CN114329876A - Method for predicting residual service life of IGBT - Google Patents
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Abstract
The invention provides a method for predicting the remaining service life of an IGBT, which comprises the following steps: step 1: construction of fusion type aging evaluation index V' (V)CE(on),Tj). Step 2: v obtained according to accelerated aging testCE(on)And TjThe aging curve extracts a fusion type aging evaluation index V' (V)CE(on),Tj) Aging curve of (1), and for V' (V)CE(on),Tj) And (4) preprocessing the data. And step 3: establishing a two-stage regression model for the preprocessed data to complete parameter estimation, and establishing a model of V' (V)CE(on),Tj) An observation equation and a state transition equation of particle filtering as characteristic quantities. And 4, step 4: and generating particle subsets in a state space by using a particle filtering algorithm and predicting a fusion type aging evaluation index value of the next period. And 5: predicting remaining useful life of IGBT moduleIts life is long. The invention takes the defects of actual working conditions, individual difference of IGBT modules and the like into consideration, and constructs the fusion VCE(on)And TjThe novel aging evaluation index solves the problem that the existing IGBT service life prediction method aims at the state monitoring of a single parameter, and improves the service life prediction precision.
Description
Technical Field
The invention belongs to the technical field of power electronic reliability, and particularly relates to a method for predicting the residual service life of an IGBT (insulated gate bipolar translator) based on fusion type aging characteristic parameter particle filtering.
Background
With the rise of high-power electronic equipment, the IGBT is used as the most common power semiconductor device and is widely used in power systems, high-speed railways, automobiles and aviation. The complex operating environment in the field makes the IGBT switch frequently under the conditions of overheating, overvoltage and overpower for a long time, which accelerates the failure process of the IGBT. Thus, IGBTs are among the weakest and most critical links in energy conversion systems. For the scenes with high reliability requirements such as electric automobiles, offshore wind turbines and convertor stations, system faults caused by IGBT failure can bring loss which is difficult to measure.
Two main packaging failures of the IGBT module, namely bonding lead falling and solder layer fatigue, are caused by the difference of thermal expansion coefficients of all physical layer materials packaged by the IGBT module, and the solder layer and the bonding lead which play a role in fixing and connecting bear thermal mechanical stress caused by power fluctuation and temperature fluctuation for a long time, so that cracks are gradually generated, and finally, the fatigue failure is caused.
There are three main methods currently under investigation for Residual Useful Life (RUL) estimation: physical model analysis methods, analytical model analysis, and data driven methods.
The first method comprises the following steps: physical model analysis method. The physical model requires time-consuming testing or finite element analysis to obtain the stress characteristics of various materials, and is not universal due to the limitations of the materials and the manufacturing process. And because the IGBT structure is precise and complex, the degradation process is more difficult to detect than most mechanical systems, and the establishment of a physical model is more complex.
And the second method comprises the following steps: analyzing the model. The main parameter of the analytical model analysis method is junction temperature, and the device is predicted according to parameters such as the variation range and the mean value of the junction temperature, but the accuracy of the method greatly fluctuates when the junction temperature is extracted. The method for directly measuring the junction temperature based on the equipment needs special equipment, has high measurement cost, needs to open the shell of the IGBT module, and is harmful to the IGBT when the junction temperature is measured. Another method to obtain junction temperature is to estimate junction temperature by modeling thermal sensitive electrical parameters. However, the junction temperature calculation method and the calculation process are complex, errors of different degrees are generated, and the residual life prediction accuracy of the IGBT module is adversely affected.
And the third is that: a data driving method. The data driving method does not need to know failure mechanisms inside the IGBT, and meanwhile, health condition information of the module can be extracted from historical data. With the development of accelerated aging tests, a large amount of historical data for constructing an IGBT aging model can be obtained, and a data driving method such as a neural network becomes an important means for evaluating and predicting the service life of the IGBT.
Most of the existing methods for predicting the RUL of the IGBT based on data driving only select one characteristic parameter as a state monitoring parameter, and neglect the influence of junction temperature on the state monitoring parameter. Junction temperature affects most characteristic parameters, so that the IGBT failure determined by a single parameter is often inconsistent with the actual situation.
