CN109934337B - Spacecraft telemetry data anomaly detection method based on integrated LSTM - Google Patents

Spacecraft telemetry data anomaly detection method based on integrated LSTM Download PDF

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CN109934337B
CN109934337B CN201910194332.0A CN201910194332A CN109934337B CN 109934337 B CN109934337 B CN 109934337B CN 201910194332 A CN201910194332 A CN 201910194332A CN 109934337 B CN109934337 B CN 109934337B
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刘大同
董静怡
庞景月
彭喜元
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Harbin Institute of Technology
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Abstract

A spacecraft telemetry data anomaly detection method based on integrated LSTM belongs to the technical field of telemetry data anomaly detection. Hair brushThe method obviously solves the problem that the existing method has low accuracy in detecting the context abnormality of the telemetering data. The invention preprocesses the training data, divides the training data into two categories of a training set A and a training set B, and then trains the training set A and the training set B respectively based on the LSTM model, thereby effectively reducing the influence of long-term dependence on the detection result and improving the accuracy of the detection of the whole abnormity; integrating the prediction results of the LSTM model according to a certain weight to obtain the final predicted value of the telemetering data, performing difference by using the error between the prediction result and the actual value, namely, smoothing the error, and performing dynamic threshold value processing according to the error
Figure DDA0001995358440000011
And detecting an abnormal interval of the telemetering data, compared with the prior method, the method can improve the accuracy of the detection of the abnormal context of the telemetering data of the spacecraft by more than 11%. The method can be applied to the technical field of detection of telemetering data abnormity.

Description

Spacecraft telemetry data anomaly detection method based on integrated LSTM
Technical Field
The invention belongs to the technical field of detection of telemetering data abnormity, and particularly relates to a spacecraft telemetering data abnormity detection method based on integrated LSTM.
Background
With the gradual increase of the complexity and functionality of spacecraft design, the probability of spacecraft failure caused by various reasons such as insufficient design, manufacturing errors, maintenance errors and unplanned events increases, and as for 764 spacecraft launched from 1993 to 2012 and including satellites and space stations, the spacecraft has 121 failures in total, and accounts for 15.8% of the total number of launched spacecraft. Meanwhile, the research and development period of the satellite is gradually shortened, the emission number is increased, and the strict requirements of safe and reliable operation of the satellite on data processing and analysis cannot be met only by the traditional reliability engineering method and means and the manual analysis method depending on the personal experience of experts.
At present, most satellites are controlled in a ground telemetering and remote control mode, during the in-orbit operation period of the satellites, a ground monitoring station needs to receive and process telemetering data of the in-orbit satellites, automatic judgment, alarming and other operations are carried out according to state monitoring parameters of the in-orbit satellites, and meanwhile, the obtained telemetering data are stored in a database for post data analysis. As the only information source for ground personnel to judge the working state of the orbiting satellite, whether the telemetering data is abnormal or not is closely related to whether the working mode of the satellite is changed or whether the satellite has a fault or not. Therefore, how to pre-judge the time, possibility and severity of the abnormal state by analyzing the satellite in-orbit running state data in real time and accurately, and performing effective abnormal processing and health management according to the pre-judging result becomes a key research for enhancing the satellite in-orbit running reliability.
The research on the anomaly detection of the telemetering data of the spacecraft has attracted extensive attention of domestic and foreign research institutions. For example, the European Space Operations control center (ESOC) affiliated to the ESA has developed an Analysis and Reporting systems (ARES) System, which is the latest off-line data Analysis System solution of the ESOC; ORCA and Induction Monitoring System (IMS) are developed by NASA Emms Research Center (ARC), wherein ORCA excavates abnormalities and outliers by calculating the distance between adjacent points, and IMS software automatically constructs a knowledge base for System health Monitoring; an automatic Telemetry data Health Monitoring System (Automated Telemetry Health Monitoring System, ATHMoS) is developed by the German Space Operation Center (GSOC), and anomaly detection is realized by using outlier detection and supervised and learning methods.
The current telemetering data abnormity detection method is mainly divided into three methods: statistical-based methods, distance-based and prediction-based methods.
Statistical methods typically assume that data obeys a known distribution model, and data that does not conform to the distribution or that the statistical features do not conform are flagged as anomalous, however, actual data is difficult to characterize with a preset distribution. The distance-based method labels the data points with longer distance and lower density by measuring the distance between different data. The method is based on the density, and is assisted by distance measurement, so that the time-series high-accuracy abnormity detection is realized; the literature (research on the similarity measurement method of the time sequence of the satellite telemetry data) develops the research on the similarity measurement method for the time sequence of the satellite telemetry data, and realizes the elimination of the correlation influence among parameters and the realization of asynchronous measurement. However, distance-based methods are very sensitive to distance metric functions and difficult to model temporal correlations. The deviation based method is characterized in that the deviation between the measured data and the model output is counted by modeling the normal data, and the data exceeding the fixed deviation is marked as abnormal. The method based on the deviation has strong interpretability based on a prediction method, strong online application capability and high test speed, and is more suitable for realizing online monitoring of telemetering data.
However, the above researches are based on the unit and multivariate abnormality detection of the telemetry data, and for the actual complex spacecraft system, the ground can send corresponding remote control instructions through a remote control link to effectively control the system. Telemetry and telemetry interact, and it is clear that telemetry without telemetry is blind and telemetry without telemetry is passive. Therefore, how to effectively fuse the remote control instructions to realize the anomaly detection of the telemetry data is an effective way for improving the anomaly detection of the telemetry data.
