CN113987900A - IGBT service life prediction method based on extended Kalman particle filter - Google Patents
IGBT service life prediction method based on extended Kalman particle filter Download PDFInfo
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Abstract
The invention discloses an IGBT service life prediction method based on extended Kalman particle filtering, which comprises the following steps: s1, acquiring historical data of the IGBT device; s2, preprocessing data and establishing a life model of the IGBT; s3, establishing a prediction equation by using an extended Kalman particle filter algorithm; and S4, predicting the residual service life of the IGBT. The method improves the particle filter algorithm and is used for predicting the residual service life of the IGBT, and the method improves the accuracy of life prediction.
Description
Technical Field
The invention relates to the field of semiconductors, in particular to an IGBT service life prediction method based on extended Kalman particle filtering.
Background
In recent years, power electronic equipment is rapidly developed and applied to many fields, but due to the complexity of the working environment, the core device, namely an insulated gate bipolar transistor (IGBT for short), of the power electronic equipment often fails. Therefore, the reliability assessment of the IGBT operation condition is of great significance, if an effective state assessment and early warning scheme can be provided, the occurrence of catastrophic accidents can be avoided, and therefore the research on the service life prediction direction of the IGBT device is of great practical significance.
Disclosure of Invention
The invention provides an IGBT service life prediction method based on extended Kalman particle filtering. The method utilizes the improved particle filter algorithm to predict the residual service life of the IGBT, and compared with the prior art, the method improves the accuracy of prediction.
In order to achieve the purpose of the invention, the invention adopts the technical scheme that:
the IGBT service life prediction method based on the extended Kalman particle filter comprises the following steps:
s1, acquiring historical data of the IGBT device;
s2, preprocessing data and establishing a life model of the IGBT;
s3, establishing a prediction equation by using an extended Kalman particle filter algorithm;
and S4, predicting the residual service life of the IGBT.
Further, the specific method of step S1 is:
and carrying out a power cycle accelerated aging experiment on the IGBT device, acquiring the conduction voltage drop between a collector and an emitter of the IGBT device in the aging process, and establishing a historical data set.
Further, the specific method of step S2 includes the following steps:
s2-1, eliminating error points generated by measurement by using a Rhein criterion;
s2-2, taking the average value of the data in the single cycle as the characteristic value of the cycle;
s2-3, mapping the data samples into the same range according to a uniform standard through scale transformation;
s2-4, performing curve fitting on the processed data, establishing a primary life model, and determining the initial values of the model parameters.
Further, the specific method of step S3 includes the following steps:
s3-1, establishing a state equation f and an observation equation h according to the life model;
s3-2, setting the number of particles as N, and initializing a particle set, namely extracting an initial state from the prior distribution P;
s3-3, calculating the Jacobian of the state transition matrix for each particleComparable matrix F(i)Noise driving matrix G(i)And observe the noise drive matrix U(i)(ii) a Wherein i corresponds to the ith particle;
s3-4, updating the particle set by using an extended Kalman filter algorithm in the importance sampling stage, and specifically calculating as follows:
wherein k corresponds to the kth data in the data set, i.e. represents the kth cycle; pre represents a corresponding predicted value; k represents a Kalman gain; r is the noise variance; z is a system observation value, namely data in a historical data set;
obtaining the mean value of the sample by the above calculation
Sum covariance
And then updating the particle set by using the particle set, wherein the specific calculation is as follows:
s3-5, calculating the weight value of each particle
The specific calculation is as follows:
wherein p represents prior probability and q represents posterior probability;
s3-6, carrying out normalization processing on the weight values, and then calculating the weighted average value of the particle set as output, thereby carrying out state updating, namely updating the parameters in the life model and determining the prediction equation.
Further, the specific method of step S4 is:
and setting an early warning threshold value, substituting the early warning threshold value into a prediction equation, and calculating to obtain the remaining service life of the IGBT device.
The invention has the beneficial effects that: according to the invention, the residual service life under the current working state can be obtained only by acquiring historical data of the IGBT device in the aging process and then processing and calculating by using the proposed method, and the more the data volume is, the more accurate the prediction result is. Compared with the prior art, the method greatly improves the accuracy of prediction, and is simple and high in practicability.
Drawings
FIG. 1 is a schematic flow diagram of the process;
fig. 2 is a diagram comparing the method with the basic particle filter.
Detailed Description
The following description of the embodiments of the present invention is provided to facilitate the understanding of the present invention by those skilled in the art, but it should be understood that the present invention is not limited to the scope of the embodiments, and it will be apparent to those skilled in the art that various changes may be made without departing from the spirit and scope of the invention as defined and defined in the appended claims, and all matters produced by the invention using the inventive concept are protected.
As shown in fig. 1, the IGBT life prediction method based on the extended kalman particle filter includes the following steps:
s1, acquiring historical data of the IGBT device;
s2, preprocessing data and establishing a life model of the IGBT;
s3, establishing a prediction equation by using an extended Kalman particle filter algorithm;
and S4, predicting the residual service life of the IGBT.
The specific method of step S1 is:
and carrying out a power cycle accelerated aging experiment on the IGBT device, acquiring the conduction voltage drop between a collector and an emitter of the IGBT device in the aging process, and establishing a historical data set.
The specific method of step S2 includes the steps of:
s2-1, eliminating error points generated by measurement by using a Rhein criterion;
s2-2, taking the average value of the data in the single cycle as the characteristic value of the cycle;
s2-3, mapping the data samples into the same range according to a uniform standard through scale transformation;
s2-4, performing curve fitting on the processed data, establishing a primary life model, and determining the initial values of the model parameters.
