CN112464140B - Satellite power supply system health assessment method and system based on gradient threshold - Google Patents
Satellite power supply system health assessment method and system based on gradient threshold Download PDFInfo
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Abstract
The invention provides a satellite power system health assessment method and system based on a gradient threshold, comprising the following steps: acquiring remote measurement parameters of a satellite power system, and preprocessing the acquired remote measurement parameters of the satellite power system; identifying a remote parameter gradient change point based on the preprocessed remote parameter and calculating a remote parameter gradient; carrying out data smoothing calculation on the preprocessed telemetering parameters by using a local weighted regression smoothing method, converting the telemetering parameters into a telemetering curve, and calculating the slope of a gradient change point position curve; recording all parameter gradients corresponding to the normal operation data of the satellite in a preset time and corresponding curve slopes, and respectively recording the maximum value and the minimum value; in the in-orbit operation process of the satellite, when a gradient change point is generated by the telemetering parameter, one evaluation operation is triggered, the parameter gradient and the curve slope are calculated, and when the gradient and the curve slope both exceed the maximum value in normal operation, the health state evaluation is triggered. The method has the characteristics of less occupied resources, strong real-time performance and the like.
Description
Technical Field
The invention relates to the technical field of satellite power systems, in particular to a satellite power system health assessment method and system based on a gradient threshold value, and more particularly to a satellite power system health assessment method and system based on gradient identification and threshold value judgment.
Background
Satellites are an important class of spacecraft that need to have high reliability to ensure successful space mission. Because the space environment is complicated, the satellite inevitably has certain faults, and the satellite power supply system is used as an energy supply system of the satellite, so that the satellite works abnormally and even the whole satellite is paralyzed if the faults occur. The spacecraft health assessment technology is one of key technologies for guaranteeing reliable and stable operation of a spacecraft in orbit, and the health assessment mainly carries out comprehensive analysis on monitoring data, historical data and the like obtained by various means, utilizes various predictive diagnosis algorithms to mine equipment health state information and change trends thereof reflected by the data, and conjectures existing or possible fault modes. The health assessment technology provides decision support for on-orbit task planning, fault processing and maintenance and the like of the spacecraft, and has very important significance for effectively improving the survival capability of the spacecraft.
Patent document CN108594631A (application number: 201810256751.8) discloses a GNSS time service performance evaluation method, in which a GNSS timing receiver is placed in a national time service center, and pseudo-range output by the receiver is corrected through parameters such as a tracing model, a satellite clock model, a transmitting channel bias, a receiving end delay and the like, so as to obtain UTC time of satellite broadcasting; meanwhile, UTC-UTC (NTSC) data issued by the BIPM T bulletin are utilized to obtain the actual UTC time. And comparing the time difference between the satellite broadcast UTC time and the actual UTC time to obtain the time service error obtained by the user. According to the method, the time difference between satellite broadcast UTC time and actual UTC time is compared to obtain the time service error obtained by a user, and the time service performance of a single satellite is evaluated.
Patent document CN108462467A (application number: 201711375139.4) discloses a novel solar wing on-track output power evaluation method under power topology, which realizes the on-track power evaluation of a new generation PCU power topology solar battery array by measuring and calculating parameters of each part of the solar battery array, is used for filling the blank problem that the solar wing on-track power evaluation method in the prior art cannot adapt to the new generation PCU, determines the on-track power output capability of the solar battery array in real time by using on-track telemetering parameters such as whole satellite load current, an array splitting and shunt regulation state, solar battery array current, track position, date and the like, and effectively verifies the result of ground design, and has high reliability and precision. The on-orbit power output capability of the solar cell array is determined in real time by utilizing on-orbit telemetering parameters such as whole satellite load current, an array shunt regulation state, solar cell array current, an orbit position, date and the like.
At present, in a health assessment algorithm in the aerospace field, threshold judgment is a core idea of an assessment technology. The health degree is evaluated by comparing the evaluation state with a set threshold value. However, in the environment of in-orbit operation of the actual satellite, the parameter value sensitivity of part of single machines is far lower than the gradient sensitivity of the single machines, and for the single machines of the type, the traditional threshold value judgment method has certain limitation.