Recently, as deep learning progresses, many models are applied to the application of anomaly detection. Particularly, the LSTM method, as a special type of Recurrent Neural Network (RNN), retains the advantages of the standard RNN, can perform deduction prediction on a time series by using historical data, and is widely concerned in time series anomaly detection. In the literature (Detecting space analytes Using LSTMs and Nonparametric Dynamic threshold), NASA combines the powerful nonlinear modeling capability and the automatic feature extraction capability of the LSTM, and simultaneously takes a remote control instruction and telemetric data as input to construct an LSTM model, thereby realizing effective marking of the telemetric data. However, because a uniform LSTM model is constructed for the control modes of all instructions, the average value and variance of the overall prediction error are increased, the detection capability of the method for detecting the context anomaly of the spacecraft telemetry data is poor, and the detection accuracy of the context anomaly is low.
Disclosure of Invention
The method aims to solve the problem that the accuracy of detecting the context abnormality of the spacecraft telemetry data is low in the existing method.
The technical scheme adopted by the invention for solving the technical problems is as follows: a spacecraft telemetry data anomaly detection method based on integrated LSTM comprises the following steps:
step one, selecting training data
Figure BDA0001995358420000021
Wherein:
Figure BDA0001995358420000022
is a group of m-dimensional vectors represented by a remote control command matrix at the time t and a remote measuring value at the time t, and the remote control command matrix at the time t is
Figure BDA0001995358420000031
telemetry value at time t of
Figure BDA0001995358420000032
Preprocessing the selected training data S to obtain reconstructed input X:
step two, respectively extracting a remote control instruction matrix in each secondary matrix contained in the reconstructed input X, wherein the remote control instruction matrix in each secondary matrix is respectively expressed as
Figure BDA0001995358420000033
Respectively calculating the L2 norm of the remote control command matrix corresponding to each secondary matrix, taking the median of all the calculated L2 norms as a threshold beta, taking a set formed by the secondary matrixes corresponding to the remote control command matrix with the L2 norm being greater than the threshold beta as a training set A, and taking a set formed by the secondary matrixes corresponding to the remote control command matrix with the L2 norm being less than or equal to the threshold beta as a training set B;
step three, constructing an integrated LSTM model containing an LSTM model A and an LSTM model B, and respectively training the LSTM model A and the LSTM model B by utilizing the training set A and the training set B obtained in the step two to obtain a well-trained LSTM model A and an LSTM model B;
step four, test data are preprocessed to obtain a test set, the test set is respectively input into the well-trained LSTM model A and the well-trained LSTM model B, and a telemetering predicted value sequence formed by predicted values of the telemetering values corresponding to each secondary matrix output by the LSTM model A is respectively obtained
Figure BDA0001995358420000034
Telemetering predicted value sequence formed by predicted values of telemetering values corresponding to each secondary matrix output by LSTM model B
Figure BDA0001995358420000035
Setting fusion parameters, and determining a final telemetering value prediction sequence according to a telemetering prediction value sequence consisting of prediction values of telemetering values corresponding to each secondary matrix output by the LSTM model A, a telemetering prediction value sequence consisting of prediction values of telemetering values corresponding to each secondary matrix output by the LSTM model B and the fusion parameters
Figure BDA0001995358420000036
Step five, the final telemetering value prediction sequence determined in the step four
Figure BDA0001995358420000037
The value in (d) is subtracted from the actual remote value at the corresponding time to obtain an error sequence e ═ e { (e)(1)，e(2)，…，e(t)，…}，e(t)Represents the error at time t;
smoothing the error sequence by adopting an exponential weighted average method to obtain a smoothed error sequence
Figure BDA0001995358420000038
Figure BDA0001995358420000039
Will smooth out the error sequenceThe smooth error values are grouped, and then the dynamic threshold value of each group is respectively calculated
Figure BDA00019953584200000310
Exceeding dynamic threshold value in each group
Figure BDA00019953584200000315
And the value of the smoothing error is less than the dynamic threshold value in the group
Figure BDA00019953584200000311
And is closest to the dynamic threshold
Figure BDA00019953584200000314
If the difference value is greater than the discrimination threshold value p, the dynamic threshold value is exceeded
Figure BDA00019953584200000312
The telemetering value at the moment corresponding to the smoothing error value is regarded as an abnormal value; if the difference is less than or equal to the discrimination threshold p, the exceeding dynamic threshold is determined
Figure BDA00019953584200000313
The telemetering value of the moment corresponding to the smoothing error value is taken as a normal value;
and step six, repeating the process from the step four to the step five for the telemetering data of the spacecraft to be detected, and realizing the detection of the abnormity of the telemetering data of the spacecraft to be detected.
The invention has the beneficial effects that: the invention discloses a spacecraft telemetering data anomaly detection method based on integrated LSTM, which comprises the steps of preprocessing training data, dividing the training data into a training set A and a training set B, and then training the training set A and the training set B respectively based on an LSTM model, so that the influence of long-term dependence on a detection result can be effectively reduced, and the accuracy of overall anomaly detection is improved; integrating the prediction results of the LSTM model according to a certain weight to obtain the final predicted value of the telemetering data, performing difference by using the error between the prediction result and the actual value, namely, smoothing the error, and performing dynamic threshold value processing according to the error
Figure BDA0001995358420000046
And detecting an abnormal interval of the telemetering data, compared with the prior method, the method can improve the accuracy of the detection of the abnormal context of the telemetering data of the spacecraft by more than 11%.
