CN113239654A - IGBT remaining life prediction method based on FIG and IPSO algorithm - Google Patents

IGBT remaining life prediction method based on FIG and IPSO algorithm Download PDF

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CN113239654A
CN113239654A CN202110558620.7A CN202110558620A CN113239654A CN 113239654 A CN113239654 A CN 113239654A CN 202110558620 A CN202110558620 A CN 202110558620A CN 113239654 A CN113239654 A CN 113239654A
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CN113239654B (en
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王尚亭
刘震
程玉华
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University of Electronic Science and Technology of China
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Abstract

The invention discloses an IGBT (insulated gate bipolar transistor) residual life prediction method based on FIG (Fig) and IPSO (Internet protocol Security) algorithms, which comprises the steps of firstly obtaining collector-emitter saturation voltage drop time sequence samples of a plurality of groups of IGBTs through an accelerated IGBT life experiment and carrying out normalization processing; processing the normalized sample by using a fuzzy information granulation processing algorithm (FIG), so that the saturated voltage drop of the collector and emitter of the IGBT is converted from point prediction to interval prediction; then, a weight combination prediction model is constructed through high-order polynomial fitting, and the weight combination prediction model is optimized through an IPSO algorithm; and finally, predicting the residual service life of the IGBT in real time.

Description

IGBT remaining life prediction method based on FIG and IPSO algorithm
Technical Field
The invention belongs to the technical field of reliability analysis of power semiconductor devices, and particularly relates to a method for predicting the residual life of an IGBT (insulated gate bipolar translator) based on an FIG (field-induced breakdown) algorithm and an IPSO (Internet protocol security) algorithm.
Background
An Insulated Gate Bipolar Transistor (IGBT) is a power-level semiconductor element, can realize high-voltage and low-voltage conversion, has the characteristics of high efficiency and high switching speed, and is widely applied to key fields of national defense and military industry, new energy power generation and the like. The IGBT module plays a key role as a core part of the system, directly affecting the performance and reliability of the whole system, and the module is susceptible to the working conditions, so accurately predicting the Remaining service Life (RUL) of the IGBT is the key to optimize the service Life. Prediction techniques for the remaining service life of the IGBT module help engineers schedule maintenance times, optimize operating efficiency and avoid planning out to present a module failure.
In view of the above problems, there are currently two main solutions: a prediction technique based on degradation mechanism analysis and a prediction technique based on data driving. The prediction technology based on the degradation mechanism analysis can obtain the RUL information of the IGBT device from the material perspective, but needs to have quite deep understanding and analysis on the essential characteristics of the device manufacturing material and the process manufacturing process. Due to constraints such as complexity of an electronic system and uncertainty of an external environment of the electronic system, a physical model obtained by analyzing a degradation principle of a device is prone to lose a nonlinear relation among object parameters, and insufficient accuracy is achieved. On the other hand, the constructed model is often closely related to the specific model of the device, which is in conflict with the rapidly increasing product types of the IGBTs in the market, so that the method necessarily brings hysteresis of the prediction result. Therefore, the establishment of an accurate degradation mechanism model is complex and is rarely applied to engineering. The prediction technology based on data driving learns the mapping relation between input and output from historical aging data of the IGBT device, and establishes a nonlinear, non-transparent and non-specific object-oriented model inside the IGBT device to calculate the RUL value of the related device. The method based on data driving has strong adaptability and high real-time performance. If an accurate residual life prediction model of the IGBT can be established, the prediction precision is greatly improved.
In the big data information age, processing, analysis, and prediction of data can help us solve many problems. Machine Learning (ML) is an indispensable method for large data prediction. The Swarm Intelligence (SI) is mainly to simulate the swarm behavior of insects, animals, birds and fish. And adopting an SI algorithm to take parameters needing to be optimized as an optimization target, and taking the ML result as an optimized fitness value. Improved Particle Swarm Optimization (IPSO) is Improved on the basis of one of the common SI algorithms.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides an IGBT (insulated gate bipolar transistor) residual life prediction method based on a FIG (Fuzzy Information Granulation) algorithm and an IPSO (Internet protocol security) algorithm.
