CN110633516B - Method for predicting performance degradation trend of electronic device - Google Patents

Method for predicting performance degradation trend of electronic device Download PDF

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CN110633516B
CN110633516B CN201910812431.0A CN201910812431A CN110633516B CN 110633516 B CN110633516 B CN 110633516B CN 201910812431 A CN201910812431 A CN 201910812431A CN 110633516 B CN110633516 B CN 110633516B
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刘震
梅文娟
杜立
程玉华
黄建国
白利兵
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University of Electronic Science and Technology of China
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Abstract

The invention discloses a method for predicting performance degradation trend of an electronic device, which is characterized in that a prediction model of an initial correlation entropy extreme learning machine is established in an off-line manner, a dynamic correlation entropy extreme learning machine is updated on line, a code book mechanism combined with singular values is constructed, and the singular values in historical data are further identified, so that the influence of noise and singular values in the data on the prediction model is overcome, and the final prediction effect of the prediction model is improved.

Description

Method for predicting performance degradation trend of electronic device
Technical Field
The invention belongs to the technical field of reliability of sub-components, and particularly relates to a method for predicting performance degradation trend of an electronic device.
Background
With the rapid development of big data, cloud computing and industrial internet, intelligent troubleshooting and health management of electronic devices have been receiving attention in recent years due to the advantages of sharing resources and related services. Meanwhile, along with the development of intelligent health management, the information interaction speed between the electronic equipment and the health management platform is increased, and the information scale is enlarged. In this respect, the acquisition of large data of the device helps to improve the accuracy of the prediction. On the other hand, the performance degradation trend data of the electronic equipment belongs to real-time flow, and has the characteristics of data output one by one, lower throughput compared with batch data in other prediction scenes, strict time delay requirement on a prediction model and the like, so that a new challenge is provided for the design of a performance degradation trend model. How to obtain accurate prediction results at high speed becomes one of the technical problems in the background of 4.0 of industry.
Most of the existing real-time flow prediction methods adopt a neural network to carry out off-line training on historical information of electronic equipment to obtain a prediction model, and then use the prediction model to carry out on-line prediction. However, for most electronic devices, the performance degradation trend is not obvious at an early stage, and the early history information cannot completely reflect the degradation rule thereof, so that a large error exists in the prediction of the performance degradation trend. On the other hand, the prediction model adopted by the existing method needs to adjust network parameters for many times in the training process, and the training time is long, so that the defect of long time delay exists in the application of real-time stream fault trend prediction. Different from the traditional neural network, the online extreme learning machine has the characteristics of rapidness and accuracy due to the characteristics of simple model, no need of adjusting hidden layer parameters and the like. Therefore, in recent years, the model and related improved methods have been widely used in real-time diagnostics. However, the method considers the statistical rule of all the historical information of the device when establishing the model. When the device is degraded, the prediction result is affected by the health state information of the device and the early degradation information, so that the current performance degradation trend cannot be well reflected by the prediction result. Meanwhile, the online learning machine adopts the minimum mean square criterion as the training basis of the output layer, so that the prediction model is easily influenced by non-Gaussian noise and singular values, and the prediction effect is reduced.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide a method for predicting the performance degradation trend of an electronic device.