Drawings
FIG. 1 is a flow chart of a method of the present invention for integrated LSTM based detection of spacecraft telemetry data anomalies;
FIG. 2 is a schematic diagram of a synchronous many-to-many time series model employed by the present invention;
FIG. 3 is a block diagram of LSTM model prediction;
FIG. 4 is a flow chart of the processing and classification of training data;
FIG. 5 is a comparison graph of an actual telemetry value and a predicted telemetry value;
FIG. 6 is a schematic diagram illustrating the abnormal point determination according to the present invention;
FIG. 7 is a schematic representation of all telemetry values on a telemetry channel;
FIG. 8a) is a schematic diagram of an actual exception sequence;
fig. 8b) is a schematic diagram of the anomaly detection result.
Detailed Description
The first embodiment is as follows: as shown in fig. 1, the method for detecting an anomaly of spacecraft telemetry data based on integrated LSTM according to the embodiment includes the following steps:
step one, selecting training data
Figure BDA0001995358420000041
Wherein:
Figure BDA0001995358420000042
is a group of m-dimensional vectors represented by a remote control command at the time t and a remote measuring value at the time t, and the remote control command at the time t is
Figure BDA0001995358420000043
telemetry value at time t of
Figure BDA0001995358420000044
Preprocessing the selected training data S to obtain reconstructed input X:
step two, respectively extracting the remote control instruction in each secondary matrix contained in the reconstructed input X, wherein the remote control instruction in each secondary matrix is respectively expressed as
Figure BDA0001995358420000045
Respectively calculating the L2 norm of the remote control command corresponding to each secondary matrix, taking the median of all the calculated L2 norms as a threshold beta, taking a set formed by the secondary matrixes corresponding to the remote control commands of which the L2 norms are greater than the threshold beta as a training set A, and taking a set formed by the secondary matrixes corresponding to the remote control commands of which the L2 norms are less than or equal to the threshold beta as a training set B;
FIG. 4 is a flow chart of the processing and classification of training data;
step three, constructing an integrated LSTM model containing an LSTM model A and an LSTM model B, and respectively training the LSTM model A and the LSTM model B by utilizing the training set A and the training set B obtained in the step two to obtain a well-trained LSTM model A and an LSTM model B;
step four, test data are preprocessed to obtain a test set (the preprocessing of the test data refers to preprocessing to obtain reconstructed input, all secondary matrixes in the reconstructed input are used as the test set), the test set is respectively input into the trained LSTM model A and the trained LSTM model B, and a telemetering predicted value sequence formed by the predicted values of the telemetering data corresponding to each secondary matrix output by the LSTM model A and a telemetering predicted value sequence formed by the predicted values of the telemetering data corresponding to each secondary matrix output by the LSTM model B are respectively obtained;
setting fusion parameters, and determining a final telemetering data prediction sequence according to a telemetering prediction value sequence formed by the prediction values of the telemetering data corresponding to each secondary matrix output by the LSTM model A, a telemetering prediction value sequence formed by the prediction values of the telemetering data corresponding to each secondary matrix output by the LSTM model B and the fusion parameters
Figure BDA0001995358420000051
Step five, the final telemetering data prediction sequence determined in the step four is used
Figure BDA0001995358420000052
The value in (d) is subtracted from the actual remote value at the corresponding time to obtain an error sequence e ═ e { (e)(1)，e(2)，…，e(t)，…}，e(t)Represents the error at time t;
smoothing the error sequence by adopting an exponential weighted average (EWMA) method to obtain a smoothed error sequence
Figure BDA0001995358420000053
Grouping the smooth error values in the smooth error sequence, and respectively calculating the dynamic threshold of each group
Figure BDA0001995358420000054
Exceeding dynamic threshold value in each group
Figure BDA0001995358420000059
And the value of the smoothing error is less than the dynamic threshold value in the group
Figure BDA0001995358420000055
And is closest to the dynamic threshold
Figure BDA0001995358420000057
If the difference value is greater than the discrimination threshold value p, the dynamic threshold value is exceeded
Figure BDA0001995358420000056
The telemetering value at the moment corresponding to the smoothing error value is regarded as an abnormal value; if the difference is less than or equal to the discrimination threshold p, the exceeding dynamic threshold is determined
Figure BDA0001995358420000058
The telemetering value of the moment corresponding to the smoothing error value is taken as a normal value;
in obtaining aAfter the group has smoothed the error, time 1 to ltTaking the smoothing error in the first group as the smoothing error in the next h time in the first group and the smoothing error in the time lt+1 to 2ltAnd taking the smoothing errors in the error group as a second group, and repeating the steps to complete the grouping of the smoothing errors, and if the quantity of the smoothing errors in the last group is less than h, fusing the smoothing errors with the previous group.
Calculating the mean and variance of each group of smoothing errors, and further calculating a dynamic threshold
Figure BDA00019953584200000510
Each ltAnd regarding the smoothing errors in the time as a group, and keeping the smoothing errors in the h time in the previous group, so that the smoothing error sequence is divided into a plurality of parts to respectively calculate the dynamic threshold.
Dynamic threshold of nth set of smoothing errors
Figure BDA00019953584200000511
The expression of (a) is:
Figure BDA0001995358420000061
Figure BDA0001995358420000062
smoothing errors for the nth set
Figure BDA0001995358420000063
The average value of (a) of (b),
Figure BDA0001995358420000064
is composed of
Figure BDA0001995358420000065
Z is a coefficient matrix.
Since the abnormality occurs at the time when the smoothing error abruptly changes, the points exceeding the threshold cannot be collectively regarded as the abnormal points. Exceeding dynamic threshold within the same group
Figure BDA00019953584200000623
Is compared with the maximum smoothing error that does not exceed the dynamic threshold, and if the difference between the two points is less than or equal to p, it is considered as a normal point. The principle is as shown in figure 6 of the drawings,
Figure BDA0001995358420000066
and
Figure BDA0001995358420000067
all exceed the threshold value, but
Figure BDA0001995358420000068
And
Figure BDA0001995358420000069
is less than p (p is 0.01), so
Figure BDA00019953584200000610
It is still the normal point, the same principle
Figure BDA00019953584200000611
The abnormality is considered.