In order to achieve the above object, the present invention provides a method for predicting remaining life of an IGBT based on FIG and IPSO algorithms, which is characterized by comprising the following steps:
(1) collecting collector-emitter saturation voltage drop time sequence samples of N groups of IGBTs;
(1.1) acquiring collector-emitter saturation voltage drop time sequence samples of N groups of IGBTs by accelerating IGBT life experiment
Figure BDA0003078078390000021
N is 1,2, …, N, and is used as a characteristic parameter of the IGBT failure process, wherein,
Figure BDA0003078078390000022
representing the M-th collector-emitter saturation voltage drop data in the n-th group of samples, wherein M represents the maximum number of collected data in the n-th group of samples;
(1.2) voltage drop time series for collector-emitter saturation
Figure BDA0003078078390000023
Carrying out normalization processing;
each will be
Figure BDA0003078078390000024
According to a single mapping function:
Figure BDA0003078078390000025
performing a normalization, wherein
Figure BDA0003078078390000026
Time sequence of voltage drop for collector-emitter saturation
Figure BDA0003078078390000027
Each of which
Figure BDA0003078078390000028
After the above normalization, each component is normalized to [0,1 ]]Number of (2) to obtain a data set Xn＝(xn1,xn2,…,xnm,…,xnM)；
(2) For data set XnCarrying out fuzzy information granulation processing (FIG);
(2.1) on dataset Xn＝(xn1,xn2,…,xnm,…,xnM) Windowing: in data set XnIn the method, every J elements are divided into a window, F is the number of the windows, F represents the total number of the windows, and F is 1,2, …, F,
Figure BDA00030780783900000211
(2.2) arranging J elements in the f-th window in an ascending order, and representing the f-th window after the ordering as
Figure BDA0003078078390000029
Representing the jth element in the f window in the nth set of samples;
(2.3) with XnfConstructing a fuzzy particle g for a domain of discourse;
Figure BDA00030780783900000210
g is a fuzzy concept and comprises three types of Low, R and Up, wherein Low represents the minimum value of the change of the fuzzy particle G, R represents the average value of the change of the fuzzy particle G, and Up represents the maximum value of the change of the fuzzy particle G;
(2.4) taking a triangular fuzzy particle form as a membership function, and depicting the fuzzy particles g through the membership function;
Figure BDA0003078078390000031
wherein a is the support lower boundary of the triangular fuzzy particle, b is the support upper boundary of the triangular fuzzy particle, and d is the nuclear parameter of the triangular fuzzy particle;
(2.5) granulating the engraved fuzzy particles g by adopting a Witold Pelrycz fuzzy granulation algorithm, and obtaining three parameter values of a, b and d in the granulation process;
Figure BDA0003078078390000032
Figure BDA0003078078390000033
Figure BDA0003078078390000034
(2.6) repeating steps (2.2) - (2.5) for each window XnfCarrying out fuzzy information granulation treatment to obtain the fuzzy information
Figure BDA0003078078390000035
And
Figure BDA0003078078390000036
(3) performing high-order polynomial fitting on the data of the three levels of Low, R and Up under the f-th window in the nth group of samples;
(3.1) setting the coefficient matrix of the fitting function under the f window in the n group of samples as
Figure BDA0003078078390000037
Represents the alpha coefficient under the f window in the n group of samples; setting the parameter matrix of the fitting function under the f window in the n group of samples as
Figure BDA0003078078390000041
Representing the f-th window in the n-th set of samples
Figure BDA0003078078390000042
The number of the parameters is one,
Figure BDA0003078078390000043
representing the degree of the fitting polynomial, superscript T representing the transposition, G representing three fuzzy sets, including
Figure BDA0003078078390000044
Figure BDA0003078078390000045
Calculating the fitting value under the f window in the n group of samples
Figure BDA0003078078390000046
Figure BDA0003078078390000047
(3.2) adopting a least square method to carry out matrix on the coefficient under the f window in the n group of samples
Figure BDA0003078078390000048
Solving is carried out to minimize R value in the following function, and corresponding coefficient matrix is recorded
Figure BDA0003078078390000049
Figure BDA00030780783900000410
Wherein,
Figure BDA00030780783900000411
representing the actual value of the fuzzy information granulation processing under the f window in the n group of samples;
(3.3) performing high-order polynomial fitting on the data of the N groups of samples at three levels of Low, R and Up according to the method of the steps (2) to (3.2) to obtain a data matrix (X)G)*；
Figure BDA00030780783900000412
(4) Constructing a weight combination prediction model XG；
XG＝WG(XG)*+EG
Wherein, WGIs XGIn the form of a row weight matrix of
Figure BDA00030780783900000413
Line weight
Figure BDA00030780783900000414
Satisfies the following conditions:
Figure BDA00030780783900000415
and is
Figure BDA00030780783900000416
EGIs a matrix of errors between the actual values and the fitted values, in the form of
Figure BDA00030780783900000417
(5) Adopting the improved adaptive particle swarm algorithm to carry out row weight matrix WGOptimizing;
(5.1) establishing an initial population: corresponding each particle of the initial population to a weight matrix
Figure BDA00030780783900000418
Each of the elements of (a); setting the size of the population as sizepop; establishing inertia weight B and learning factor c of initial population1And c2And the maximum number of iterations is kmaxInitializing the current iteration number k to be 1;
(5.2) establishing a fitness function zeta by utilizing residual square sum RSS indexes in the least square methodRSS；
Figure BDA0003078078390000051
(5.3) solving for min (ζ) by least squaresRSS)：
Figure BDA0003078078390000052