Aiming at the single machines which can not realize the evaluation function by taking threshold judgment as a core thought technology, health evaluation needs to be completed aiming at single machine parameter gradient, single machine parameter gradient identification is prepared, and alarm is completed in the abnormal state of the parameter gradient. Aiming at the blank existing in the current evaluation technology, the invention provides a satellite power supply system health evaluation technology based on gradient identification and threshold judgment.
Disclosure of Invention
Aiming at the defects in the prior art, the invention aims to provide a satellite power system health assessment method and system based on a gradient threshold.
The invention provides a satellite power system health assessment method based on a gradient threshold, which comprises the following steps:
step M1: acquiring telemetry parameters of a satellite power system, and preprocessing the acquired telemetry parameters of the satellite power system to obtain preprocessed telemetry parameters;
Step M2: identifying a remote parameter gradient change point based on the preprocessed remote parameter and calculating a remote parameter gradient;
step M3: carrying out data smoothing calculation on the preprocessed telemetering parameters by using a local weighted regression smoothing method, converting the telemetering parameters into a telemetering curve, and calculating the slope of a gradient change point position curve;
step M4: recording all parameter gradients and corresponding curve slopes corresponding to normal satellite operation data within preset on-orbit operation time of the satellite, and respectively recording a maximum value and a minimum value;
step M5: in the in-orbit operation process of the satellite, when a gradient change point is generated by the telemetering parameter, one evaluation operation is triggered, the parameter gradient and the curve slope are calculated, and when the gradient and the curve slope both exceed the maximum value in normal operation, the health state evaluation is triggered.
Preferably, the step M1 includes:
step M1.1: resampling the remote parameter data at equal time intervals;
step M1.2: calculating the mean value mean and the standard deviation sigma of the remote parameters in a unit time period, replacing parameter values exceeding [ mean-sigma, mean + sigma ] with values of the previous moment, and acquiring preprocessed remote parameter data;
step M1.3: and replacing the data which do not conform to the parameter range of the illumination area in the preprocessed remote parameter data with null values, and defining the remote parameter state as a shadow area.
Preferably, the step M2 includes:
step M2.1: let the remote parameter value be n 1 、n 2 、…、n m Gradient change sequence is n 2 -n 1 、n 3 -n 2 、…、n m -n m-1 ；
Step M2.2: identifying non-zero points N in a gradient change sequence 1 、N 2 、…N p And recording the corresponding time stamp T 1 、T 2 、…、T p ；
Step M2.3: calculating the corresponding remote parameter gradient of non-zero point as (N) p -N p-1 )/(T p -T p-1 )。
Preferably, the step M5 includes:
step M5.1: judging whether the gradient and the slope of the curve exceed the maximum value of normal operation or not, and if not, the health degree is 1;
step M5.2: when the gradient and the slope of the curve exceed the maximum value of normal operation, the inverse tangent normalization formula normalizes the degree of exceeding the maximum value into a numerical value of 0-1 to represent the health degree.
Preferably, said step M5.2 comprises:
x*＝atan(x)*2/π
wherein x represents the gradient corresponding to the gradient change point and the slope of the curve, and x represents the health degree.
The invention provides a satellite power system health assessment system based on a gradient threshold, which comprises:
module M1: acquiring telemetry parameters of a satellite power system, and preprocessing the acquired telemetry parameters of the satellite power system to obtain preprocessed telemetry parameters;
module M2: identifying a remote parameter gradient change point based on the preprocessed remote parameter and calculating a remote parameter gradient;
module M3: carrying out data smoothing calculation on the preprocessed telemetering parameters by using a local weighted regression smoothing method, converting the telemetering parameters into a telemetering curve, and calculating the slope of a gradient change point position curve;
Module M4: recording all parameter gradients and corresponding curve slopes corresponding to normal satellite operation data within preset on-orbit operation time of the satellite, and respectively recording a maximum value and a minimum value;
module M5: in the in-orbit operation process of the satellite, when a gradient change point is generated by the telemetering parameter, one evaluation operation is triggered, the parameter gradient and the curve slope are calculated, and when the gradient and the curve slope both exceed the maximum value in normal operation, the health state evaluation is triggered.