And step six, repeating the process from the step four to the step five for the telemetering data of the spacecraft to be detected, and realizing the detection of the abnormity of the telemetering data of the spacecraft to be detected.
The training data and the test data of the present embodiment are derived from the data set collected in the incorporated surservices, analog (isa) reports published by NASA, which relates to two spacecraft types, including smap (soil motion Active and passive) and msl (mars Science laboratory).
The second embodiment is as follows: the first difference between the present embodiment and the specific embodiment is: the specific process of preprocessing the selected training data S to obtain the reconstructed input X is as follows:
splitting the training data S into a plurality of sub-matrices comprising successive time vectors, said sub-matrices being represented respectively as:
Figure BDA00019953584200000612
and the number of columns of each secondary matrix is ltThen the reconstructed input X is represented as:
Figure BDA00019953584200000613
wherein:
Figure BDA00019953584200000614
the first secondary matrix is represented as a function of,
Figure BDA00019953584200000615
a second sub-matrix is represented which is,
Figure BDA00019953584200000616
representing the nth secondary matrix;
first order matrix
Figure BDA00019953584200000617
Second secondary matrix
Figure BDA00019953584200000618
And nth secondary matrix
Figure BDA00019953584200000619
Are respectively:
Figure BDA00019953584200000620
Figure BDA00019953584200000621
Figure BDA00019953584200000622
the third concrete implementation mode: the second embodiment is different from the first embodiment in that: the specific process of the second step is as follows:
respectively extracting a remote control instruction matrix in each secondary matrix contained in the reconstructed input X, and respectively representing the remote control instruction matrix in each secondary matrix as
Figure BDA0001995358420000071
Wherein:
Figure BDA0001995358420000072
is composed of
Figure BDA0001995358420000073
The matrix of remote control commands in (1),
Figure BDA0001995358420000074
is composed of
Figure BDA0001995358420000075
The matrix of remote control commands in (1),
Figure BDA0001995358420000076
is composed of
Figure BDA0001995358420000077
The matrix of remote controls of (1) is,
remote control instruction matrix
Figure BDA0001995358420000078
The expression of (a) is:
Figure BDA0001995358420000079
remote control instruction
Figure BDA00019953584200000710
The L2 norm is calculated as:
Figure BDA00019953584200000711
wherein:
Figure BDA00019953584200000712
represents
Figure BDA00019953584200000713
The transpose of (a) is performed,
Figure BDA00019953584200000714
represents
Figure BDA00019953584200000715
The L2 norm;
Figure BDA00019953584200000716
representing and calculating matrix
Figure BDA00019953584200000717
The maximum eigenvalue of (d);
after the L2 norm of each remote control command matrix is calculated, the median of all L2 norms is taken as a threshold value beta, a set formed by secondary matrixes corresponding to the remote control command matrix with the L2 norm being less than or equal to the threshold value beta is taken as a training set B, and a set formed by the secondary matrixes corresponding to the remote control command matrix with the L2 norm being greater than the threshold value beta is taken as a training set A.
The fourth concrete implementation mode: the first difference between the present embodiment and the specific embodiment is: the LSTM model A in the third step comprises an input layer, a first hidden layer, a second hidden layer and an output layer, wherein a loss function of the LSTM model A adopts a Mean absolute error function (MAE), an optimization function adopts an adam (adaptive motion estimation) function, and the value of drop is [0.2, 0.3 ];
the structure and parameter settings of the LSTM model B are the same as those of the LSTM model A.
The structures of the LSTM model a and the LSTM model B adopted in the present embodiment are divided into three layers: an Input Layer (Input Layer), a Hidden Layer (Hidden Layer) and an Output Layer (Output Layer), wherein the Hidden Layer is two layers. Each hidden layer contains a plurality of Memory blocks (Memory blocks), and each Memory Block contains a plurality of Memory cells (Memory cells). For the anomaly detection problem to be solved by the invention, the model type adopted is a synchronous many-to-many time series model, as shown in fig. 2, each test data is input and passes through a hidden layer, and then a prediction matrix is synchronously output.
The memory cells in a single memory block are connected together by a chain structure. Based on standard RNN, a memory Cell in LSTM contains one Cell (Cell) and three gates (Gate). The information in the cell is called the cell state, which is updated based on historical output as well as real-time input. The three gates are respectively: an Input Gate (Input Gate), a forgetting Gate (Forget Gate), and an Output Gate (Output Gate). Because the telemetering data is a time sequence, the change of the value of the telemetering data is related to the remote control command at the same time, and during the learning process of the LSTM on the training data, the accuracy of the anomaly detection system can be better improved by selectively adopting the characteristic data.
The training process of the data in the LSTM system is as follows: input data x at time ttAll cells in the same Block as the previous time,
Figure BDA0001995358420000081
as input into the input gate and the forgetting gate. The output of the input gate is:
Figure BDA0001995358420000082
Figure BDA0001995358420000083
wherein
Figure BDA0001995358420000084
For the output of the input gate, f (x) is the Sigmoid function. OmegailFor the ith input at the same time
Figure BDA0001995358420000085
Weight matrix of ωclFor the output of the c-th cell in the same block
Figure BDA0001995358420000086
The weight matrix of (2).
The output of Forget Gate is:
Figure BDA0001995358420000087
Figure BDA0001995358420000088
wherein
Figure BDA0001995358420000089
For the output of the forgetting gate, f (x) is the Sigmoid function.