Disclosure of Invention
The technical problem to be solved is as follows: the existing IGBT service life prediction method neglects the influence of junction temperature aiming at the state monitoring of a single parameter, and does not consider the defects of actual working conditions, individual difference of IGBT modules and the like. The invention provides a method for predicting the remaining service life of an IGBT (insulated gate bipolar transistor), which is used for solving the problem of predicting the remaining service life of a single IGBT module in practical application based on particle filtering of fusion state monitoring parameters.
In order to achieve the purpose, the invention adopts the technical scheme that: a method for predicting the residual service life of an IGBT comprises the following steps:
step 1: construction of comprehensive consideration junction temperature TjAnd saturated on-state voltage drop VCE(on)The fusion type aging evaluation index V' (V)CE(on),Tj) Determining a failure criterion;
step 2: v obtained according to accelerated aging testCE(on)And TjThe aging curve extracts a fusion type aging evaluation index V' (V)CE(on),Tj) Aging curve of (1), and for V' (V)CE(on),Tj) Preprocessing data, wherein the preprocessing comprises abnormal data elimination and data compression;
and step 3: the preprocessed V' (V) is processedCE(on),Tj) Establishing two-stage regression model for data, completing parameter estimation, and establishing V' (V)CE(on),Tj) An observation equation and a state transition equation of particle filtering as characteristic quantities;
and 4, step 4: generating particle subsets in a state space using a particle filter algorithm and predicting a fusion-type aging evaluation index value V' (V) at a next cycle, i.e., at a time k +1CE(on),Tj)k+1；
And 5: predicting the residual service life of the IGBT module;
in a preferred embodiment, the fusion-type aging evaluation index V' (V) in step 1 isCE(on),Tj) The formula of (1) is:
V'(VCE(on),Tj)＝VCE(on)-q1×(Tj-T0) (3)
when T isj＝T0When, V' (V)CE(on),T0)＝V’0；
Wherein, T0Is the initial junction temperature, V'0An initial value of a fusion type aging evaluation index, q1To describe VCE(on)And TjA constant coefficient of linear relationship;
when V' (V)CE(on),Tj) To an initial value of V'0And when the preset multiple threshold value is reached, the IGBT power module is considered to be invalid.
In a preferred scheme, the value of the preset multiple threshold is 1.05-1.2.
In a preferred embodiment, the removing the abnormal data includes: and eliminating dead pixels generated by errors in experimental design through a Rhein criterion, and compressing the data after the dead pixels are eliminated.
In a preferred embodiment, the data compression includes: and the data compression is realized by taking the average value of the monitoring parameter data of a certain number of cycles as the characteristic value of the data.
In a preferred embodiment, the two-stage regression model in step 3 is:
the first M% aging data was fitted with a linear function, the formula:
V'(VCE(on),Tj)＝p1·n+p2 (5)
wherein p is1、p2All are aging model parameters of the first stage, n is a variable for representing the number of thermal cycles that the IGBT undergoes in the aging process, and M is within the range of 50,70]；
The last (100-M)% aging data was fitted with a bi-exponential regression function, with the formula:
V'(VCE(on),Tj)＝a·exp(b·n)+c·exp(d·n) (6)
and a, b, c and d are all the fitted aging model parameters of the second stage.
In a preferred embodiment, the observation equation is
Zk＝V'(VCE(on),Tj),k+wk (7)
Wherein Z iskAs observation data of the system at time k, wkMeasurement noise at time k, V' (V)CE(on),Tj),kIs V' (V) at time kCE(on),Tj) A value;
the state transition equation is:
V'(VCE(on),Tj),k＝V'(VCE(on),Tj),k-1·exp(b)+c·exp(d·k)(1-exp(b-d))+vk-1 (8)
wherein V' (V)CE(on),Tj)k-1V' (V) at time k-1CE(on),Tj) Value vk-1Is the process noise at time k-1.
In a preferred embodiment, the step 5 comprises: repeating the step 4 to enable the predicted value V' (V) of the target IGBT sampleCE(on),Tj)k+1And continuously approaching the failure threshold value until the residual service life of the target IGBT sample is determined, and giving the posterior probability distribution of the prediction result.