In order to achieve the above object, the present invention provides a method for predicting a performance degradation trend of an electronic device, comprising the steps of:
(1) establishing a prediction model of an initial correlation entropy extreme learning machine in an off-line manner
(1.1) acquiring historical data { x) of the electronic device to be testedi,yiWherein i-1, 2, …, k-1, k-1 represents the historical number before real-time predictionNumber of data, xiReal-time data of the i-th moment of the electronic device under test, yiIs xiA corresponding expected degradation trend characteristic;
(1.2) randomly generating hidden layer weight { w) of related entropy extreme learning machine1,w2,…,wMAnd bias b1,b2,…,bmM represents the dimension of the hidden layer weight, and M represents the number of offsets;
(1.3) calculating hidden layer output H under historical data(k-1)；
Figure BDA0002185427720000021
Wherein T represents transpose, g (w)1,b1,x1) Representing the output of the 1 st historical data under the 1 st hidden layer node;
(1.4) calculating output layer output under historical data;
Figure BDA0002185427720000022
wherein the content of the first and second substances,
Figure BDA0002185427720000023
eithe error between the predicted value and the true value of the model at the ith moment is represented, and sigma is the related entropy variance; t is(k-1)The historical true output value of the electronic device at the k-1 moment is obtained;
(1.5) initializing a code book of the relevant entropy limit learning machine;
setting the length L of the codebookw：
Lw＝min{Lmax,k-1}
Wherein L ismaxThe maximum length used for the code book;
record information in the codebook:
Figure BDA0002185427720000031
Figure BDA0002185427720000032
Figure BDA0002185427720000033
Figure BDA0002185427720000034
wherein, WcbFor prediction error of data in the codebook, HcbFor hidden layer output of corresponding data, acbFor the associated entropy of the data in the codebook, TcbFor the actual output of the data in the codebook,
Figure BDA0002185427720000035
is the L < th > of code bookwThe hidden layer corresponding to each data is output,
Figure BDA0002185427720000036
is the L-th in code bookwActual output corresponding to the data;
(1.6) calculating information of singular value estimation;
Figure BDA0002185427720000037
Figure BDA0002185427720000038
wherein the content of the first and second substances,
Figure BDA0002185427720000039
for the variance of the singular value estimate, alpha is the singular value estimate threshold, lambda1、λ2、λ3Is a constant;
(2) acquiring real-time data x of the tested electronic device at the kth momentkAs input to a predictive model;
(3) calculating real-time data x input at the kth moment based on the prediction modelkCorresponding hidden layer output H(k)；
H(k)＝{g(w1,b1,xk),g(w2,b2,xk),…,g(wM,bm,xk)}
(4) And calculating a prediction result y at the k-th time(k)；
y(k)＝β(k-1)H(k-1)
(5) Acquiring real-time data x at the kth momentkDegradation information t ofk；
(6) Using the real-time data x at the k-th timekUpdating K(k)；
Figure BDA00021854277200000310
(7) And calculating the real-time data x at the kth momentkLower output layer output beta(k)；
Figure BDA00021854277200000311
(8) Updating information in the code book;
(8.1) if the number of samples in the codebook is less than LmaxThen the information update codebook is as follows:
Figure BDA0002185427720000041
Figure BDA0002185427720000042
Figure BDA0002185427720000043
Figure BDA0002185427720000044
(8.2) if the number of samples in the codebook has reached the maximum value LmaxThen the codebook is updated as follows:
Figure BDA0002185427720000045
Figure BDA0002185427720000046
Figure BDA0002185427720000047
Figure BDA0002185427720000048
(9) updating information of singular value estimation;
Figure BDA0002185427720000049
Figure BDA00021854277200000410
wherein the content of the first and second substances,
Figure BDA00021854277200000411
for the variance of the updated singular value estimate,
Figure BDA00021854277200000412
estimating a threshold value for the updated singular value;
(10) extracting singular value information in the historical data;
H*＝{hi|hi∈Hcb,wi≥αM 2}
Λ*＝diag{ai|ai∈acb,wi≥αM 2}
T*＝{ti|ti∈Tcb,wi≥αM 2}
(11) eliminating the influence of singular value information in a prediction model to obtain the output layer output beta at the kth moment(k)Thereby predicting the performance degradation trend of the tested electronic device at the kth moment;
β(k)＝(K(k)-H*TΛ*H*)-1(K(k)β(k)-H*TΛ*T*)。
the invention aims to realize the following steps:
the invention relates to a method for predicting performance degradation trend of an electronic device, which is characterized in that a prediction model of an initial correlation entropy extreme learning machine is established in an off-line manner, a code book mechanism combined with a singular value is constructed by updating a dynamic correlation entropy extreme learning machine on line, and then the singular value in historical data is identified, so that the influence of noise and the singular value in the data on the prediction model is overcome, and the final prediction effect of the prediction model is improved.
Meanwhile, the method for predicting the performance degradation trend of the electronic device further has the following beneficial effects:
(1) the dynamic correlation entropy extreme learning machine is constructed, and can be used for overcoming the influence of noise and singular values in data on a prediction model;
(2) the influence of the singular value in the prediction model is eliminated, so that the performance degradation trend of the electronic device can be accurately and quickly predicted;
(3) the invention has the characteristics of fast dynamic update, good prediction effect and high robustness.
