Remaining Useful Life Prediction of MOSFETs via the Takagi–Sugeno Framework
Abstract
:1. Introduction
- Development of a Takagi–Sugeno framework for modeling MOSFET degradation modeling based on run-to-failure historical data. Thus, instead of a large amount of historical data, parameters of the historical models are stored.
- Development of an on-line estimation and inference scheme selecting a historical model that is used for RUL prediction purposes.
- Development of an on-line approach for estimating an analytical exponential model along with RUL prediction.
- Merging together Takagi–Sugeno data-driven and analytical approaches to form an interval of RUL instead of a point prediction.
- Experimental validation of the proposed approach based on the NASA Ames Prognostics Data Repository [23].
2. RUL Prediction of Power MOSFETs
2.1. MOSFETs
- Maximum drain to source voltage: a nominal parameter of MOSFETs; exceeding the limit voltage results in an uncontrolled mode conduction.
- Maximum drain current: in general, a drain current should not reach a border value. However, there are momentary increases in the current, which are called a pulse of drain current.
- Maximum temperature: specifies the maximum temperature of the junction, which does not affects changes of functionality.
- Gate oxide breakdown: a decrease in gate dielectric thickness causes an increased frequency of switching. A significant reduction of lifetime is noticeable when the nominal gate voltage is exceeded, which possibly results in a failure.
2.2. RUL Prediction Concept
2.3. Methods of Modeling MOSFET Degradation Process
3. Multiple Model-Based T-S Framework MOSFET Degradation Modeling
- Q1:
- How to provide on-line updating rules for (3) with the possibly less restrictive assumptions on the uncertainty terms? They should be assumed bounded but it is unrealistic to assume that they obey any particular distribution.
- Q2:
- How to introduce (3) within the RUL prediction scheme for a currently performing MOSFET?
- Q3:
- How to use historical data to support fault diagnostics and assessment of the state of the currently operating MOSFET?
3.1. T-S-Based Historical Data Approach
- Step 0:
- Assume the initial parameters: , for , moreover for , wherein .
- Step 1:
- Obtain a set of active sub-models:
- Step 2:
- Assume:
- Step 3:
- Obtain:
- Step 4:
- If , then calculate:
- Step 5:
- Set and go to Step 1.
- Step 0:
- Assume the initial parameters: , , , and .
- Step 1:
- Obtain:
- Step 2:
- If then calculate:
- Step 3:
- Set and go to Step 1.
- Perform Closest historical T-S model selection.
- Introduce the run-to-failure-based Historical T-S models repository for obtaining the RUL of a currently operating MOSFET.
- Use an exponential model (3) for obtaining an alternative RUL prediction.
- Perform an RUL fusion resulting in an RUL interval.
4. Validation of the RUL Prediction Framework
4.1. Power MOSFETs Data Processing
- Drain-to-source leakage current of −25 A;
- Maximum continuous drain voltage of −100 V;
- Continuous drain current of −9.7 A;
- Minimum gate threshold voltage of −2 V;
- Maximum gate threshold voltage of −4 V;
- Internal drain inductance of −4.5 nH;
- Internal source inductance of −7.5 nH.
4.2. Design of Historical Model Repository
4.3. RUL Estimation and Prediction
- Gaussian process regression;
- Extended Kalman filter;
- Particle filter.
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Abbreviations
ARMA | Autoregressive-Moving Average Model |
ANN | Artificial Neural Network |
EoL | End of Life |
FPT | First Prediction Time |
MOSFET | Metal-Oxide Semiconductor Field-Effect Transistor |
On-State Resistance | |
RLS | Recursive Least-Square |
RUL | Remaining Useful Life |
PHM | Prognostics and Health Management |
TTF | Time-To-Failure |
T-S | Takagi–Sugeno model |
QOBE | Quasi-Outer Bounding Ellipsoid algorithm |
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Witczak, M.; Mrugalski, M.; Lipiec, B. Remaining Useful Life Prediction of MOSFETs via the Takagi–Sugeno Framework. Energies 2021, 14, 2135. https://doi.org/10.3390/en14082135
Witczak M, Mrugalski M, Lipiec B. Remaining Useful Life Prediction of MOSFETs via the Takagi–Sugeno Framework. Energies. 2021; 14(8):2135. https://doi.org/10.3390/en14082135
Chicago/Turabian StyleWitczak, Marcin, Marcin Mrugalski, and Bogdan Lipiec. 2021. "Remaining Useful Life Prediction of MOSFETs via the Takagi–Sugeno Framework" Energies 14, no. 8: 2135. https://doi.org/10.3390/en14082135
APA StyleWitczak, M., Mrugalski, M., & Lipiec, B. (2021). Remaining Useful Life Prediction of MOSFETs via the Takagi–Sugeno Framework. Energies, 14(8), 2135. https://doi.org/10.3390/en14082135