(5.4) updating the speed and the position of the nth particle after the kth iteration;
Figure BDA0003078078390000053
Figure BDA0003078078390000054
wherein,
Figure BDA0003078078390000055
respectively representing the velocity and position of the nth particle after the kth iteration,
Figure BDA0003078078390000056
respectively representing the updated velocity and position of the nth particle,
Figure BDA0003078078390000057
representing the individual extreme position and the global extreme position of the nth particle at the kth generation; r is1,r2Is a random number between 0 and 1;
similarly, the speed and the position of all the particles after the k iteration are updated according to the formula;
(5.5) calculating the fitness function value after updating all the particles
Figure BDA0003078078390000058
Comparing the updated fitness value of each particle with the fitness value corresponding to the individual extreme value, if the updated fitness value of a certain particle is smaller than the fitness value corresponding to the individual extreme value, replacing the individual extreme value with the particle, and recording the corresponding optimal position; otherwise, keeping the individual extreme value unchanged; finally, selecting the individual extreme value with the minimum fitness value and the corresponding optimal position from all the individual extreme values as a global extreme value;
(5.6) checking whether the current iteration number k reaches the maximum iteration number kmaxIf yes, stopping iteration, and outputting the optimal position corresponding to the global extreme value
Figure BDA0003078078390000059
And storing the corresponding prediction model; otherwise, adding 1 to the current iteration number k, and then returning to the step (5.4)
(6) According to the optimal position
Figure BDA00030780783900000510
The corresponding prediction model carries out advanced prediction on the data group to be predicted;
(6.1) Performance failure threshold x for a given IGBTthreshold；
(6.2) collecting the collector-emitter saturation voltage drop time sequence of the IGBT to be predicted at the first t moments
Figure BDA00030780783900000511
Wherein,
Figure BDA00030780783900000512
represents the collected p-th collector-emitter saturation voltage drop data and satisfies the following conditions:
Figure BDA0003078078390000061
p represents the number of data collected at the first t moments;
(6.3) mixing
Figure BDA0003078078390000062
Fitting the obtained data matrix according to the method described in steps (1.2) - (3.3)
Figure BDA0003078078390000063
(6.4) matching the optimal weight matrix
Figure BDA0003078078390000064
Input prediction model
Figure BDA0003078078390000065
To obtain
Figure BDA0003078078390000066
(6.5) determination
Figure BDA0003078078390000067
Whether or not to equal or exceed a performance failure threshold xthresholdIf the current time t exceeds the threshold value, recording the current time t as the critical point of the residual service life; otherwise, returning to the step (6.2) to continue to acquire data at the t +1 th moment, and processing the collector-emitter saturation voltage drop time sequence at the previous t +1 moments according to the steps (6.3) - (6.5).
The invention aims to realize the following steps:
according to the IGBT residual life prediction method based on the FIG and the IPSO algorithm, firstly, through an accelerated IGBT life experiment, collecting and emitting electrode saturation voltage drop time sequence samples of a plurality of groups of IGBTs are obtained and normalized; processing the normalized sample by using a fuzzy information granulation processing algorithm (FIG), so that the saturated voltage drop of the collector and emitter of the IGBT is converted from point prediction to interval prediction; then, a weight combination prediction model is constructed through high-order polynomial fitting, and the weight combination prediction model is optimized through an IPSO algorithm; and finally, predicting the residual service life of the IGBT in real time.
Meanwhile, the IGBT remaining life prediction method based on the FIG and the IPSO algorithm also has the following beneficial effects:
(1) the fuzzy information granulation is utilized to process the collector-emitter saturation pressure drop data, point prediction is converted into interval prediction, the influence of the characteristics of violent fluctuation and instability of the acquired time series data on the prediction is reduced, the prediction specific data is converted into the prediction data variation range, and the prediction reliability is improved.
(2) And combining the fitting results of all groups of fitting data by using different weighting strategies by using a combined weight method so as to predict the residual service life, and reducing the proportion of a sample data group with larger deviation with the test data so as to improve the prediction precision.
Drawings
FIG. 1 is a flowchart of the IGBT remaining life prediction method based on FIG and IPSO algorithm;
FIG. 2 is a schematic diagram of the IGBT's collector-emitter saturation voltage drop time series samples divided into training data and test data after fuzzy information granulation;
FIG. 3 is a three-dimensional waterfall graph of the raw data of saturation voltage drop of the 5 groups of IGBT collectors and emitters obtained from an accelerated life experiment;
FIG. 4 is a data diagram of normalized data of group 5 after fuzzy information granulation;
FIG. 5 is a result of scattering point data of IGBT collector-emitter saturation voltage drop samples to be predicted in an accelerated life experiment and a prediction curve under the IGBT remaining life prediction method based on fuzzy information granulation and IPSO algorithm provided by the invention;
fig. 6 is a three-dimensional waterfall graph comparing normalized data of all collected IGBT collector-emitter saturation voltage drops with a prediction curve, which is obtained in an accelerated life experiment.
