CN103646167A - Satellite abnormal condition detection system based on telemeasuring data - Google Patents
Satellite abnormal condition detection system based on telemeasuring data Download PDFInfo
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Abstract
The invention discloses a satellite abnormal condition detection system based on telemeasuring data. The system comprises a telemeasuring data preprocessing unit, a relevance calculation unit, an extreme point extraction unit, an abnormal condition extraction unit, a telemeasuring parameter original database, a relevance threshold database and an extreme point threshold database. The system fully utilizes a historical telemeasuring parameter sample, combines satellite telemeasuring parameter data characteristics and a data change law, compares the telemeasuring data in a period with relevance of the historical sample data and the extreme point errors, achieves satellite abnormal condition detection and extraction through a small amount of historical data without design knowledge, solves the problem that the existing abnormal state detection method depends on experiential knowledge of experts and abnormal change of telemeasuring parameter in the normal range cannot be solved through the existing detection method, and provides an effective and visual method and tool for satellite managers to analyze the satellite state change.
Description
Technical field
The present invention relates to a kind of satellite abnormal state detection system, relate in particular to a kind of satellite abnormal state detection system based on telemetry, belong to prognostic and health management technical field.
Background technology
Telemetry is to embody the whether normal important evidence of satellite transit state.In conjunction with telemetry, Satellite operation management personnel, domain expert and designer can find misoperation situation, analysis abnormal cause, the improvement design of satellites of satellite exactly, thereby improve overall design and the operation and management level of satellite.Satellite abnormal state detection method and apparatus based on telemetry, mainly utilize the historical telemetry of satellite, in conjunction with TT&C event and quantity of state variation in the recent period, by comparing the difference of data to be tested and normal sample notebook data, the state mutation that completes satellite detects.
In current mutation detection method, there is the mutation detection method based on statistical distribution, for example utilize the parameter rate of change scope of statistics to carry out mutation detection; There is the mutation detection method based on knowledge, for example, utilize parameter designing bound to carry out mutation detection.The mutation that these methods can both realize data in some aspects detects, but need a large amount of historical datas to add up or need to be grasped system knowledge, and can only judge the one point data in a certain moment, cannot detect the ANOMALOUS VARIATIONS not transfiniting, there is certain limitation.
Summary of the invention
Technology of the present invention is dealt with problems and is: overcome the deficiencies in the prior art, a kind of satellite abnormal state detection system based on telemetry is provided, improved detection efficiency and accuracy.
Technical solution of the present invention is: a kind of satellite abnormal state detection system based on telemetry, comprises telemetry pretreatment unit, correlation calculating unit, extreme point extraction unit, abnormality extraction unit, telemetry parameter raw data base, degree of correlation threshold data storehouse, extreme point threshold data storehouse;
Telemetry parameter raw data base, the historical telemetry of storage telemetry parameter;
Degree of correlation threshold data storehouse, storage degree of correlation threshold value;
Extreme point threshold data storehouse, storage extreme point threshold value;
Telemetry pretreatment unit, the historical telemetry sequence that in the telemetry sequence to be detected of special time length and telemetry parameter raw data base, same time is corresponding is carried out to pre-service, and pre-service content comprises error code and singular point rejecting, mean value computation and null value filling;
Correlation calculating unit; Correlation coefficient process to pretreated telemetry sequence to be detected and historical telemetry sequence calculates, obtain the related coefficient of telemetry sequence to be detected and historical telemetry sequence, the degree of correlation threshold value of storing in the related coefficient obtaining and degree of correlation threshold data storehouse is compared, according to comparative result, judge which telemetry parameter exists abnormal, the telemetry parameter that wherein related coefficient is greater than degree of correlation threshold value is considered as extremely, otherwise is considered as normal;
Extreme point extraction unit, to there is abnormal telemetry parameter, extract telemetry sequence to be detected that abnormal telemetry parameter is corresponding and the extreme point in historical telemetry sequence, by linear interpolation, mend point methods the extreme point of two corresponding time points of telemetry sequence is integrated, make telemetry parameter sequence to be detected identical with the extreme point number in historical telemetry parameter sequence;
Abnormality extraction unit, the relative error of extreme point in two telemetry sequences of node-by-node algorithm, the extreme point threshold value of storing in the relative error obtaining and extreme point threshold data storehouse is compared, wherein to be greater than the extreme point of extreme point threshold value be abnormal extreme point to relative error, according to abnormal extreme point, extract the abnormal time section of telemetry, the telemetry of abnormal time section is carried out to analysis of trend and realize satellite abnormal state detection.