Compared with the prior art, the invention has the advantages that:
1. the invention reduces the junction temperature TjCorrection of saturated on-state voltage drop V incorporating aging characteristic parametersCE(on)Obtaining a new aging evaluation index V' (V)CE(on),Tj). The IGBT aging model is constructed according to the fusion type aging evaluation index obtained through actually measured data according to the actual working conditions of the IGBT, and the method has important significance for stable operation of the IGBT power module and even the whole system.
2. The method considers the influence of individual difference and actual working condition caused by the specific production process of the IGBT. Can establish V' (V) according to a power cycle accelerated aging testCE(on),Tj) And the residual service life of a single IGBT module can be accurately predicted by the mathematical model of the power cycle times.
Drawings
FIG. 1: the invention relates to a flow chart for predicting the residual service life of an IGBT module;
FIG. 2: v corresponding to different junction temperatures under large current conditionCE(ON)Saber simulation circuit diagram of (1);
FIG. 3: i isCA constant, TjAt time of change VCE(on)Saber simulation waveform diagram.
FIG. 4: the invention relates to a computer program flow chart for predicting the residual service life of an IGBT module.
Detailed Description
The method for predicting the remaining service life of the IGBT based on the fusion parameter monitoring particle filter according to the present invention is described in detail below with reference to the accompanying drawings, but it should be noted that the embodiments and examples of the present invention are preferred solutions for illustrative purposes and are not intended to limit the scope of the present invention.
Referring to fig. 1, a flow chart of the method for predicting the remaining service life of the IGBT based on the fused aging characteristic parameter particle filter according to the present invention will be described in detail.
Step 1: construction of comprehensive consideration junction temperature TjAnd saturated on-state voltage drop VCE(on)The fusion type aging evaluation index V' (V)CE(on),Tj) And determining a failure criterion, and determining the failure criterion.
Analyzing IGBT module VCE(on)And TjCorrelation and verification in saber simulation software. The saturation conduction voltage drop of the IGBT can be expressed as shown in formula (1)
VCE(on)(Tj,IC)＝f(Tj,IC)＝[V0-a1(Tj-Tj0)]+[R0+b1(Tj-Tj0)]×IC (1)
In the formula, VCE(on)、TjAnd ICThe saturation voltage drop, junction temperature and forward conduction current of the IGBT power module, respectively. V0And R0Respectively, reference junction temperature Tj0The saturation voltage drop and the equivalent resistance of the lower IGBT power module are shown, a1 represents the negative temperature coefficient of the saturation voltage drop, and b1 represents the positive temperature coefficient of the equivalent resistance. When I isCEqual to the large current threshold IC(h)When the second term of formula (1)[R0+b(Tj-Tj0)]×ICTo VCE(on)The effect of size is dominant, when VCE(on)And TjAnd (4) positively correlating. V under the condition of high current is researched through saber simulation softwareCE(on)、TjFunctional relationship between them.
As shown in the simulation circuit of fig. 2, an IGBT module based on a Hefner physical model carried by Saber software is adopted, a junction temperature input interface of the module is connected with a heat source, the junction temperature is controlled by the value of the heat source, and a direct-current power supply is adjusted to enable the current I to be ICThe current (large current) is equal to the current under the accelerated aging test condition, and the on-off of the IGBT is controlled by a pulse power supply. The junction temperature is simulated to increase from 120 ℃ to 200 ℃ (the increment amplitude is 10 ℃) by adopting a parameter scanning tool in Saber, and the corresponding V is obtained by simulationCE(on). It can be seen from FIG. 3 that with TjIncreasing by a factor VCE(on)The change law of (2) is approximately linear. Record data and make VCE(on)And TjTwo-dimensional relationship scattergram (V)CE(on),Tj) Is approximately a straight line, and shows the saturated on-state pressure drop VCE(on)There is a good linear relationship with junction temperature at high current.
From the above, it can be seen thatC＝IC(h)The healthy baseline curve of the IGBT is shown below:
VCE(on)＝f(Tj)＝q1*Tj+q2 (2)
wherein q is1、q2To describe VCE(on)And TjConstant coefficients of linear relationship.
