Reliability is now a defining parameter for modern power electronic systems. As the world pushes harder toward electric mobility, renewable energy adoption, and high-efficiency digital infrastructure, key converters like EV chargers, solar inverters, and SMPS are running in incredibly demanding environments. High switching frequencies, aggressive power densities, wide bandgap materials (like SiC/GaN), and really stringent uptime expectations have all squeezed those reliability margins down to almost nothing. Clearly, traditional threshold-based alarms or basic periodic maintenance are no longer enough to guarantee stable operation.
This is exactly where AI-enabled failure prediction emerges as a breakthrough. By integrating real-time sensing, historical stress patterns, physics-based models, and deep learning, AI unlocks the ability to spot early degradation. This gives us the power to accurately estimate remaining useful life (RUL) and prevent catastrophic breakdowns long before they ever occur.
Jean‑Marc Chéry, CEO of STMicroelectronics, has emphasised that the practical value of AI in power electronics emerges at fleet and lifecycle scale rather than at individual-unit prediction level, particularly for SiC- and GaN-based SMPS.
Aggregated field data across large deployments is used to refine derating guidelines, validate device-level reliability models, and harden next-generation power technologies, instead of attempting deterministic failure prediction on a per-unit basis.
Limitations of Traditional Monitoring in Power Electronics
Conventional condition monitoring methods, things like simple temperature alarms, current protection limits, or basic event logs, operate reactively. They only catch failures after components have already drifted past the acceptable redline. Yet, converter failures actually start much earlier from subtle, long-term changes. Think about:
- Gradual ESR (Equivalent Series Resistance) increase in electrolytic capacitors
- Bond wire fatigue and solder joint cracking inside IGBT/MOSFET modules
- Gate oxide degradation in newer SiC devices
- Magnetic core saturation and insulation ageing
- Switching waveform distortions caused by gate driver drift
AI Techniques Powering Predictive Failure Intelligence
AI-based diagnostics in power electronics rest on three complementary pillars:
- Deep Learning for Real-Time Telemetry
AI-based diagnostics in power electronics rely on three complementary pillars:
Deep Learning for Real Time Telemetry Converters pump out rich telemetry data: temperatures, currents, switching waveforms, harmonics, soft switching behaviour, and acoustic profiles. Deep learning models find patterns here that are absolutely impossible for a human to spot manually.
- CNNs (Convolutional Neural Networks): These analyse switching waveforms, spot irregularities in turn-on/turn-off cycles, identify diode recovery anomalies, and classify abnormal transient events instantly.
- LSTMs (Long Short Term Memory Networks): These track the long-term drift in junction temperature, capacitor ESR, cooling efficiency, and load cycle behaviour over months.
- Autoencoders: learn the “healthy signature” of a converter and identify deviations that signal emerging faults.
- Physics-Informed ML
Pure machine learning struggles with operating points it has not seen; physics-informed machine learning offers better generalisation. It integrates:
- Power cycle fatigue equations
- MOSFET/IGBT thermal models
- Magnetics core loss equations
- Capacitor degradation curves
- SiC/GaN stress lifetime relationships
Peter Herweck, former CEO of Schneider Electric, has underscored that long-life power conversion systems cannot rely on data-driven models alone.
In solar and industrial inverters, Schneider Electric’s analytics explicitly anchor AI models to thermal behaviour, power-cycling limits, and component ageing physics, enabling explainable and stable Remaining Useful Life estimation across wide operating conditions.
- Digital Twins & Edge AI
Digital twins act as virtual replicas of converters, simulating electrical, thermal, and switching behaviour in real time. AI continuously updates the twin using field data, enabling:
- Dynamic stress tracking
- Load-cycle-based lifetime modelling
- Real-time deviation analysis
- Autonomous derating or protective responses
Edge-AI processors integrated into chargers, inverters, or SMPS enable on-device inference even without cloud connectivity.
AI-Driven Failure Prediction in EV Chargers
EV fast chargers (50 kW–350 kW+) operate under harsh conditions with high thermal and electrical stress. Uptime dictates consumer satisfaction, making predictive maintenance critical.