Preferably, said module M1 comprises:
module M1.1: resampling the remote parameter data at equal time intervals;
module M1.2: calculating the mean value mean and the standard deviation sigma of the remote parameters in a unit time period, replacing parameter values exceeding [ mean-sigma, mean + sigma ] with values of the previous moment, and acquiring preprocessed remote parameter data;
module M1.3: and replacing the data which do not conform to the parameter range of the illumination area in the preprocessed remote parameter data with null values, and defining the remote parameter state as a shadow area.
Preferably, said module M2 comprises:
module M2.1: let the remote parameter value be n 1 、n 2 、…、n m Gradient change sequence is n 2 -n 1 、n 3 -n 2 、…、n m -n m-1 ；
Module M2.2: identifying non-zero points N in a gradient change sequence 1 、N 2 、…N p And recording the corresponding time stamp T 1 、T 2 、…、T p ；
Module M2.3: calculating the corresponding remote parameter gradient of non-zero point as (N) p -N p-1 )/(T p -T p-1 )。
Preferably, said module M5 comprises:
module M5.1: judging whether the gradient and the slope of the curve exceed the maximum value of normal operation or not, and if not, the health degree is 1;
module M5.2: when the gradient and the slope of the curve exceed the maximum value of normal operation, the inverse tangent normalization formula normalizes the degree of exceeding the maximum value into a numerical value of 0-1 to represent the health degree.
Preferably, said module M5.2 comprises:
x*＝atan(x)*2/π
wherein x represents the gradient corresponding to the gradient change point and the slope of the curve, and x represents the health degree.
Compared with the prior art, the invention has the following beneficial effects:
1. the method carries out health assessment on the gradient sensitive single machine of the satellite power supply system by combining the satellite remote reference step gradient and the fitting change rate, not only solves the problem that the health assessment technology has no margin for the assessment of the gradient sensitive single machine, but also has the characteristics of less computing occupied resources, strong real-time performance and the like.
2. The invention can realize the real-time on-line monitoring of the gradient sensitive component of the orbiting satellite by utilizing very small monitoring and calculating resources;
3. the invention provides a condition triggering evaluation mechanism, realizes the real-time monitoring of single variable quantity in the running process, has simple monitoring mechanism and extremely small occupied computing resource.
4. Once the evaluation condition is triggered, the evaluation operation can be immediately carried out, and the accurate health state evaluation can be carried out. The method realizes effective combination of low operation resources and high evaluation effect, and well meets the requirements of real-time, low operation cost and high evaluation precision of satellite power supply system state evaluation.
Drawings
Other features, objects and advantages of the invention will become more apparent upon reading of the detailed description of non-limiting embodiments with reference to the following drawings:
FIG. 1 is a general architecture diagram of a satellite power system health assessment technique based on gradient identification and threshold determination;
FIG. 2 is a diagram of a satellite power system remote reference original signal;
FIG. 3 is a diagram of remote reference data preprocessing;
FIG. 4 is a gradient non-zero point identification graph;
FIG. 5 is a gradient calculation chart;
FIG. 6 is a smoothing calculation graph;
FIG. 7 is a graph of slope calculations;
fig. 8 is a diagram of the results of the health assessment calculation.
Detailed Description
The present invention will be described in detail with reference to specific examples. The following examples will assist those skilled in the art in further understanding the invention, but are not intended to limit the invention in any way. It should be noted that it would be obvious to those skilled in the art that various changes and modifications can be made without departing from the spirit of the invention. All falling within the scope of the present invention.
Example 1
In order to solve the limitation of the traditional threshold judgment detection and evaluation method on the telemetering parameters of the in-orbit satellite part in health management, the invention provides a satellite health evaluation technical method based on gradient identification and threshold judgment, and aims to solve the problems.
The invention provides a health assessment technology aiming at a single machine with gradient sensitivity characteristic in the in-orbit operation of a satellite. The method and the device simultaneously consider the parameter value and the parameter change rate of the satellite power supply system, can automatically identify the parameter step point, calculate the parameter gradient, determine the state and degree of the gradient exceeding the threshold value by a threshold value judging method, automatically cut off the parameter change process by the parameter step point, and further realize qualitative and quantitative evaluation of the health state of the satellite power supply system.