To exclude junction temperature vs. V during accelerated agingCE(on)According to VCE(on)And TjWith junction temperature as a correction factor and VCE(on)Normalized to the same value of temperature T0Then, the fusion type aging evaluation index V' (V) was obtained by normalizationCE(on),Tj) The formula is as follows:
V'(VCE(on),Tj)＝VCE(on)-q1×(Tj-T0) (3)
when T isj＝T0When, V' (V)CE(on),T0)＝V’0(ii) a Wherein, T0Is the initial junction temperature, V'0An initial value of a fusion type aging evaluation index, q1To describe VCE(on)And TjConstant coefficients of linear relationship.
When V' (V)CE(on),Tj) To an initial value of V'0The time of 1.05-1.2 times of the time is considered to be that the IGBT power module fails. When in this example V'0When 1.05 is selected, the critical line of failure is
V'(VCE(on),Tj)＝1.05V0′ (4)
Step 2, obtaining V according to accelerated aging testCE(on)And TjThe aging curve extracts a fusion type aging evaluation index V' (V)CE(on),Tj) Aging curve of (1), and for V' (V)CE(on),Tj) And preprocessing the data, including removing abnormal data and compressing the data.
Obtaining V according to actual measurement parameters in accelerated aging testCE(on)And TjThe fusion type aging evaluation index V' (V) is extracted from the aging curve of (1) by the formula (3)CE(on),Tj) Aging curve of (2). And eliminating dead pixels generated by errors in experimental design through a Rhein criterion, and compressing the data after the dead pixels are eliminated. Data compression is realized mainly by taking the average value of monitoring parameter data of a certain number of cycles as a characteristic value of the data compression, and the data compression is used for life prediction.
Step 3, according to the preprocessed V' (V)CE(on),Tj) Establishing two-stage regression model for data, completing parameter estimation, and establishing V' (V)CE(on),Tj) An observation equation and a state transition equation of particle filtering as characteristic quantities.
The trend of an aging model is explored through regression analysis, and the aging data of about the first M% (M epsilon [50,70]) is found to have better fitting effect by using a linear function
V'(VCE(on),Tj)＝p1·n+p2 (5)
Wherein p is1、p2The parameters are fitted aging model parameters of the first stage, and n is a variable representing the number of thermal cycles that the IGBT undergoes in the aging process.
The fitting effect of the double exponential degradation function for about the last (100-M)% of aging data is better
V'(VCE(on),Tj)＝a·exp(b·n)+c·exp(d·n) (6)
And a, b, c and d are all the fitted aging model parameters of the second stage.
Respectively establishing observation equations
Zk＝V'(VCE(on),Tj),k+wk (7)
Wherein Z iskAs observation data of the system at time k, wkMeasurement noise at time k, V' (V)CE(on),Tj),kIs V' (V) at time kCE(on),Tj) A value;
reestablishing the State transition equation
V'(VCE(on),Tj),k＝V'(VCE(on),Tj),k-1·exp(b)+c·exp(d·k)(1-exp(b-d))+vk-1 (8)
Wherein V' (V)CE(on),Tj)k-1V' (V) at time k-1CE(on),Tj) Value vk-1Is the process noise at time k-1.
Step 4, generating particle sets in a state space by using a particle filter algorithm and predicting a fusion type aging evaluation index value V' (V) at the next period, namely k +1 momentCE(on),Tj)k+1。
Importance sampling of particles generates particle subsets and weights are calculated. Particles with small weight and little contribution to state estimation are removed through resampling, and the next period V '(V') of the target IGBT sample is completedCE(on),Tj)k+1The mean value of (1) is estimated.
Repeating the steps4, making the predicted value V' (V) of the target IGBT sampleCE(on),Tj)k+1And continuously approaching the failure threshold value until the residual service life of the target IGBT sample is determined, and giving the posterior probability distribution of the prediction result. Fig. 4 shows a flowchart of a computer program for predicting the remaining service life of the IGBT module according to the present invention.