Drawings
FIG. 1 is a flow chart of a method for predicting degradation trend of electronic device performance according to the present invention;
FIG. 2 is a graph of the predicted effect of the photocoupler;
fig. 3 is a comparison graph of prediction accuracy of the photocoupler.
Detailed Description
The following description of the embodiments of the present invention is provided in order to better understand the present invention for those skilled in the art with reference to the accompanying drawings. It is to be expressly noted that in the following description, a detailed description of known functions and designs will be omitted when it may obscure the main content of the present invention.
Examples
FIG. 1 is a flow chart of a method for predicting degradation trend of electronic device performance according to the present invention.
In this embodiment, as shown in fig. 1, a method for predicting performance degradation trend of an electronic device according to the present invention includes the following steps:
s1, establishing a prediction model of the initial correlation entropy extreme learning machine in an off-line manner
S1.1, acquiring historical data { x) of the electronic device to be testedi,yiWherein i-1, 2, …, k-1, k-1 represents the number of historical data before real-time prediction, and xiIs the i-th real-time data, y, of the electronic device to be testediIs xiA corresponding expected degradation trend characteristic;
s1.2, randomly generating hidden layer weight { w) of related entropy extreme learning machine1,w2,…,wMAnd bias b1,b2,…,bmM represents the dimension of the hidden layer weight, and M represents the number of offsets;
s1.3, calculating hidden layer output H under historical data(k-1)；
Figure BDA0002185427720000051
Wherein T represents transpose, g (w)1,b1,x1) Representing the output of the 1 st historical data under the 1 st hidden layer node;
s1.4, calculating output layer output under historical data;
Figure BDA0002185427720000052
wherein the content of the first and second substances,
Figure BDA0002185427720000061
eithe error between the predicted value and the true value of the model at the ith moment is represented, and sigma is the related entropy variance; t is(k-1)The historical true output value of the electronic device at the k-1 moment is obtained;
s1.5, initializing a code book of the relevant entropy extreme learning machine;
setting the length L of the codebookw：
Lw＝min{Lmax,k-1}
Wherein L ismaxThe maximum length used for a codebook;
record information in the codebook:
Figure BDA0002185427720000062
Figure BDA0002185427720000063
Figure BDA0002185427720000064
Figure BDA0002185427720000065
wherein, WcbFor prediction error of data in the codebook, HcbFor hidden layer output of corresponding data, acbFor the associated entropy of the data in the codebook, TcbFor the actual output of the data in the codebook,
Figure BDA0002185427720000066
is the L < th > of code bookwThe hidden layer corresponding to each data is output,
Figure BDA0002185427720000067
is the L < th > of code bookwActual outputs corresponding to the individual data, i.e., degradation information;
s1.6, calculating information of singular value estimation;
Figure BDA0002185427720000068
Figure BDA0002185427720000069
wherein the content of the first and second substances,
Figure BDA00021854277200000610
is the variance of the singular value estimation, alpha is the singular value estimation threshold, and med (-) represents the median calculation;
s2, collecting real-time data x of the tested electronic device at the kth momentkAs input to a predictive model;
s3, calculating the real-time data x input at the k-th time based on the prediction modelkCorresponding hidden layer output H(k)；
H(k)＝{g(w1,b1,xk),g(w2,b2,xk),…,g(wM,bm,xk)}
S4, calculating the prediction result y at the k-th moment(k)；
y(k)＝β(k-1)H(k-1)
S5, acquiring the real-time data x at the kth momentkDegradation information t ofk；
S6, real-time data x using the k timekUpdating K(k)；
Figure BDA0002185427720000071
S7, calculating the real-time data x at the k momentkLower output layer output beta(k)；
Figure RE-GDA0002277374300000072
S8, updating information in the code book;
s8.1, if the number of samples in the code book is less than LmaxThen the information update codebook is as follows:
Figure BDA0002185427720000073
Figure BDA0002185427720000074
Figure BDA0002185427720000075
Figure BDA0002185427720000076
s8.2, if the number of samples in the code book reaches the maximum value LmaxThen the codebook is updated as follows:
Figure BDA0002185427720000077
Figure BDA0002185427720000078
Figure BDA0002185427720000079
Figure BDA00021854277200000710
s9, updating information of singular value estimation;
Figure BDA00021854277200000711
Figure BDA00021854277200000712
s10, extracting singular value information in the historical data;
H*＝{hi|hi∈Hcb,wi≥αM 2}
Λ*＝diag{ai|ai∈acb,wi≥αM 2}
T*＝{ti|ti∈Tcb,wi≥αM 2}
s11, eliminating the influence of singular value information in the prediction model to obtain the output layer output beta at the kth moment(k)Thereby predicting the performance degradation trend of the tested electronic device at the kth moment;
β(k)＝(K(k)-H*TΛ*H*)-1(K(k)β(k)-H*TΛ*T*)。
examples of the invention
In order to illustrate the technical effect of the invention, the invention is verified by taking the direct current transmission ratio real-time current prediction of the photoelectric coupler as an example.