Key components under AI surveillance
- SiC/Si MOSFETs and diodes
- Gate drivers and isolation circuitry
- DC-link electrolytic and film capacitors
- Liquid/air-cooling systems
- EMI filters, contactors, and magnetic components
Roland Busch, CEO of Siemens, has emphasised that reliability in power-electronic infrastructure depends on predictive condition insight rather than reactive protection.
In high-power EV chargers and grid-connected converters, Siemens’ AI-assisted monitoring focuses on detecting long-term degradation trends—thermal cycling stress, semiconductor wear-out, and DC-link capacitor ageing—well before protection thresholds are reached.
AI-enabled predictive insights
- Waveform analytics: CNNs detect micro-oscillations in switching transitions, indicating gate driver degradation.
- Thermal drift modelling: LSTMs predict MOSFET junction temperature rise under high-power cycling.
- Cooling system performance: Autoencoders identify airflow degradation, pump wear, or radiator clogging.
- Power-module stress estimation: Digital twins estimate cumulative thermal fatigue and RUL.
Charging network operators report a 20–40% reduction in unexpected downtime by implementing AI-enabled diagnostics.
Solar & Industrial Inverters: Long-Life Systems Under Environmental Stress
Solar inverters operate for 10–20 years in harsh outdoor conditions—dust, high humidity, temperature cycling, and fluctuating PV generation.
Common failure patterns identified by AI
- Bond-wire lift-off in IGBT modules due to repetitive thermal stress
- Capacitor ESR drift affecting DC-link stability
- Transformer insulation degradation
- MPPT (Maximum Power Point Tracking) anomalies due to sensor faults
- Resonance shifts in LCL filters
AI-powered diagnostic improvements
- Digital twin comparisons highlight deviations in thermal behaviour or DC-link ripple.
- LSTM RUL estimation predicts when capacitors or IGBTs are nearing end-of-life.
- Anomaly detection identifies non-obvious behavior such as partial shading impacts or harmonic anomalies.
SMPS: High-Volume Applications Where Reliability Drives Cost Savings
SMPS units power everything from telecom towers to consumer electronics. With millions of units deployed, even a fractional improvement in reliability creates massive financial savings.
AI monitors key SMPS symptoms
- Switching frequency drift due to ageing components
- Hotspot formation on magnetics
- Acoustic signatures of transformer failures
- Leakage or gate-charge changes in GaN devices
- Capacitor health degradation trends
Manufacturers use aggregated fleet data to continuously refine design parameters, enhancing long-term reliability.
Cross-Industry Benefits of AI-Enabled Failure Prediction
Industries implementing AI-based diagnostics report:
- 30–50% reduction in catastrophic failures
- 25–35% longer equipment lifespan
- 20–30% decline in maintenance expenditure
- Higher uptime and service availability
Challenges and Research Directions
Even with significant progress, several challenges persist:
- Scarcity of real-world failure data: Failures occur infrequently; synthetic data and stress testing are used to enrich datasets.
- Model transferability limits: Variations in topology, gate drivers, and cooling systems hinder direct model reuse.
- Edge compute constraints: Deep models often require compression and pruning for deployment.
- Explainability requirements: Engineers need interpretable insights, not just anomaly flags.
Research in XAI, transfer learning, and physics-guided datasets is rapidly addressing these concerns.
The Future: Power Electronics Designed with Built-In Intelligence
In the coming decade, AI will not merely monitor power electronic systems—it will actively participate in their operation:
- AI-adaptive gate drivers adjusting switching profiles in real time
- Autonomous derating strategies extending lifespan during high-stress events
- Self-healing converters recalibrating to minimise thermal hotspots
- Cloud-connected fleet dashboards providing RUL estimates for entire EV charging or inverter networks
- WBG-specific failure prediction models tailored for SiC/GaN devices
Conclusion
AI-enabled failure prediction is completely transforming the reliability of EV chargers, solar inverters, and SMPS systems. Engineers are now integrating sensor intelligence, deep learning, physics-based models, and digital twin technology. This allows them to spot early degradation, accurately forecast future failures, and effectively stretch the lifespan of the equipment.
This whole predictive ecosystem doesn’t just cut your operational cost; it significantly boosts system safety, availability, and overall performance. As electrification accelerates, AI-driven reliability will become the core foundation of next-generation power electronic design. It makes systems smarter, more resilient, and truly future-ready.