The described method that the correlation coefficient process of pretreated telemetry sequence to be detected and historical telemetry sequence is calculated is: the computing formula of related coefficient Cov is as follows:
Wherein, x is telemetry sequence to be detected, and y is historical telemetry sequence,
be respectively the average of sequence x, y, the span of related coefficient Cov is [1,1].
In described telemetry sequence x to be detected, the extracting method of extreme point is:
In historical telemetry sequences y, the extracting method of extreme point is:
1<i<N
Wherein, x is telemetry sequence to be detected, and y is historical telemetry sequence, and N is sequence length.
The present invention's beneficial effect is compared with prior art as follows: the satellite abnormal state detection system based on telemetry provided by the invention, make full use of historical telemetry parameter sample, in conjunction with satellite telemetry parameters data characteristics and data variation rule, by the comparison of the degree of correlation comparison of the telemetry of a period of time and its historical sample data and extreme point error, realize and do not need design knowledge, only need satellite abnormal state detection and the extraction of a small amount of historical data, detection efficiency and accuracy have been improved, thereby overcome existing abnormal state detection method and cannot solve to the dependency problem of expertise knowledge and existing detection method the ANOMALOUS VARIATIONS problem that telemetry parameter does not exceed normal range, for analyzing satellitosis variation, Satellite Management personnel provide effective, Method and kit for intuitively.
Accompanying drawing explanation
Fig. 1 is overhaul flow chart of the present invention;
Fig. 2 is that detection system of the present invention forms structural drawing;
The extreme value sequence integration process schematic diagram that Fig. 3 provides for the embodiment of the present invention.
Embodiment
Below in conjunction with the accompanying drawing in the embodiment of the present invention, the technical scheme in the embodiment of the present invention is clearly and completely described, obviously, described embodiment is only the present invention's part embodiment, rather than whole embodiment.Embodiment based in the present invention, those of ordinary skills, not making the every other embodiment obtaining under creative work prerequisite, belong to the scope of protection of the invention.
The core concept that realizes of satellite abnormal state detection system of the present invention is: satellite telemetry parameters has certain Changing Pattern under normal condition, by the sample data under parameter data to be tested and parameter normal condition is compared, realize detection and the extraction of the satellite abnormality that does not rely on expertise.
The principle of the method for the invention is: by reference to the accompanying drawings 1, the present invention is directed to the problem of utilizing the historical telemetry of satellite to realize detection and the extraction of satellite abnormality, first by loading telemetry parameter at the appointed time telemetry to be detected and the historical telemetry of section, the historical data of this telemetry parameter and telemetry to be detected are carried out to the pre-service such as error code and singular point rejecting, mean value computation, null value filling; Secondly, after the pre-service that completes data, utilize correlation coefficient process whether telemetry parameter is existed extremely and judged, correlation coefficient threshold can obtain by the training to historical data; Again, to there is abnormal telemetry parameter, extract the extreme point of its data to be tested and historical sample data thereof and integrate; Finally, by the mode extracting parameter abnormal time section of point-by-point comparison extreme point error, and the variation tendency of abnormality is analyzed, wherein extreme point relative error threshold value can be trained and be obtained by historical data.
Describe implementation procedure of the present invention below in detail, as shown in Figure 2, the invention provides a kind of satellite abnormal state detection system based on telemetry, comprising: telemetry pretreatment unit, correlation calculating unit, extreme point extraction unit, abnormality extraction unit, telemetry parameter raw data base, degree of correlation threshold data storehouse, extreme point threshold data storehouse; Telemetry parameter raw data base, the historical telemetry of storage telemetry parameter; Degree of correlation threshold data storehouse, storage degree of correlation threshold value; Extreme point threshold data storehouse, storage extreme point threshold value;
Telemetry pretreatment unit, the historical telemetry sequence that in the telemetry sequence to be detected of special time length and telemetry parameter raw data base, same time is corresponding is carried out to pre-service, and pre-service content comprises error code and singular point rejecting, mean value computation and null value filling;
Correlation calculating unit; Correlation coefficient process to pretreated telemetry sequence to be detected and historical telemetry sequence calculates, obtain the related coefficient of telemetry sequence to be detected and historical telemetry sequence, the degree of correlation threshold value of storing in the related coefficient obtaining and degree of correlation threshold data storehouse is compared, according to comparative result, judge which telemetry parameter exists abnormal, the telemetry parameter that wherein related coefficient is greater than degree of correlation threshold value is considered as extremely, otherwise is considered as normal;
Extreme point extraction unit, to there is abnormal telemetry parameter, extract telemetry sequence to be detected that abnormal telemetry parameter is corresponding and the extreme point in historical telemetry sequence, by linear interpolation, mend point methods the extreme point of two corresponding time points of telemetry sequence is integrated, make telemetry parameter sequence to be detected identical with the extreme point number in historical telemetry parameter sequence;
Abnormality extraction unit, the relative error of extreme point in two telemetry sequences of node-by-node algorithm, the extreme point threshold value of storing in the relative error obtaining and extreme point threshold data storehouse is compared, wherein to be greater than the extreme point of extreme point threshold value be abnormal extreme point to relative error, according to abnormal extreme point, extract the abnormal time section of telemetry, the telemetry of abnormal time section is carried out to analysis of trend and realize satellite abnormal state detection.