Figure BDA00019953584200000810
For the ith input at the same time
Figure BDA00019953584200000811
The weight matrix of (a) is determined,
Figure BDA00019953584200000812
for the output of the c-th cell in the same block
Figure BDA00019953584200000813
The weight matrix of (2).
The output of the cell will combine the output of the input gate and the output of the forgetting gate:
Figure BDA00019953584200000814
Figure BDA00019953584200000815
Figure BDA00019953584200000816
being cellsOutput, g (x), is the tahn function.
Figure BDA00019953584200000817
Update information for candidate cells, ωicFor the ith input at the same time
Figure BDA00019953584200000818
The weight matrix of (2).
Final intracellular output
Figure BDA00019953584200000819
Input x that will correspond to the current timetThe corresponding output is the final prediction result of the current learning as the input of the output gate.
Figure BDA00019953584200000820
Figure BDA00019953584200000821
For Output Gate: omegaiωFor the ith input at the same time
Figure BDA00019953584200000822
Weight matrix of ωiωFor the ith input at the same time
Figure BDA00019953584200000823
A weight matrix of (a);
ωcωis the output of the c Cell of the same Block
Figure BDA0001995358420000091
The weight matrix of (2).
Figure BDA0001995358420000092
For Output Gate Output, f (x) is the activation function.
The learning rate of each parameter is dynamically adjusted by using the first Moment estimation and the second Moment estimation of the gradient due to a model optimization function adam (adaptive motion estimation), and the learning rate has the capability of storing the average value of the previous partial derivative square attenuation.
Through multiple iterative learning, the model can provide higher performance by updating the optimized weight matrix, and in order to prevent overfitting, a certain proportion of memory modules are prevented from participating in the learning process of the LSTM in each hidden layer, which is also called dropout.
The fifth concrete implementation mode: the fourth difference between this embodiment and the specific embodiment is that: in the third step, the training set A and the training set B obtained in the second step are used for respectively training the LSTM model A and the LSTM model B to obtain a trained LSTM model A and a trained LSTM model B; the specific process comprises the following steps:
respectively inputting the training set A and the training set B into an LSTM model A and an LSTM model B for training, setting the maximum iteration times of the LSTM model A and the LSTM model B for training to be alpha, continuously adjusting each weight matrix of the LSTM model A and the LSTM model B in each iteration training process, and stopping training until the maximum iteration times are reached to obtain the well-trained LSTM model A and the LSTM model B.
For any one secondary matrix in input X
Figure BDA0001995358420000093
For each one
Figure BDA0001995358420000094
To say that ltThe sequence of how many consecutive times to determine is used as a set of input values for the LSTM model, and each set of input values is used to predict the telemetry value at the next time.
If the secondary matrix
Figure BDA0001995358420000095
Belonging to the training set A, the secondary matrix
Figure BDA0001995358420000096
Inputting LSTM model A for training, and determining the matrix
Figure BDA0001995358420000097
Belonging to training set B, the secondary matrix
Figure BDA0001995358420000098
Inputting an LSTM model B for training;
obtaining a predicted telemetry value of the next moment of each iteration
Figure BDA0001995358420000099
And analyzing the error of the predicted remote measurement value at the next moment by using the loss function, optimizing the model by using the optimization function based on the result of the loss function until the training is finished when the maximum iteration times are reached, and obtaining a trained LSTM model A and an LSTM model B.
Predicted telemetry value for next time instant
Figure BDA00019953584200000910
The expression of (a) is:
Figure BDA00019953584200000911
in order to train the weight and find the corresponding gradient, a proper loss function and an optimization function are selected to help the model to determine the corresponding optimal weight matrix, so that the prediction accuracy is improved, and the difference between the true value and the prediction value is reduced. The specific LSTM model framework is shown in fig. 3: in the training process, the training data is automatically adjusted through the numerical value of the optimization function in a multi-iteration mode until the optimal weight is obtained, and the accurate prediction of the time sequence data is realized. After the model training is finished, the model can quickly predict the multidimensional data, and the predicted value is used as an important basis for the abnormal detection of the telemetering data.
The sixth specific implementation mode: the fourth difference between this embodiment and the specific embodiment is that: the specific form of the absolute value error function is as follows:
Figure BDA0001995358420000101
wherein: k is the total number of predicted values, yiFor the ith real value, the value of the real value,
Figure BDA0001995358420000102
is the (i) th predicted value,
Figure BDA0001995358420000103
the absolute value error function value.