The photoelectric coupler is an electronic component for converting electric energy and light energy, which transmits electric signals by taking light as a medium, and is used for isolating input and output electric signals. The direct current transmission ratio of the photoelectric coupler can effectively reflect the health state of the device. In order to verify the effectiveness of the method, a prediction model is established by the method, and the trend of real-time stream data in the degradation state of the photoelectric coupler is predicted.
Meanwhile, the method is compared with a relevant entropy limit learning machine (RCC-ELM), an online limit learning machine (MOS-ELM), an M estimation online limit learning machine (MOS-ELM) and an extreme survival error learning machine (ESEP-ELM). The off-line training precision and the on-line training precision are shown in table 1.
Algorithm RCC-ELM ESEP-ELM OSELM MOSELM The invention
Off-line training accuracy 4.68E-05 1.88E-04 4.61E-05 4.68E-05 4.61E-05
Real-time stream prediction accuracy 6.76E-04 2.62E-04 1.91E-04 1.88E-04 9.65E-05
TABLE 1
As can be seen from Table 1, the prediction model generated by the invention can achieve the optimal off-line training precision and the prediction precision under real-time streaming. As can be seen from comparison of prediction curves generated by the models in fig. 2, the off-line prediction method RCC-ELM is only trained on off-line data, so that the data rule of the photocoupler during degradation cannot be accurately captured, and therefore reliable prediction is hardly performed. ESEP-ELM, OSELM and MOS-ELM can effectively and reasonably predict the model, but are influenced by noise in data, the prediction result has large fluctuation, and large errors exist on partial nodes. Compared with the prior art, the method overcomes the influence of noise, the prediction curve can accurately reflect the performance degradation trend of the fault, the prediction curve has no large fluctuation, and the prediction effect is stable.
Fig. 3 compares the prediction errors of the respective methods for the respective samples, and it can be seen that the prediction errors of the first 10 sample points of the five methods are all low, while the information of the following sample points can show that RCC-ELM, ESEP-ELM, OSELM and MOS-ELM all have a phenomenon of reduced prediction accuracy, but the prediction accuracy of the present invention is relatively stable and reduced, which indicates that the algorithm has the capability of stably reflecting the data change trend and provides a reliable prediction result.
Table 2 shows the time comparison between the training and prediction of the five algorithms, and it can be seen that the prediction model of the present invention has a low training time, the prediction time is controlled within 0.5 second, and the present invention has advantages compared with other real-time algorithms, and can well adapt to the time requirement of performance degradation trend prediction under the real-time streaming condition.
Algorithm RCCELM ESEP-ELM OSELM MOS-ELM The invention
Test time(s) 0.2972 0.05113 0.00815 0.03764 0.00964
Predicting time(s) 0.03667 0.03462 0.5085 0.55803 0.45394
TABLE 2
Although illustrative embodiments of the present invention have been described above to facilitate the understanding of the present invention by those skilled in the art, it should be understood that the present invention is not limited to the scope of the embodiments, and various changes may be made apparent to those skilled in the art as long as they are within the spirit and scope of the present invention as defined and defined by the appended claims, and all inventions utilizing the inventive concept are protected.