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 subject matter of the present invention.
Examples
FIG. 1 is a flowchart of the IGBT remaining life prediction method based on FIG and IPSO algorithm;
in this embodiment, as shown in fig. 1, the method for predicting the remaining life of an IGBT based on FIG and IPSO algorithms of the present invention includes the following steps:
s1, collecting collector-emitter saturation voltage drop time sequence samples of the N-5 groups of IGBTs;
s1.1, through the accelerated IGBT lifetime experiment, as shown in fig. 1, acquiring collector-emitter saturation voltage drop time series samples of N-5 groups of IGBTs
Figure BDA0003078078390000071
n is 1,2, …,5, and is used as a characteristic parameter of the IGBT failure process, wherein,
Figure BDA0003078078390000072
representing the M-th collector-emitter saturation voltage drop data in the n-th group of samples, wherein M represents the number of collected data in the n-th group of samples; in this embodiment, taking the group 1 sample as an example, the number M of the collected data is 2232;
s1.2, collector-emitter saturation voltage drop time sequence
Figure BDA0003078078390000073
Carrying out normalization processing;
each will be
Figure BDA0003078078390000074
According to a single mapping function:
Figure BDA0003078078390000075
performing a normalization, wherein
Figure BDA0003078078390000076
Time sequence of voltage drop for collector-emitter saturation
Figure BDA0003078078390000077
Each of which
Figure BDA0003078078390000078
After the above normalization, each component is normalized to [0,1 ]]Number of (2) to obtain a data set Xn＝(xn1,xn2,…,xnm,…,xnM)；
S2, data set XnCarrying out fuzzy information granulation processing (FIG);
s2.1, data set Xn＝(xn1,xn2,…,xnm,…,xnM) Windowing: in data set XnIn the interval J, 5 elements are divided into a window, F is the number of the windows, F represents the total number of the windows, F is 1,2, …, F,
Figure BDA0003078078390000081
in this embodiment, taking the nth-1 group of samples as an example, the total number of windows is
Figure BDA0003078078390000082
S2.2, arranging 5 elements in the f-th window in an ascending order, and representing the f-th window after the f-th window is arranged in the ascending order
Figure BDA0003078078390000083
Representing the jth element in the f window in the nth set of samples;
s2.3, with XnfConstructing a fuzzy particle g for a domain of discourse;
Figure BDA0003078078390000084
g is a fuzzy concept and comprises three types of Low, R and Up, wherein Low represents the minimum value of the change of the fuzzy particle G, R represents the average value of the change of the fuzzy particle G, and Up represents the maximum value of the change of the fuzzy particle G;
s2.4, taking a triangular fuzzy particle form as a membership function, and depicting the fuzzy particles g through the membership function;
Figure BDA0003078078390000085
wherein a is the support lower boundary of the triangular fuzzy particle, b is the support upper boundary of the triangular fuzzy particle, and d is the nuclear parameter of the triangular fuzzy particle;
s2.5, granulating the engraved fuzzy particles g by adopting a Witold Pelrycz fuzzy granulation algorithm, and obtaining three parameter values of a, b and d in the granulation process;
Figure BDA0003078078390000086
Figure BDA0003078078390000087
Figure BDA0003078078390000088
s2.6, repeating the steps S2.2-S2.5, and aiming at each window XnfCarrying out fuzzy information granulation treatment to obtain the fuzzy information
Figure BDA0003078078390000091
And
Figure BDA0003078078390000092
s3, performing high-order polynomial fitting on the data of three levels of Low, R and Up in the f-th window in the n-th group of samples;
s3.1, setting a coefficient matrix of a fitting function under the f window in the n group of samples as
Figure BDA0003078078390000093
Represents the alpha coefficient under the f window in the n group of samples; setting parameters of fitting function under f window in n group of samplesThe matrix is
Figure BDA0003078078390000094
Representing the f-th window in the n-th set of samples
Figure BDA0003078078390000095
The number of the parameters is one,
Figure BDA0003078078390000096
representing the degree of the fitting polynomial, superscript T representing the transposition, G representing three fuzzy sets, including
Figure BDA0003078078390000097
Figure BDA0003078078390000098
Calculating the fitting value under the f window in the n group of samples
Figure BDA0003078078390000099
Figure BDA00030780783900000910
S3.2, adopting a least square method to carry out coefficient matrix under the f window in the n group of samples
Figure BDA00030780783900000911
Solving is carried out to minimize R value in the following function, and corresponding coefficient matrix is recorded
Figure BDA00030780783900000912
Figure BDA00030780783900000913
Wherein,
Figure BDA00030780783900000914
representing the actual value of the fuzzy information granulation processing under the f window in the n group of samples;
s3.3, and carrying out high-order polynomial fitting on the data of the N groups of samples at three levels of Low, R and Up according to the method of the steps S2-S3.2 to obtain a data matrix (X)G)*；
Figure BDA00030780783900000915
S4, constructing weight combination prediction model XG；
XG＝WG(XG)*+EG
Wherein, WGIs XGIn the form of a row weight matrix of
Figure BDA0003078078390000101
Line weight
Figure BDA0003078078390000102
Satisfies the following conditions:
Figure BDA0003078078390000103
EGis a matrix of errors between the actual values and the fitted values, in the form of
Figure BDA0003078078390000104
S5, adopting the improved adaptive particle swarm optimization to carry out row weight matrix WGOptimizing;
s5.1, establishing an initial population: corresponding each particle of the initial population to a weight matrix
Figure BDA0003078078390000105
Each of the elements of (a); setting the size of the population as sizepop; constructing inertia weight B and learning factor c of initial population1And c2And the maximum number of iterations is kmax100, and initializing the current iteration number k to 1;