The seventh embodiment: the first difference between the present embodiment and the specific embodiment is: the telemetry prediction value sequence formed by the prediction values of the telemetry values corresponding to each secondary matrix output by the LSTM model A, the telemetry prediction value sequence formed by the prediction values of the telemetry values corresponding to each secondary matrix output by the LSTM model B and the fusion parameter determine the final telemetry value prediction sequence
Figure BDA0001995358420000104
The method comprises the following steps:
for secondary matrix in test set
Figure BDA0001995358420000105
Secondary matrix
Figure BDA0001995358420000106
The corresponding remote control command matrix is
Figure BDA0001995358420000107
Secondary matrix of the image
Figure BDA0001995358420000108
Inputting the LSTM model A to obtain the predicted value of the remote measurement value output by the LSTM model A
Figure BDA0001995358420000109
Secondary matrix of the image
Figure BDA00019953584200001010
Inputting the LSTM model B to obtain the predicted value of the remote measurement value output by the LSTM model B
Figure BDA00019953584200001011
Similarly, the predicted value of the remote measurement value output by the LSTM model A and the predicted value of the remote measurement value output by the LSTM model B are input to other secondary matrixes in the test set;
then the remote measuring predicted value sequence formed by the predicted values of the remote measuring values corresponding to each secondary matrix output by the LSTM model A
Figure BDA00019953584200001012
Expressed as:
Figure BDA00019953584200001013
herein, the
Figure BDA00019953584200001014
The predicted value of telemetry data corresponding to the first submatrix of the test set,
Figure BDA00019953584200001015
for the predicted value of telemetry data corresponding to the second submatrix of the test set,
Figure BDA00019953584200001016
is the t-th sub-matrix of the test set (i.e., the
Figure BDA00019953584200001017
) A predicted value of the corresponding telemetry data;
then the remote measuring predicted value sequence formed by the predicted values of the remote measuring values corresponding to each secondary matrix output by the LSTM model B
Figure BDA00019953584200001018
Expressed as:
Figure BDA00019953584200001019
and calculating a secondary matrix
Figure BDA00019953584200001020
Corresponding remote control instruction matrix
Figure BDA00019953584200001021
The L2 norm;
if the remote control instruction matrix
Figure BDA00019953584200001022
L2 norm is greater than threshold β, then secondary matrix
Figure BDA00019953584200001023
Corresponding final telemetry value prediction values
Figure BDA00019953584200001024
Expressed as:
Figure BDA0001995358420000111
if the remote control instruction matrix
Figure BDA0001995358420000112
L2 norm is less than or equal to threshold beta, then secondary matrix
Figure BDA0001995358420000113
Corresponding final telemetry value prediction values
Figure BDA0001995358420000114
Expressed as:
Figure BDA0001995358420000115
similarly, the final telemetering value predicted value corresponding to other secondary matrixes in the test set is obtained, and the final telemetering value predicted sequence
Figure BDA0001995358420000116
The specific implementation mode is eight: the first difference between the present embodiment and the specific embodiment is: smoothing the error sequence by adopting an exponential weighted average methodProcessing to obtain a smoothing error sequence es(ii) a The specific process comprises the following steps:
Figure BDA0001995358420000117
Figure BDA0001995358420000118
for the smoothed error value at time t,
Figure BDA0001995358420000119
a smoothing error value at time t-1 (when t is 1, the error at that time is a default smoothing error value), and γ is a threshold value of the smoothing error;
the smoothing error sequence is
Figure BDA00019953584200001110
As shown in fig. 5, the variation of the error is very large under normal conditions, which is not beneficial to analysis, and most of the error is considered to be caused by the difference between the predicted value and the actual value rather than the occurrence of abnormality. Smoothing the error becomes particularly important. The error e is smoothed, so that an error peak which often occurs when a predicted value is obtained based on the LSTM prediction model can be suppressed. Since the actual telemetry value may be abruptly changed due to the remote command at that time, the abrupt change is usually not well predicted, which will result in an abrupt change of e at a certain time, and the abrupt change is a normal phenomenon and cannot be regarded as abnormal.
In the embodiment, the error value is processed by using a Weighted moving average method (EWMA) to obtain a smooth error, thereby avoiding the sudden change of the error value caused by a large difference between a predicted value and a normal value. And the meaning of judging whether the data is abnormal is given only when the error value after smoothing is abnormally high or abnormally low in a period of time.
Examples
The training data and test data included 82 sets of data out of twelve subsystems, with a total of 55 sets (67%) belonging to SMAP and 27 sets (33%) belonging to MSL. The test data samples are shown in table 1:
TABLE 1
Figure BDA00019953584200001111
Figure BDA0001995358420000121
By reporting relevant data records of the SMAP and the MSL through the ISA, the time range and the abnormal type of each set of telemetric data are marked, and as shown in figure 7, the shaded part is the abnormal value of the set of telemetric data.
The detailed total amount of training data, total amount of abnormal sequences, total amount of testing telemetry channels and total amount of testing data are shown in table 2 below:
TABLE 2
Figure BDA0001995358420000122
LSTM model parameter setting and performance index:
the LSTM parameters used by each training set are the same, the predicted values output by the LSTM model A and the LSTM model B are fused by a weight matrix, and the relevant parameters and fusion parameters of the LSTM model are shown in the following table 3:
TABLE 3
Figure BDA0001995358420000123
Figure BDA0001995358420000131
The performance analysis of the anomaly detection method provided by the invention mainly comprises four aspects: accuracy and false detection rate for SMAP point anomaly, accuracy and false detection rate for MSL point anomaly, accuracy and false detection rate for SMAP context anomaly, and accuracy and false detection rate for MSL point anomaly.
When the detected abnormality is overlapped with the marked abnormality in a large range, the abnormality is considered to be successfully detected, and the ratio of the total number of successfully detected abnormal sequences to the total number of actual abnormal sequences is the accuracy of the abnormality detection scheme, as shown in fig. 8a) and 8 b).
When the detected abnormality does not belong to the actual abnormality or the overlap portion is extremely small, it is regarded as a false detection. The ratio of the total number of the sequences detected by mistake to the sum of the number of the sequences detected by mistake and the number of the sequences detected accurately is the false detection rate of the abnormal detection scheme.
As shown in table 4, compared to the LSTM-based anomaly detection method, the integrated LSTM-based telemetry data anomaly detection method has a certain degree of improvement in accuracy in both point anomaly and context anomaly. The improvement of the abnormality detection accuracy rate for the SMAP is particularly obvious. The accuracy of the telemetry data detection method based on the integrated LSTM improves the accuracy of point anomaly detection and context anomaly detection by more than 11%. And aiming at the abnormal detection of the MSL, the accuracy rate of the abnormal context is improved by more than 11%. In a whole view, the method provided by the invention has more obvious improvement on the accuracy rate of the context abnormity, and the detection accuracy rate of the point abnormity is improved by a small extent.