The foregoing is only a preferred embodiment of the present invention. It should be understood that the above description of the preferred embodiments is given for clarity and not for any purpose of limitation, and that various changes, substitutions and alterations can be made herein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (8)
1. The method for predicting the residual service life of the IGBT is characterized by comprising the following steps:
step 1: construction of comprehensive consideration junction temperature TjAnd saturated on-state voltage drop VCE(on)The fusion type aging evaluation index V' (V)CE(on),Tj) Determining a failure criterion;
step 2: v obtained according to accelerated aging testCE(on)And TjThe aging curve extracts a fusion type aging evaluation index V' (V)CE(on),Tj) Aging curve of (1), and for V' (V)CE(on),Tj) Preprocessing data, wherein the preprocessing comprises abnormal data elimination and data compression;
and step 3: the preprocessed V' (V) is processedCE(on),Tj) Establishing two-stage regression model for data, completing parameter estimation, and establishing V' (V)CE(on),Tj) An observation equation and a state transition equation of particle filtering as characteristic quantities;
and 4, step 4: generating particle subsets in a state space using a particle filter algorithm and predicting a fusion-type aging evaluation index value V' (V) at a next cycle, i.e., at a time k +1CE(on),Tj)k+1；
And 5: and predicting the residual service life of the IGBT module.
2. The method for predicting the remaining service life of the IGBT according to claim 1, wherein the fused aging evaluation index V' (V) in the step 1CE(on),Tj) The formula of (1) is:
V'(VCE(on),Tj)＝VCE(on)-q1×(Tj-T0) (3)
when T isj＝T0When, V' (V)CE(on),T0)＝V’0；
Wherein, T0Is the initial junction temperature, V'0An initial value of a fusion type aging evaluation index, q1To describe VCE(on)And TjA constant coefficient of linear relationship;
when V' (V)CE(on),Tj) To an initial value of V'0And when the preset multiple threshold value is reached, the IGBT power module is considered to be invalid.
3. The method for predicting the remaining service life of the IGBT according to claim 2, wherein the preset multiple threshold value is 1.05-1.2.
4. The method for predicting the remaining service life of the IGBT according to claim 1, wherein the removing abnormal data comprises: and eliminating dead pixels generated by errors in experimental design through a Rhein criterion, and compressing the data after the dead pixels are eliminated.
5. The method for predicting the remaining service life of the IGBT according to claim 1, wherein the data compression comprises: and the data compression is realized by taking the average value of the monitoring parameter data of a certain number of cycles as the characteristic value of the data.
6. The method for predicting the remaining service life of the IGBT according to claim 1, wherein the two-stage regression model in step 3 is:
the first M% aging data was fitted with a linear function, the formula:
V'(VCE(on),Tj)＝p1·n+p2 (5)
wherein p is1、p2For the fitted aging model parameters of the first stage, n is a variable characterizing the number of thermal cycles that the IGBT undergoes during aging, and M is an element [50,70]]；
The last (100-M)% aging data was fitted with a bi-exponential regression function, with the formula:
V'(VCE(on),Tj)＝a·exp(b·n)+c·exp(d·n) (6)
and a, b, c and d are all the fitted aging model parameters of the second stage.
7. The method for predicting the remaining service life of the IGBT as claimed in claim 6, wherein the observation equation is
Zk＝V'(VCE(on),Tj),k+wk (7)
Wherein Z iskAs observation data of the system at time k, wkMeasurement noise at time k, V' (V)CE(on),Tj),kIs V' (V) at time kCE(on),Tj) A value;
the state transition equation is:
V'(VCE(on),Tj),k＝V'(VCE(on),Tj),k-1·exp(b)+c·exp(d·k)(1-exp(b-d))+vk-1 (8)
wherein V' (V)CE(on),Tj)k-1V' (V) at time k-1CE(on),Tj) Value vk-1Is the process noise at time k-1.
8. The method for predicting the remaining service life of the IGBT according to claim 1, wherein the step 5 comprises the following steps: repeating the step 4 to enable the predicted value V' (V) of the target IGBT sampleCE(on),Tj)k+1Force continuouslyAnd (4) approaching the failure threshold value until the residual service life of the target IGBT sample is determined, and giving the posterior probability distribution of the prediction result.
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