in this embodiment, the inertia weight B keeps the inertia of the particle moving, so that it has a tendency to expand the search space. A proper B value can effectively give consideration to the search precision and the search speed, the global search and the local search, and in order to ensure the performance of the algorithm, the inertia weight B is adjusted according to the following formula:
Figure BDA0003078078390000106
wherein, Bmin＝0.8,Bmax＝1.5；
The learning factor is two parameters which represent the ability of the particle to optimally learn from the self history and optimally learn from the group history. Compared with the traditional classical learning factor value, the method can not only ensure the learning capability of the particles in the initial stage of iteration, but also strengthen the global search; the learning factor is adjusted through different evolution speeds of the particles, so that the particles adjust the learning mode by combining self conditions. Learning factor c1Representing the ability of the particle to approach the optimal potential of the individual, giving the particle a self-summarizing ability, a learning factor c2Representing the ability of a particle to approach a global optimum position, larger c early in the particle flight search1And smaller c2The particles can be better flown to the global optimal solution; in the later stages of particle flight search, the smaller c1And greater c2The particles can be made to fly in locally optimal directions, which is advantageous for accelerating the convergence of the particles. The learning factor adjustment strategy is as follows:
Figure BDA0003078078390000107
Figure BDA0003078078390000108
wherein, c1max＝2.5,c1min＝1.5,c2max＝2.5,c2min＝1.5；
S5.2, the invention introduces the important index residual error sum of squares (Res) in the least square methodActual Sum of Squares, RSS) seeks the best function match with the trend of the test data, and this index is used as a factor for evaluating the strong or weak adaptability. The smaller the fitness value is, the higher the optimization performance is; then, a fitness function zeta is established by utilizing the residual square sum RSS index in the least square methodRSSComprises the following steps:
Figure BDA0003078078390000111
s5.3, solving min (zeta) through a least square methodRSS)：
Figure BDA0003078078390000112
S5.4, updating the speed and the position of the nth particle after the kth iteration;
Figure BDA0003078078390000113
the updating of the particle position is related to the current position and the flight speed, when the particle is in a bad position and the flight speed is not ideal, the updated particle has poor adaptability, and the particle is guided to fly to the global optimal position based on a position updating formula; the particle position update adjustment formula is as follows:
Figure BDA0003078078390000114
wherein,
Figure BDA0003078078390000115
respectively representing the velocity and position of the nth particle after the kth iteration,
Figure BDA0003078078390000116
respectively representing the updated velocity and position of the nth particle,
Figure BDA0003078078390000117
representing the individual extreme position and the global extreme position of the nth particle at the kth generation; r is1,r2Is a random number between 0 and 1, and the values are all 0.5;
similarly, the speed and the position of all the particles after the k iteration are updated according to the formula;
s5.5, calculating the fitness function value after updating all the particles
Figure BDA0003078078390000118
Comparing the updated fitness value of each particle with the fitness value corresponding to the individual extreme value, if the updated fitness value of a certain particle is smaller than the fitness value corresponding to the individual extreme value, replacing the individual extreme value with the particle, and recording the corresponding optimal position; otherwise, keeping the individual extreme value unchanged; finally, selecting the individual extreme value with the minimum fitness value and the corresponding optimal position from all the individual extreme values as a global extreme value;
s5.6, checking whether the current iteration number k reaches the maximum iteration number kmaxIf the position is 100, stopping iteration and outputting the optimal position corresponding to the global extreme value
Figure BDA0003078078390000121
And storing the corresponding prediction model; otherwise, adding 1 to the current iteration number k, and then returning to the step S5.4;
s6, according to the best position
Figure BDA0003078078390000122
The corresponding prediction model carries out advanced prediction on the data group to be predicted;
s6.1, setting performance failure threshold x of IGBTthreshold＝0.15；
S6.2, collecting the collector-emitter saturation voltage drop time sequence of the IGBT to be predicted at the first t moments
Figure BDA0003078078390000123
Wherein,
Figure BDA0003078078390000124
represents the collected p-th collector-emitter saturation voltage drop data and satisfies the following conditions:
Figure BDA0003078078390000125
p represents the number of data acquired at the previous t moments, and at this time, P is 1905;
s6.3, mixing
Figure BDA0003078078390000126
Fitting the obtained data matrix according to the method described in steps S1.2-S3.3
Figure BDA0003078078390000127
S6.4, matching the optimal weight matrix
Figure BDA0003078078390000128
And (X)G)*Input prediction model
Figure BDA0003078078390000129
To obtain
Figure BDA00030780783900001210
S6.5, judging
Figure BDA00030780783900001211
Whether or not to equal or exceed a performance failure threshold xthresholdIf the current time t exceeds the threshold value, recording the current time t as the critical point of the residual service life; otherwise, returning to the step S6.2 to continuously acquire the data at the t +1 th moment, and processing the collector-emitter saturation voltage drop time sequence at the previous t +1 moments according to the steps S6.3-S6.5.