TABLE 4
Figure BDA0001995358420000132
Figure BDA0001995358420000141
The above-described calculation examples of the present invention are merely to explain the calculation model and the calculation flow of the present invention in detail, and are not intended to limit the embodiments of the present invention. It will be apparent to those skilled in the art that other variations and modifications of the present invention can be made based on the above description, and it is not intended to be exhaustive or to limit the invention to the precise form disclosed, and all such modifications and variations are possible and contemplated as falling within the scope of the invention.

Claims (5)

1. A spacecraft telemetry data anomaly detection method based on integrated LSTM is characterized by comprising the following steps:
step one, selecting training data
Figure FDA0002626935210000011
Wherein:
Figure FDA0002626935210000012
is a group of m-dimensional vectors represented by a remote control command matrix at the time t and a remote measuring value at the time t, and the remote control command matrix at the time t is
Figure FDA0002626935210000013
telemetry value at time t of
Figure FDA0002626935210000014
Preprocessing the selected training data S to obtain reconstructed input X;
the specific process of preprocessing the selected training data S to obtain the reconstructed input X is as follows:
splitting the training data S into a plurality of sub-matrices comprising successive time vectors, said sub-matrices being represented respectively as:
Figure FDA0002626935210000015
and the number of columns of each secondary matrix is ltThen the reconstructed input X is represented as:
Figure FDA0002626935210000016
wherein:
Figure FDA0002626935210000017
the first secondary matrix is represented as a function of,
Figure FDA0002626935210000018
a second sub-matrix is represented which is,
Figure FDA0002626935210000019
representing the nth secondary matrix;
first order matrix
Figure FDA00026269352100000110
Second secondary matrix
Figure FDA00026269352100000111
And nth secondary matrix
Figure FDA00026269352100000112
Are respectively:
Figure FDA00026269352100000113
Figure FDA00026269352100000114
Figure FDA00026269352100000115
step two, respectively extracting a remote control instruction matrix in each secondary matrix contained in the reconstructed input X, wherein the remote control instruction matrix in each secondary matrix is respectively expressed as
Figure FDA00026269352100000116
Respectively calculating the L2 norm of the remote control command matrix corresponding to each secondary matrix, taking the median of all the calculated L2 norms as a threshold beta, and taking the remote control command with the L2 norm larger than the threshold betaTaking a set formed by secondary matrixes corresponding to the matrixes as a training set A, and taking a set formed by the secondary matrixes corresponding to the remote control instruction matrixes with the L2 norm being less than or equal to a threshold value beta as a training set B;
step three, constructing an integrated LSTM model containing an LSTM model A and an LSTM model B, and respectively training the LSTM model A and the LSTM model B by utilizing the training set A and the training set B obtained in the step two to obtain a well-trained LSTM model A and an LSTM model B;
the LSTM model A in the third step comprises an input layer, a first hidden layer, a second hidden layer and an output layer, the loss function of the LSTM model A adopts an absolute value error function, the optimization function adopts an Adam function, and the value of dropout is [0.2, 0.3 ];
the structure and parameter setting of the LSTM model B are the same as those of the LSTM model A;
the specific form of the absolute value error function is as follows:
Figure FDA0002626935210000021
wherein: k is the total number of predicted values, yiFor the ith real value, the value of the real value,
Figure FDA0002626935210000022
is the (i) th predicted value,
Figure FDA0002626935210000023
is the absolute value error function value;
step four, test data are preprocessed to obtain a test set, the test set is respectively input into the well-trained LSTM model A and the well-trained LSTM model B, and a telemetering predicted value sequence formed by predicted values of the telemetering values corresponding to each secondary matrix output by the LSTM model A is respectively obtained
Figure FDA0002626935210000024
Telemetering predicted value sequence formed by predicted values of telemetering values corresponding to each secondary matrix output by LSTM model B
Figure FDA0002626935210000025
Setting fusion parameters, and determining a final telemetering value prediction sequence according to a telemetering prediction value sequence consisting of prediction values of telemetering values corresponding to each secondary matrix output by the LSTM model A, a telemetering prediction value sequence consisting of prediction values of telemetering values corresponding to each secondary matrix output by the LSTM model B and the fusion parameters
Figure FDA0002626935210000026
Step five, the final telemetering value prediction sequence determined in the step four
Figure FDA0002626935210000027
The value in (d) is subtracted from the actual remote value at the corresponding time to obtain an error sequence e ═ e { (e)(1)，e(2)，…，e(t)，…}，e(t)Represents the error at time t;
smoothing the error sequence by adopting an exponential weighted average method to obtain a smoothed error sequence
Figure FDA0002626935210000028
Grouping the smooth error values in the smooth error sequence, and respectively calculating the dynamic threshold of each group
Figure FDA0002626935210000029
Exceeding dynamic threshold value in each group
Figure FDA00026269352100000214
And the value of the smoothing error is less than the dynamic threshold value in the group
Figure FDA00026269352100000210
And is closest to the dynamic threshold
Figure FDA00026269352100000211
Is smoothed byThe error value is subtracted, if the difference value is larger than the discrimination threshold value p, the exceeding dynamic threshold value is determined
Figure FDA00026269352100000212
The telemetering value at the moment corresponding to the smoothing error value is regarded as an abnormal value; if the difference is less than or equal to the discrimination threshold p, the exceeding dynamic threshold is determined
Figure FDA00026269352100000213
The telemetering value of the moment corresponding to the smoothing error value is taken as a normal value;
and step six, repeating the process from the step four to the step five for the telemetering data of the spacecraft to be detected, and realizing the detection of the abnormity of the telemetering data of the spacecraft to be detected.