The method can be used for health state evaluation of satellite single-machine telemetering data of positive gradient sensitivity type, negative gradient sensitivity type, step sensitivity type and the like. The telemetry data to be detected is required to have the following characteristics:
1. the sensitivity of the single machine to the parameter values is not higher than the sensitivity of the parameter value variation;
2. the parameter values are continuous variables, equally spaced sampling variables or regularized resampling variables.
The invention provides a technical method which occupies less computing resources and realizes better health state health assessment. The method ensures real-time state monitoring of the components at low health cost by monitoring gradient sensitive parameters (such as temperature, sudden temperature rise, short circuit and ablation of a representation component) of the satellite power supply system in real time, triggers an evaluation program once the gradient sensitive parameter mutation is identified, determines the position of the parameter mutation and determines the mutation degree through an evaluation method so as to realize quantitative evaluation of the health state of the satellite.
According to the satellite power system health assessment technology based on gradient identification and threshold judgment, real-time monitoring of gradient sensitivity performance of a satellite power system and accurate health state assessment can be achieved, and large performance assessment benefits are achieved with low monitoring cost.
According to the method for evaluating the health of the satellite power system based on the gradient threshold, as shown in fig. 1, the method comprises the following steps:
step M1: acquiring telemetry parameters of a satellite power system, and preprocessing the acquired telemetry parameters of the satellite power system to obtain preprocessed telemetry parameters;
step M2: identifying a remote parameter gradient change point based on the preprocessed remote parameter and calculating a remote parameter gradient;
Step M3: carrying out data smoothing calculation on the preprocessed telemetering parameters by using a local weighted regression smoothing method, converting the telemetering parameters into a telemetering curve, and calculating the slope of a gradient change point position curve;
step M4: recording all parameter gradients and corresponding curve slopes corresponding to normal satellite operation data of the satellite in orbit for 6-18 months, and respectively recording the maximum value and the minimum value;
step M5: in the in-orbit operation process of the satellite, when a gradient change point is generated by the telemetering parameter, one evaluation operation is triggered, the parameter gradient and the curve slope are calculated, and when the gradient and the curve slope both exceed the maximum value in normal operation, the health state evaluation is triggered.
Specifically, the step M1 includes:
step M1.1: the satellite remote parameter data has an unstable sampling interval, and on the basis, the remote parameter data is resampled at equal time intervals;
step M1.2: calculating mean value mean and standard deviation sigma of the remote parameter in unit time period (hour, day or month), replacing parameter values exceeding mean-sigma, mean + sigma with values of the previous moment, and acquiring preprocessed remote parameter data, wherein null values, wild values and random values in the remote parameters are removed by the method;
Step M1.3: and replacing the data which do not conform to the parameter range of the illumination area in the preprocessed remote parameter data with null values, and defining the remote parameter state as a shadow area.
Specifically, the step M2 includes:
step M2.1: let the remote parameter value be n 1 、n 2 、…、n m Gradient change sequence is n 2 -n 1 、n 3 -n 2 、…、n m -n m-1 ；
Step M2.2: identifying non-zero points N in a gradient change sequence 1 、N 2 、…N p And recording the corresponding time stamp T 1 、T 2 、…、T p ；
Step M2.3: calculating the corresponding remote parameter gradient of non-zero point as (N) p -N p-1 )/(T p -T p-1 )。
Specifically, the step M5 includes:
step M5.1: judging whether the gradient and the slope of the curve exceed the maximum value of normal operation or not, and if not, the health degree is 1;
step M5.2: when the gradient and the slope of the curve exceed the maximum value of normal operation, the inverse tangent normalization formula normalizes the degree of exceeding the maximum value into a numerical value of 0-1 to represent the health degree.
In particular, said step M5.2 comprises:
x*＝atan(x)*2/π
wherein x represents the gradient corresponding to the gradient change point and the slope of the curve, and x represents the health degree.