In this embodiment, to illustrate the technical effect of the present invention, a total of 6 groups of IGBT accelerated life experiments are completed for verification, wherein as shown in fig. 2, the first 5 groups of data are used for training a prediction model, and the 6 th group of data are used for real-time prediction. Fig. 3 is a three-dimensional waterfall graph of raw data of saturation voltage drops of 5 groups of IGBTs obtained by experiments. By observing the original data shown in fig. 3, the data can be found to have the characteristics of redundancy and high dimension. If the method is directly applied to life prediction, the efficiency and accuracy of prediction can be greatly reduced. Therefore, according to the actual needs and problem characteristics, in this embodiment, the fuzzy information granulation process is performed on the processed data set obtained in step S1, the fuzzy process of the present invention uses the triangular fuzzy particle form a (x, a, d, b) to determine G as the membership function, and 3 types of fuzzy granulated particles that obtain the same data sample are: low, R and Up to accurately predict the lifetime of the IGBT, and fig. 4 shows a data scattergram situation after fuzzy information granulation is performed on the 5 th group of data after normalization processing.
In order to select the optimal weight parameter in the weight combination prediction model to improve the prediction precision of the data to be predicted, an improved particle swarm algorithm is adopted to select the optimal weight parameter. In order to improve the fitting accuracy and guarantee the predicted undistorted condition, the polynomial fitting order is 9, the size of the population scale is set to be 200, and the maximum evolutionary number is 100. The design utilizes random numbers to establish an initial population.
Under the above parameter conditions, in order to better verify the prediction performance of the method proposed by the present invention, two experiments under different conditions were performed as follows: (1) testing the influence of different failure threshold APTs on the prediction accuracy of the residual life of the IGBT on the same training data point number and verification data point number, and analyzing the influence of different failure threshold APTs on the residual life of the IGBT; (2) by utilizing the running and timing functions of Matlab software, the prediction performance of the algorithm under different sizepop values of the algorithm provided by the invention is compared from two aspects of prediction precision and time consumption.
Table 1 shows the predicted results of the algorithm of the present invention in experiment (1), and Table 2 shows the predicted results in experiment (2).
TABLE 1
Failure threshold APT Actual value Prediction value Prediction error Relative error
0.15 2101 2120 19 0.904％
0.16 2108 2135 27 1.280％
0.17 2156 2155 1 0.046％
0.18 2169 2170 1 0.046％
0.19 2179 2178 1 0.046％
0.20 2194 2198 4 0.182％
In order to verify the superior performance of the algorithm of the model, the average absolute percentage Error (MSE) is calculated between the predicted data and the actual data in the test set. The MSE ranges from [0, + ∞), and an MSE of 0% indicates a perfect model, i.e., when the predicted value matches the true value completely.
Figure BDA0003078078390000131
TABLE 2
Figure BDA0003078078390000132
Figure BDA0003078078390000141
From table 1, it can be seen that when different failure thresholds APT are set, the residual life of the IGBT predicted based on fuzzy information granulation and the IPSO algorithm provided by the present invention has a relative error not exceeding 1.5%. In addition, fig. 5 (solid line part) shows a result graph of the average level prediction condition of the IGBT data of the data group to be predicted, which is predicted by the method provided by the present invention, and fig. 6 also shows a three-dimensional waterfall graph of a curve of the training data and the predicted average level R, in order to visually compare with the training data and to more visually recognize the result. By combining the table 1, the fig. 5 and the fig. 6, the method provided by the invention has the advantages that the overall trend of the IGBT experimental data can be well predicted, and the residual life can be more accurately predicted.