To telemetry pretreatment unit, after the telemetry parameter that obtains satellite telemetry to be detected and historical telemetry, first the error code existing in telemetry is identified and rejected, and this data point is set to sky; Singular point is rejected main telemetry serial mean and the variance yields that calculates telemetry parameter that adopt, the mode of adding and subtracting three times of variance yields by mean value determine telemetry parameter 95% data should in data area, thereby eliminate the not singular point data within the scope of this, mainly to utilize the mean value of telemetry to be detected and three times of variances to form whether a historical telemetry is the judgement threshold range of singular value, when historical telemetry is not during in this scope, this data dot values is considered as to singular value point, now directly this data point is set to sky; Minute average of telemetry after calculating rejecting error code and singular point; For null value point data, to utilize the method for linear interpolation to carry out benefit value to null value point, thereby form a complete ordered series of numbers, the missing data section of supposing telemetry sequence is x
t, x
t+1..., x
t+M-1, a linear interpolation data benefit point formula is:
Wherein, i=0,1,2 ..., M-1, M is disappearance telemetry length, x is telemetry parameter sequence to be detected, x
t, x
t+1..., x
t+M-1for disappearance telemetry, x
t-1, x
t+Mbe respectively the previous data of disappearance telemetry section beginning, a rear telemetry at disappearance telemetry section end.
For correlation calculating unit; Obtain the degree of correlation of telemetry sequence to be detected and historical telemetry sequence, related coefficient is more larger close to the degree of correlation of 1, two sequence; Related coefficient is more less close to the degree of correlation of 0, two sequence.By the serial correlation coefficient calculating and degree of correlation threshold value are compared, show whether parameter exists abnormal conclusion.The Calculation of correlation factor formula of telemetry sequence x to be detected, historical telemetry sequences y is as follows:
Wherein, x is telemetry sequence to be detected, and y is historical telemetry sequence,
be respectively sequence x, y average, related coefficient Cov span [1,1], if Cov is less than setting threshold, thinks that the degree of correlation of two sequences is lower, and parameter exists abnormal.
For extreme point extraction unit: to existing telemetry sequence to be detected and the historical telemetry sequence of abnormal parameter to carry out respectively the extreme point feature extraction based on traversal search, and mend and some the extreme point of the corresponding time point of two sequences is integrated by linear interpolation, make sequence to be detected identical with sample sequence extreme point number.Local extremum point reflection the Changing Pattern of parameter local detail, utilize the thought of differentiate, if there is extreme point, extreme point both sides variation tendency is different, differentiate result is inevitable one positive one negative.Each parameter pointwise in telemetry after pre-service is compared to calculating, to obtain the numerical point that meets this feature, i.e. extreme point.
The extreme point determination methods of telemetry sequence x to be detected is shown below:
The extreme point determination methods of historical telemetry sequences y is as follows:
Wherein, x is telemetry sequence to be detected, and y is historical telemetry sequence, and N is sequence length.
Extreme point sequence is carried out to integration process: first by the two poles of the earth value sequence relatively, search and whether have the there is identical horizontal ordinate extreme point of (as the b in Fig. 3), then other points are carried out to corresponding benefit value.Integration process schematic diagram as shown in Figure 3, carries out benefit value to the b point place in extreme point sequence to be detected, establish integrate before telemetry sequence a to be detected, c two point coordinate be respectively x
a, x
c, historical telemetry sequence a, c two point coordinate are y respectively
a, y
c, the computing formula that b is ordered is as follows:
Wherein, x is telemetry sequence to be detected, and y is historical telemetry sequence.
For abnormality extraction unit: in two telemetry parameter sequences of node-by-node algorithm, the method for the relative error of extreme point is: as follows in the computing formula of b point extreme point relative error in Fig. 3:
Wherein, E is extreme point relative error, x
b, y
bbe respectively telemetry extrema in a sequence point coordinate to be detected and historical telemetry extrema in a sequence point coordinate through integrating.If relative error E is greater than set threshold value, thinks that this extreme point place is for abnormal, otherwise think normal.