Example 2
Example 2 is a modification of example 1
The implementation flow of the patent method is described by taking certain type of satellite shunt parameter data as an example. The shunt has the characteristic of temperature change sensitivity, the temperature parameter of the shunt is a typical forward gradient sensitive type telemetering parameter and is mainly characterized by being sensitive to the excessively fast temperature rise of a single machine, and the method provided by the patent is applied to realize continuous health state evaluation of single machine data of the shunt by taking day as a unit, and comprises the following specific steps:
The temperature remote parameter of the current divider of the satellite at a certain day is called, and the raw data is shown in the attached figure 2.
And (3) resampling the temperature data of the shunt at equal intervals by taking 1s as a unit, and eliminating shadow region and field data to finish data preprocessing, wherein the result is shown in figure 3.
Calculating the remote parameter gradient, identifying a non-zero point and calculating the remote parameter gradient corresponding to the non-zero point, wherein the identification result of the gradient non-zero point is shown in figure 4. The results of the gradient calculations are shown in FIG. 5.
And carrying out data smoothing on the telemetering parameters by using a local weighted regression smoothing method, converting the telemetering parameters into a telemetering curve, and calculating the slope of the position curve of the gradient change point, wherein the smooth calculation result is shown in figure 6, and the slope result is shown in figure 7.
And (3) calling data of 6 th to 18 th months of on-orbit operation of the satellite of the model, and calculating all parameter gradients and corresponding maximum values of curve slopes to be 0.003/s and 0.0025/s according to the steps 1 to 3.
The health degree overrun result of 20xx year, 3 month and 2 days calculated according to the fifth step is shown in fig. 8, and the health evaluation result is that the health degree is 0.933.
Those skilled in the art will appreciate that, in addition to implementing the systems, apparatus, and various modules thereof provided by the present invention in purely computer readable program code, the same procedures can be implemented entirely by logically programming method steps such that the systems, apparatus, and various modules thereof are provided in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers and the like. Therefore, the system, the device and the modules thereof provided by the present invention can be considered as a hardware component, and the modules included in the system, the device and the modules thereof for implementing various programs can also be considered as structures in the hardware component; modules for performing various functions may also be considered to be both software programs for performing the methods and structures within hardware components.
The foregoing description has described specific embodiments of the present invention. It is to be understood that the present invention is not limited to the specific embodiments described above, and that various changes or modifications may be made by one skilled in the art within the scope of the appended claims without departing from the spirit of the invention. The embodiments and features of the embodiments of the present application may be combined with each other arbitrarily without conflict.
Claims (10)
1. A satellite power system health assessment method based on a gradient threshold value is characterized by comprising the following steps:
step M1: acquiring telemetry parameters of a satellite power system, and preprocessing the acquired telemetry parameters of the satellite power system to obtain preprocessed telemetry parameters;
step M2: identifying a remote parameter gradient change point based on the preprocessed remote parameter and calculating a remote parameter gradient;
step M3: carrying out data smoothing calculation on the preprocessed telemetering parameters by using a local weighted regression smoothing method, converting the telemetering parameters into a telemetering curve, and calculating the slope of a gradient change point position curve;
step M4: recording all parameter gradients and corresponding curve slopes corresponding to normal satellite operation data within preset on-orbit operation time of the satellite, and respectively recording a maximum value and a minimum value;
Step M5: in the in-orbit operation process of the satellite, when a gradient change point is generated by the telemetering parameter, one evaluation operation is triggered, the parameter gradient and the curve slope are calculated, and when the gradient and the curve slope both exceed the maximum value in normal operation, the health state evaluation is triggered.
2. The gradient threshold-based satellite power system health assessment method according to claim 1, wherein said step M1 comprises:
step M1.1: resampling the remote parameter data at equal time intervals;
step M1.2: calculating the mean value mean and the standard deviation sigma of the remote parameters in a unit time period, replacing parameter values exceeding [ mean-sigma, mean + sigma ] with values of the previous moment, and acquiring preprocessed remote parameter data;
step M1.3: and replacing the data which do not conform to the parameter range of the illumination area in the preprocessed remote parameter data with null values, and defining the remote parameter state as a shadow area.