Besides, table 2 lists the time consumption of predicting the saturation voltage drop data of the IGBT collector and emitter in the group 6 of the accelerated life experiment by using sizepop with three population sizes, and it can be seen that when the algorithm sets different sizepop parameters, the root mean square error of the verification data is within 10%. Based on MAPE, we can judge the model is good.
In summary, the above experimental results show that the method can obtain a higher prediction progress under the condition of a small number of training samples on the basis of ensuring the operation efficiency.
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 matters of the invention which utilize the inventive concepts are protected.

Claims (3)

1. A prediction method for the residual life of an IGBT based on FIG and IPSO algorithm is characterized by comprising the following steps:
(1) collecting collector-emitter saturation voltage drop time sequence samples of N groups of IGBTs;
(1.1) acquiring collector-emitter saturation voltage drop time sequence samples of N groups of IGBTs by accelerating IGBT life experiment
Figure FDA0003078078380000011
N is 1,2, …, N, and is used as a characteristic parameter of the IGBT failure process, wherein,
Figure FDA0003078078380000012
representing the M-th collector-emitter saturation voltage drop data in the n-th group of samples, wherein M represents the maximum number of collected data in the n-th group of samples;
(1.2) voltage drop time series for collector-emitter saturation
Figure FDA0003078078380000013
Carrying out normalization processing;
each will be
Figure FDA0003078078380000014
According to a single mapping function:
Figure FDA0003078078380000015
performing a normalization, wherein
Figure FDA0003078078380000016
Time sequence of voltage drop for collector-emitter saturation
Figure FDA0003078078380000017
Each of which
Figure FDA0003078078380000018
After the above normalization, each component is normalized to [0,1 ]]Number of (2) to obtain a data set Xn＝(xn1,xn2,…,xnm,…,xnM)。
(2) For data set XnCarrying out fuzzy information granulation processing (FIG);
(2.1) on dataset Xn＝(xn1,xn2,…,xnm,…,xnM) Windowing: in data set XnIn the method, every J elements are divided into a window, F is the number of the windows, F represents the total number of the windows, and F is 1,2, …, F,
Figure FDA0003078078380000019
(2.2) arranging J elements in the f-th window in an ascending order, and representing the f-th window after the ordering as
Figure FDA00030780783800000110
Figure FDA00030780783800000111
Representing the jth element in the f window in the nth set of samples;
(2.3) with XnfConstructing a fuzzy particle g for a domain of discourse;
Figure FDA00030780783800000112
g is a fuzzy concept and comprises three types of Low, R and Up, wherein Low represents the minimum value of the change of the fuzzy particle G, R represents the average value of the change of the fuzzy particle G, and Up represents the maximum value of the change of the fuzzy particle G;
(2.4) taking a triangular fuzzy particle form as a membership function, and depicting the fuzzy particles g through the membership function;
Figure FDA0003078078380000021
wherein a is the support lower boundary of the triangular fuzzy particle, b is the support upper boundary of the triangular fuzzy particle, and d is the nuclear parameter of the triangular fuzzy particle;
(2.5) granulating the engraved fuzzy particles g by adopting a Witold Pelrycz fuzzy granulation algorithm, and obtaining three parameter values of a, b and d in the granulation process;
Figure FDA0003078078380000022
Figure FDA0003078078380000023
Figure FDA0003078078380000024
(2.6) repeating steps (2.2) - (2.5) for each window XnfCarrying out fuzzy information granulation processingAfter the treatment is finished, obtaining
Figure FDA0003078078380000025
And
Figure FDA0003078078380000026
(3) carrying out high-order polynomial fitting on the data of three levels of Low, R and Up under each window in the nth group of samples;
(3.1) setting the coefficient matrix of the fitting function under the f window in the n group of samples as
Figure FDA0003078078380000027
Figure FDA0003078078380000028
Represents the alpha coefficient under the f window in the n group of samples; setting the parameter matrix of the fitting function under the f window in the n group of samples as
Figure FDA0003078078380000029
Figure FDA00030780783800000210
Representing the f-th window in the n-th set of samples
Figure FDA00030780783800000211
The number of the parameters is one,
Figure FDA00030780783800000212
representing the degree of the fitting polynomial, superscript T representing the transposition, G representing three fuzzy sets, including
Figure FDA00030780783800000213
Figure FDA00030780783800000214
Calculate the nth groupFit value at f window in sample
Figure FDA0003078078380000031
Figure FDA0003078078380000032
(3.2) adopting a least square method to carry out matrix on the coefficient under the f window in the n group of samples
Figure FDA0003078078380000033
Solving is carried out to minimize R value in the following function, and corresponding coefficient matrix is recorded
Figure FDA0003078078380000034
Figure FDA0003078078380000035
Wherein,
Figure FDA0003078078380000036
representing the actual value of the fuzzy information granulation processing under the f window in the n group of samples;