The specific method of step S3 includes the steps of:
s3-1, establishing a state equation f and an observation equation h according to the life model;
s3-2, setting the number of particles as N, and initializing a particle set, namely extracting an initial state from the prior distribution P;
s3-3, calculating Jacobian matrix F of state transition matrix of each particle(i)Noise driving matrix G(i)And observe the noise drive matrix U(i)(ii) a Wherein i corresponds to the ith particle;
s3-4, updating the particle set by using an extended Kalman filter algorithm in the importance sampling stage, and specifically calculating as follows:
wherein k corresponds to the kth data in the data set, i.e. represents the kth cycle; pre represents a corresponding predicted value; k represents a Kalman gain; r is the noise variance; z is a system observation value, namely data in a historical data set;
obtaining the mean value of the sample by the above calculation
Sum covariance
And then updating the particle set by using the particle set, wherein the specific calculation is as follows:
s3-5, calculating the weight value of each particle
The specific calculation is as follows:
wherein p represents prior probability and q represents posterior probability;
s3-6, carrying out normalization processing on the weight values, and then calculating the weighted average value of the particle set as output, thereby carrying out state updating, namely updating the parameters in the life model and determining the prediction equation.
The specific method of step S4 is:
and setting an early warning threshold value, substituting the early warning threshold value into a prediction equation, and calculating to obtain the remaining service life of the IGBT device.
In the specific implementation process, when the service life of a certain IGBT device is predicted, a power cycle accelerated aging experiment needs to be performed on the IGBT device first to obtain the conduction voltage drop between a collector and an emitter of the IGBT device in the aging process as a data set of experimental measurement, as shown in fig. 2, then a part of data is selected as training data, and another part of data is selected as verification data, in this embodiment, 10 data points are predicted, so that the last 10 points of the experimental measurement data are used as verification data, and the rest of data are used as training data, and a prediction equation is obtained by training and calculating the method provided by the present invention, in the embodiment, the conduction voltage drop of the next cycle is only predicted each time, as shown in fig. 2, it can be seen from the experimental results that the prediction result of the method is closer to the real experimental measurement data, compared with the basic particle filtering algorithm, the method provided by the invention has better accuracy, so that the method can predict the residual service life more accurately, and the accuracy of the service life prediction is improved.
In conclusion, the invention provides an IGBT service life prediction method based on extended Kalman particle filtering. The method improves the particle filter algorithm, and improves the accuracy of the particle filter algorithm for service life prediction. Compared with the prior art, the method greatly improves the accuracy of prediction, is simple, has strong practicability, and improves the reliability of the device in practical application.
Claims (5)
1. The IGBT service life prediction method based on the extended Kalman particle filter is characterized by comprising the following steps:
s1, acquiring historical data of the IGBT device;
s2, preprocessing data and establishing a life model of the IGBT;
s3, establishing a prediction equation by using an extended Kalman particle filter algorithm;
and S4, predicting the residual service life of the IGBT.
2. The IGBT life prediction method based on extended Kalman particle filter according to claim 1, characterized in that the specific method of step S1 is:
and carrying out a power cycle accelerated aging experiment on the IGBT device, acquiring the conduction voltage drop between a collector and an emitter of the IGBT device in the aging process, and establishing a historical data set.
3. The IGBT life prediction method based on extended Kalman particle filter according to claim 1, characterized in that the specific steps of step S2 are:
s2-1, eliminating error points generated by measurement by using a Rhein criterion;
s2-2, taking the average value of the data in the single cycle as the characteristic value of the cycle;
s2-3, mapping the data samples into the same range according to a uniform standard through scale transformation;
s2-4, performing curve fitting on the processed data, establishing a primary life model, and determining the initial values of the model parameters.
4. The IGBT life prediction method based on extended Kalman particle filter according to claim 1, characterized in that the specific steps of step S3 are:
s3-1, establishing a state equation f and an observation equation h according to the life model;
s3-2, setting the number of particles as N, and initializing a particle set, namely extracting an initial state from the prior distribution P;
s3-3, calculating Jacobian matrix F of state transition matrix of each particle(i)Noise driving matrix G(i)And observe the noise drive matrix U(i)(ii) a Wherein i corresponds to the ith particle;
s3-4, updating the particle set by using an extended Kalman filter algorithm in the importance sampling stage, and specifically calculating as follows:
wherein k corresponds to the kth data in the data set, i.e. represents the kth cycle; pre represents a corresponding predicted value; k represents a Kalman gain; r is the noise variance; z is a system observation value, namely data in a historical data set;
obtaining the mean value of the sample by the above calculation
Sum covariance
And then updating the particle set by using the particle set, wherein the specific calculation is as follows:
s3-5, calculating the weight value of each particle
The specific calculation is as follows:
wherein p represents prior probability and q represents posterior probability;
s3-6, carrying out normalization processing on the weight values, and then calculating the weighted average value of the particle set as output, thereby carrying out state updating, namely updating the parameters in the life model and determining the prediction equation.
5. The IGBT life prediction method based on extended Kalman particle filter according to claim 1, characterized in that the specific method of step S4 is:
and setting an early warning threshold value, substituting the early warning threshold value into a prediction equation, and calculating to obtain the remaining service life of the IGBT device.
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| CN117764271A (en) * | 2023-11-24 | 2024-03-26 | 华南理工大学 | Power grid dynamic state estimation method and system based on extended Kalman particle filtering |
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| CN106845866A (en) * | 2017-02-27 | 2017-06-13 | 四川大学 | Equipment method for predicting residual useful life based on improved particle filter algorithm |
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