3. The gradient threshold-based satellite power system health assessment method according to claim 1, wherein said step M2 comprises:
step M2.1: let the remote parameter value be n 1 、n 2 、…、n m Gradient change sequence is n 2 -n 1 、n 3 -n 2 、…、n m -n m-1 ；
Step M2.2: identifying non-zero points N in a gradient change sequence 1 、N 2 、…N p And recording the corresponding time stamp T 1 、T 2 、…、T p ；
Step M2.3: calculating the corresponding remote parameter gradient of non-zero point as (N) p -N p-1 )/(T p -T p-1 )。
4. The gradient threshold-based satellite power system health assessment method according to claim 1, wherein said step M5 comprises:
step M5.1: judging whether the gradient and the slope of the curve exceed the maximum value of normal operation or not, and if not, the health degree is 1;
step M5.2: when the gradient and the slope of the curve exceed the maximum value of normal operation, the degree of exceeding the maximum value is normalized to be a numerical value between 0 and 1 according to an arc tangent normalization formula to represent the health degree.
5. The gradient threshold-based satellite power system health assessment method according to claim 4, wherein said step M5.2 comprises:
x*＝atan(x)*2/π
wherein x represents the gradient corresponding to the gradient change point and the slope of the curve, and x represents the health degree.
6. A gradient threshold based satellite power system health assessment system, comprising:
module M1: acquiring telemetry parameters of a satellite power system, and preprocessing the acquired telemetry parameters of the satellite power system to obtain preprocessed telemetry parameters;
module M2: identifying a remote parameter gradient change point based on the preprocessed remote parameter and calculating a remote parameter gradient;
module M3: carrying out data smoothing calculation on the preprocessed telemetering parameters by using a local weighted regression smoothing method, converting the telemetering parameters into a telemetering curve, and calculating the slope of a gradient change point position curve;
Module M4: recording all parameter gradients and corresponding curve slopes corresponding to normal satellite operation data within preset on-orbit operation time of the satellite, and respectively recording a maximum value and a minimum value;
module M5: in the in-orbit operation process of the satellite, when a gradient change point is generated by the telemetering parameter, one evaluation operation is triggered, the parameter gradient and the curve slope are calculated, and when the gradient and the curve slope both exceed the maximum value in normal operation, the health state evaluation is triggered.
7. The gradient threshold-based satellite power system health assessment system according to claim 6, wherein said module M1 comprises:
module M1.1: resampling the remote parameter data at equal time intervals;
module M1.2: calculating the mean value mean and the standard deviation sigma of the remote parameters in a unit time period, replacing parameter values exceeding [ mean-sigma, mean + sigma ] with values of the previous moment, and acquiring preprocessed remote parameter data;
module M1.3: and replacing the data which do not conform to the parameter range of the illumination area in the preprocessed remote parameter data with null values, and defining the remote parameter state as a shadow area.
8. The gradient threshold-based satellite power system health assessment system according to claim 6, wherein said module M2 comprises:
Module M2.1: let the remote parameter value be n 1 、n 2 、…、n m Gradient change sequence is n 2 -n 1 、n 3 -n 2 、…、n m -n m-1 ；
Module M2.2: identifying non-zero points N in a gradient change sequence 1 、N 2 、…N p And recording the corresponding time stamp T 1 、T 2 、…、T p ；
Module M2.3: calculating the corresponding remote parameter gradient of non-zero point as (N) p -N p-1 )/(T p -T p-1 )。
9. The gradient threshold-based satellite power system health assessment system according to claim 6, wherein said module M5 comprises:
module M5.1: judging whether the gradient and the slope of the curve exceed the maximum value of normal operation or not, and if not, the health degree is 1;
module M5.2: when the gradient and the slope of the curve exceed the maximum value of normal operation, the degree of exceeding the maximum value is normalized to be a numerical value between 0 and 1 according to an arc tangent normalization formula to represent the health degree.
10. The gradient threshold-based satellite power system health assessment system according to claim 9, wherein said module M5.2 comprises:
x*＝atan(x)*2/π
wherein x represents the gradient corresponding to the gradient change point and the slope of the curve, and x represents the health degree.
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