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International Journal for Research Trends and Innovation
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ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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Issue: July 2026
Volume 11 | Issue 7
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| Paper Title: | Sensorless Electro-Thermal Health Monitoring Of SiC MOSFETs Using Physics-Informed Neural Networks |
| Authors Name: | Aditya Shrishail Tirki |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJRTI_213358
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| Published Paper Id: | IJRTI2606133 |
| Published In: | Volume 11 Issue 6, June-2026 |
| DOI: | |
| Abstract: | Silicon Carbide (SiC) MOSFETs have become foundational to high-density power converters, yet their long-term reliability is limited by gate-oxide degradation and thermo-mechanical fatigue. Accurate, real-time estimation of the junction temperature (Tj) is essential for condition monitoring; however, direct in situ measurement is practically unfeasible. While purely data-driven machine learning approaches have been proposed for sensorless Tj estimation, they inherently lack domain knowledge, leading to poor generalization under dynamic load profiles and a high rate of false positive fault detections. This paper proposes a novel sensorless electro-thermal monitoring framework utilizing Physics-Informed Neural Networks (PINNs). By embedding the governing electro-thermal dynamic equations of the SiC device—specifically the transient thermal impedance network and coupled power loss models—directly into the neural network's loss function, the model is constrained by physical laws rather than relying on training data alone. The proposed PINN utilizes only standard, non-intrusive operational variables, including phase current, DC-link voltage, and coolant temperature, to accurately predict Tj. Validation results demonstrate that the physics-guided framework successfully decouples normal load-induced thermal transients from the progressive thermal shifts indicative of physical degradation. This approach not only provides high-fidelity, real-time Tj estimation but does so while actively preventing the false alarms prevalent in traditional black-box health monitoring systems, offering a highly reliable, sensorless predictive maintenance solution for next-generation power electronics. |
| Keywords: | SiC MOSFET, Physics-Informed Neural Networks (PINN), Junction Temperature, Condition Monitoring, Electro-Thermal Modeling, Predictive Maintenance |
| Cite Article: | "Sensorless Electro-Thermal Health Monitoring Of SiC MOSFETs Using Physics-Informed Neural Networks", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2456-3315, Vol.11, Issue 6, page no.b215-b218, June-2026, Available :http://www.ijrti.org/papers/IJRTI2606133.pdf |
| Downloads: | 00083 |
| ISSN: |
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID: IJRTI2606133
Registration ID:213358
Published In: Volume 11 Issue 6, June-2026
DOI (Digital Object Identifier):
Page No: b215-b218 Country: Chennai, Tamil Nadu, India Research Area: Engineering Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2606133 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2606133 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
Journal Starting Year (ESTD) : 2016
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