Claims (1)

1. A method for predicting performance degradation trend of an electronic device is characterized by comprising the following steps:
(1) establishing a prediction model of an initial correlation entropy extreme learning machine in an off-line manner
(1.1) acquiring historical data { x) of the electronic device to be testedi,yiWherein i-1, 2, …, k-1, k-1 represents the number of historical data before real-time prediction, and xiAs real-time data at the i-th moment of the electronic device under test, yiIs xiA corresponding expected degradation trend characteristic;
(1.2) randomly generating hidden layer weight { w) of related entropy extreme learning machine1,w2,…,wMAnd bias b1,b2,…,bmM represents the dimension of the hidden layer weight, and M represents the number of offsets;
(1.3) calculating hidden layer output H under historical data(k-1)；
Figure FDA0003556125850000011
Wherein T represents transpose, g (w)1,b1,x1) Representing the output of the 1 st historical data under the 1 st hidden layer node;
(1.4) calculating output layer output under historical data;
Figure FDA0003556125850000012
wherein the content of the first and second substances,
Figure FDA0003556125850000013
eifor predicted and true values of model at i-th momentError, σ is the associated entropy variance; t is a unit of(k-1)The historical true output value of the electronic device at the k-1 moment is obtained;
(1.5) initializing a code book of the relevant entropy limit learning machine;
setting the length L of the codebookw：
Lw＝min{Lmax,k-1}
Wherein L ismaxThe maximum length used for the code book;
record information in the codebook:
Figure FDA0003556125850000021
Figure FDA0003556125850000022
Figure FDA0003556125850000023
Figure FDA0003556125850000024
wherein, WcbFor prediction error of data in the codebook, HcbFor hidden layer output of corresponding data, acbFor the associated entropy of the data in the codebook, TcbFor the actual output of the data in the codebook,
Figure FDA0003556125850000025
is the L < th > of code bookwThe hidden layer corresponding to each data is output,
Figure FDA0003556125850000026
is the L < th > of code bookwActual output corresponding to the data;
(1.6) calculating information of singular value estimation;
Figure FDA0003556125850000027
Figure FDA0003556125850000028
wherein the content of the first and second substances,
Figure FDA0003556125850000029
for the variance of the singular value estimate, alpha is the singular value estimate threshold, lambda1、λ2、λ3Constant, med (-) represents the median calculation;
(2) acquiring real-time data x of the tested electronic device at the kth momentkAs input to a predictive model;
(3) calculating real-time data x input at the kth moment based on the prediction modelkCorresponding hidden layer output H(k)；
H(k)＝{g(w1,b1,xk),g(w2,b2,xk),…,g(wM,bm,xk)}
(4) And calculating a prediction result y at the k-th time(k)；
y(k)＝β(k-1)H(k-1)
(5) And acquiring real-time data x at the kth momentkDegradation information t ofk；
(6) Using the real-time data x at the k-th timekUpdating K(k)；
Figure FDA00035561258500000210
(7) And calculating the real-time data x at the kth momentkLower output layer output beta(k)；
Figure FDA00035561258500000211
(8) Updating the information in the code book;
(8.1) if the number of samples in the codebook is less than LmaxThen the codebook is updated as follows:
Figure FDA0003556125850000031
Figure FDA0003556125850000032
Figure FDA0003556125850000033
Figure FDA0003556125850000034
(8.2) if the number of samples in the codebook has reached the maximum value LmaxThen the codebook is updated as follows:
Figure FDA0003556125850000035
Figure FDA0003556125850000036
Figure FDA0003556125850000037
Figure FDA0003556125850000038
(9) updating information of singular value estimation;
Figure FDA0003556125850000039
Figure FDA00035561258500000310
wherein the content of the first and second substances,
Figure FDA00035561258500000311
for the variance of the updated singular value estimate,
Figure FDA00035561258500000312
estimating a threshold value for the updated singular value;
(10) extracting singular value information in the historical data;
H*＝{hi|hi∈Hcb,wi≥α2}
Λ*＝diag{ai|ai∈acb,wi≥α2}
T*＝{ti|ti∈Tcb,wi≥α2}
(11) eliminating the influence of singular value information in a prediction model to obtain the output layer output beta at the kth moment(k)Thereby predicting the performance degradation trend of the tested electronic device at the kth moment;
β(k)＝(K(k)-H*TΛ*H*)-1(K(k)β(k)-H*TΛ*T*)。
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