(3.3) performing high-order polynomial fitting on the data of the N groups of samples at three levels of Low, R and Up according to the method of the steps (2) to (3.2) to obtain a data matrix (X)G)*；
Figure FDA0003078078380000037
(4) Constructing a weight combination prediction model XG；
XG＝WG(XG)*+EG
Wherein, WGIs XGIn the form of a row weight matrix of
Figure FDA0003078078380000038
Line weight
Figure FDA0003078078380000039
Satisfies the following conditions:
Figure FDA00030780783800000310
and is
Figure FDA00030780783800000311
EGIs a matrix of errors between the actual values and the fitted values, in the form of
Figure FDA00030780783800000312
(5) Adopting the improved adaptive particle swarm algorithm to carry out row weight matrix WGOptimizing;
(5.1) establishing an initial population: corresponding each particle of the initial population to a weight matrix
Figure FDA00030780783800000313
Each of the elements of (a); setting the size of the population as sizepop; constructing inertia weight B and learning factor c of initial population1And c2And the maximum number of iterations is kmaxInitializing the current iteration number k to be 1;
(5.2) establishing a fitness function zeta by utilizing residual square sum RSS indexes in the least square methodRSS；
Figure FDA0003078078380000041
(5.3) solving for min (ζ) by least squaresRSS)：
Figure FDA0003078078380000042
(5.4) updating the speed and the position of the nth particle after the kth iteration;
Figure FDA0003078078380000043
Figure FDA0003078078380000044
wherein,
Figure FDA0003078078380000045
respectively representing the velocity and position of the nth particle after the kth iteration,
Figure FDA0003078078380000046
respectively representing the updated velocity and position of the nth particle,
Figure FDA0003078078380000047
representing the individual extreme position and the global extreme position of the nth particle at the kth generation;
similarly, the speed and the position of all the particles after the k iteration are updated according to the formula;
(5.5) calculating the fitness function value after updating all the particles
Figure FDA0003078078380000048
Comparing the updated fitness value of each particle with the fitness value corresponding to the individual extreme value, if the updated fitness value of a certain particle is smaller than the fitness value corresponding to the individual extreme value, replacing the individual extreme value with the particle, and recording the corresponding optimal position; otherwise, keeping the individual extreme value unchanged; finally, selecting the individual extreme value with the minimum fitness value and the corresponding optimal position from all the individual extreme values as a global extreme value;
(5.6) checking whether the current iteration number k reaches the maximum iteration number kmaxIf so, thenStopping iteration, and outputting the optimal position corresponding to the global extreme value
Figure FDA0003078078380000049
And storing the corresponding prediction model; otherwise, adding 1 to the current iteration number k, and then returning to the step (5.4)
(6) According to the optimal position
Figure FDA00030780783800000410
The corresponding prediction model carries out advanced prediction on the data group to be predicted;
(6.1) Performance failure threshold x for a given IGBTthreshold；
(6.2) collecting the collector-emitter saturation voltage drop time sequence of the IGBT to be predicted at the first t moments
Figure FDA00030780783800000411
Wherein,
Figure FDA00030780783800000412
represents the collected p-th collector-emitter saturation voltage drop data and satisfies the following conditions:
Figure FDA00030780783800000413
p represents the number of data collected at the first t moments;
(6.3) mixing
Figure FDA00030780783800000414
Fitting the obtained data matrix according to the method described in steps (1.2) - (3.3)
Figure FDA00030780783800000415
(6.4) matching the optimal weight matrix
Figure FDA0003078078380000051
And (X)G)*Input prediction model
Figure FDA0003078078380000052
To obtain
Figure FDA0003078078380000053
(6.5) determination
Figure FDA0003078078380000054
Whether or not to equal or exceed a performance failure threshold xthresholdIf the current time t exceeds the threshold value, recording the current time t as the critical point of the residual service life; otherwise, returning to the step (6.2) to continue to acquire data at the t +1 th moment, and processing the collector-emitter saturation voltage drop time sequence at the previous t +1 moments according to the steps (6.3) - (6.5).
2. The method for predicting the remaining life of the IGBT based on the FIG and IPSO algorithm according to claim 1, wherein the inertia weight B satisfies the following condition:
Figure FDA0003078078380000055
wherein, Bmax,BminRespectively the maximum value and the minimum value of the given inertia weight, and lambda is a constant;
3. the IGBT remaining life prediction method based on FIG and IPSO algorithm of claim 1, wherein the learning factor c1、c2Satisfies the following conditions:
Figure FDA0003078078380000056
Figure FDA0003078078380000057
wherein, c1maxRepresents a learning factor c1The upper limit of the adjustment of (a),c1minthen represents the learning factor c1The lower limit of adjustment of (2); c. C2maxRepresents a learning factor c2Lower limit of adjustment of c2minThen represents the learning factor c2And adjusting the lower limit.
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