Calculate the distance between adjacent two trouble spots, if be greater than set distance threshold, think that two trouble spots belong to respectively two faulty sections, otherwise think that two trouble spots belong to a faulty section, finally obtain the time period information that fault occurs.
For example: the telemetry of analyzing satellite telemetry parameters, feature according to telemetry parameter, select Yi Tianwei unit to carry out mean value computation and variance yields calculating to data, avoided due to satellite in short-term state variation cause short time data and other constantly data variation cause more greatly average to depart from short time data, thereby can retain better the real historical data of telemetry parameter, reject the singular point in telemetry parameter.After having rejected singular point, telemetry changes relatively mild, and the method that is now applicable to employing linear interpolation realizes the filling of null value point.The correlation coefficient threshold of the dissimilar parameter of the different subsystems of satellite may be different, and its correlation coefficient threshold can be by obtaining the training of parameter historical data.The present invention mends point and integrates by the extreme point of parameter data to be tested and sample data being carried out to interpolation, avoids occurring that extreme point position difference cannot calculate the problem of relative error.In addition, the relative error threshold value of different telemetry parameter telemetry extreme points can obtain by the training to historical data.
The present invention further has following characteristics: described correlation calculating unit, the degree of correlation threshold value of different parameters can be trained and be obtained by historical data, also can artificially set by experience, thereby improve the accuracy that the degree of correlation is judged.Extreme point extraction unit can carry out interpolation to the extreme point of parameter data to be tested and sample data to be mended point and integrates, and avoids occurring that extreme point position difference cannot calculate the problem of relative error.Abnormality extraction unit, the extreme point relative error threshold value of different parameters can be trained and be obtained by historical data, also can artificially set by experience, can improve the accuracy of abnormal parameters time period extraction.
The present invention not detailed description is known to the skilled person technology.
Claims (3)
1. the satellite abnormal state detection system based on telemetry, is characterized in that comprising: telemetry pretreatment unit, correlation calculating unit, extreme point extraction unit, abnormality extraction unit, telemetry parameter raw data base, degree of correlation threshold data storehouse, extreme point threshold data storehouse;
Telemetry parameter raw data base, the historical telemetry of storage telemetry parameter;
Degree of correlation threshold data storehouse, storage degree of correlation threshold value;
Extreme point threshold data storehouse, storage extreme point threshold value;
Telemetry pretreatment unit, the historical telemetry sequence that in the telemetry sequence to be detected of special time length and telemetry parameter raw data base, same time is corresponding is carried out to pre-service, and pre-service content comprises error code and singular point rejecting, mean value computation and null value filling;
Correlation calculating unit; Correlation coefficient process to pretreated telemetry sequence to be detected and historical telemetry sequence calculates, obtain the related coefficient of telemetry sequence to be detected and historical telemetry sequence, the degree of correlation threshold value of storing in the related coefficient obtaining and degree of correlation threshold data storehouse is compared, according to comparative result, judge which telemetry parameter exists abnormal, the telemetry parameter that wherein related coefficient is greater than degree of correlation threshold value is considered as extremely, otherwise is considered as normal;
Extreme point extraction unit, to there is abnormal telemetry parameter, extract telemetry sequence to be detected that abnormal telemetry parameter is corresponding and the extreme point in historical telemetry sequence, by linear interpolation, mend point methods the extreme point of two corresponding time points of telemetry sequence is integrated, make telemetry parameter sequence to be detected identical with the extreme point number in historical telemetry parameter sequence;
Abnormality extraction unit, the relative error of extreme point in two telemetry sequences of node-by-node algorithm, the extreme point threshold value of storing in the relative error obtaining and extreme point threshold data storehouse is compared, wherein to be greater than the extreme point of extreme point threshold value be abnormal extreme point to relative error, according to abnormal extreme point, extract the abnormal time section of telemetry, the telemetry of abnormal time section is carried out to analysis of trend and realize satellite abnormal state detection.
2. a kind of satellite abnormal state detection system based on telemetry as claimed in claim 1, is characterized in that: the described method that the correlation coefficient process of pretreated telemetry sequence to be detected and historical telemetry sequence is calculated is: the computing formula of related coefficient Cov is as follows:
Wherein, x is telemetry sequence to be detected, and y is historical telemetry sequence,
be respectively the average of sequence x, y, the span of related coefficient Cov is [1,1].
3. a kind of satellite abnormal state detection system based on telemetry as claimed in claim 1, is characterized in that: in described telemetry sequence x to be detected, the extracting method of extreme point is:
In historical telemetry sequences y, the extracting method of extreme point is:
1<i<N
Wherein, x is telemetry sequence to be detected, and y is historical telemetry sequence, and N is sequence length.
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