2. The method for detecting spacecraft telemetry data anomaly based on integrated LSTM according to claim 1, wherein the specific process of the second step is as follows:
respectively extracting a remote control instruction matrix in each secondary matrix contained in the reconstructed input X, and respectively representing the remote control instruction matrix in each secondary matrix as
Figure FDA0002626935210000031
Wherein:
Figure FDA0002626935210000032
is composed of
Figure FDA0002626935210000033
The matrix of remote control commands in (1),
Figure FDA0002626935210000034
is composed of
Figure FDA0002626935210000035
The matrix of remote control commands in (1),
Figure FDA0002626935210000036
is composed of
Figure FDA0002626935210000037
The remote control instruction matrix in (1);
remote control instruction matrix
Figure FDA0002626935210000038
The expression of (a) is:
Figure FDA0002626935210000039
remote control instruction matrix
Figure FDA00026269352100000310
The L2 norm is calculated as:
Figure FDA00026269352100000311
wherein:
Figure FDA00026269352100000312
represents
Figure FDA00026269352100000313
The transpose of (a) is performed,
Figure FDA00026269352100000314
represents
Figure FDA00026269352100000315
The L2 norm;
Figure FDA00026269352100000316
representing and calculating matrix
Figure FDA00026269352100000317
Maximum eigenvalue of；
After the L2 norm of each remote control command matrix is calculated, the median of all L2 norms is taken as a threshold value beta, a set formed by secondary matrixes corresponding to the remote control command matrix with the L2 norm being less than or equal to the threshold value beta is taken as a training set B, and a set formed by the secondary matrixes corresponding to the remote control command matrix with the L2 norm being greater than the threshold value beta is taken as a training set A.
3. The method for detecting the anomaly of the telemetry data of the LSTM-integrated spacecraft as claimed in claim 1, wherein the training set A and the training set B obtained in the second step are used in the third step to train the LSTM model A and the LSTM model B respectively to obtain a trained LSTM model A and a trained LSTM model B; the specific process comprises the following steps:
respectively inputting the training set A and the training set B into an LSTM model A and an LSTM model B for training, setting the maximum iteration times of the LSTM model A and the LSTM model B for training to be alpha, continuously adjusting each weight matrix of the LSTM model A and the LSTM model B in each iteration training process, and stopping training until the maximum iteration times are reached to obtain the well-trained LSTM model A and the LSTM model B.
4. The method according to claim 1, wherein the method for detecting the anomaly of telemetry data of the LSTM-integrated spacecraft is characterized in that a final telemetry prediction sequence is determined according to a telemetry prediction value sequence formed by prediction values of telemetry values corresponding to each secondary matrix output by the LSTM model A, a telemetry prediction value sequence formed by prediction values of telemetry values corresponding to each secondary matrix output by the LSTM model B and fusion parameters
Figure FDA0002626935210000041
The method comprises the following steps:
for secondary matrix in test set
Figure FDA0002626935210000042
Secondary matrix
Figure FDA0002626935210000043
The corresponding remote control command matrix is
Figure FDA0002626935210000044
Secondary matrix of the image
Figure FDA0002626935210000045
Inputting the LSTM model A to obtain the predicted value of the remote measurement value output by the LSTM model A
Figure FDA0002626935210000046
Secondary matrix of the image
Figure FDA0002626935210000047
Inputting the LSTM model B to obtain the predicted value of the remote measurement value output by the LSTM model B
Figure FDA0002626935210000048
Similarly, the predicted value of the remote measurement value output by the LSTM model A and the predicted value of the remote measurement value output by the LSTM model B are input to other secondary matrixes in the test set;
then the remote measuring predicted value sequence formed by the predicted values of the remote measuring values corresponding to each secondary matrix output by the LSTM model A
Figure FDA0002626935210000049
Expressed as:
Figure FDA00026269352100000410
then the remote measuring predicted value sequence formed by the predicted values of the remote measuring values corresponding to each secondary matrix output by the LSTM model B
Figure FDA00026269352100000411
Expressed as:
Figure FDA00026269352100000412
and calculating a secondary matrix
Figure FDA00026269352100000413
Corresponding remote control instruction matrix
Figure FDA00026269352100000414
The L2 norm;
if the remote control instruction matrix
Figure FDA00026269352100000415
L2 norm is greater than threshold β, then secondary matrix
Figure FDA00026269352100000416
Corresponding final telemetry value prediction values
Figure FDA00026269352100000417
Expressed as:
Figure FDA00026269352100000418
if the remote control instruction matrix
Figure FDA00026269352100000419
L2 norm is less than or equal to threshold beta, then secondary matrix
Figure FDA00026269352100000420
Corresponding final telemetry value prediction values
Figure FDA00026269352100000421
Expressed as:
Figure FDA00026269352100000422
similarly, the final telemetering value predicted value corresponding to other secondary matrixes in the test set is obtained, and the final telemetering value predicted sequence
Figure FDA00026269352100000423
5. The method for detecting spacecraft telemetry data anomaly based on integrated LSTM according to claim 1, wherein smoothing is performed on an error sequence by using an exponential weighted average method to obtain a smoothed error sequence es(ii) a The specific process comprises the following steps:
Figure FDA00026269352100000424
Figure FDA00026269352100000425
for the smoothed error value at time t,
Figure FDA00026269352100000426
is the smoothing error value at the time t-1, and gamma is the threshold value of the smoothing error;
the smoothing error sequence is
Figure FDA00026269352100000427
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