_i-\stackrel{\‾}{\mathrm{error}})}^2}$

Wherein, error _ithe error of fitting result sequence error that represents known month _atin i error of fitting data;

Step sixteen: in average and standard deviation sigma _errorthe random error result sequence error that generates disappearance month in the data of normal distribution _qt;

Step 2 nine: by the matching fluctuation characterization parameter var_f in the disappearance month obtaining in step 2 six _qtthe error result sequence error in the disappearance month generating with step 2 nine _qtsuperpose, obtain the reconstruct fluctuation characterization parameter sequence var in disappearance month _qt

var _qt＝var_f _qt+error _qt；

Step 2 ten: the reconstruct fluctuation characterization parameter sequence var in the disappearance month that the fluctuation characterization parameter var (y) that step 2 five is obtained and step 2 ten obtain _qtcurve plotting after arranging according to the numbering in month, and using this curve as take the indirect health factor that the time is variable.

3. the indirect health factor preparation method towards radio data-transmission equipment according to claim 2, is characterized in that, the variance curve expression formula after the matching described in step 2 six, and fluctuation characterization parameter is:

var_f＝a·e ^b·t

Wherein, var_f represents all month matching variances, and t represents the label in initial month to final month, and a and b are parameter.