Question 1

What is the Coffin-Manson model?

Accepted Answer

The Coffin-Manson model predicts the number of thermal cycles a material can sustain before fatigue failure: Nf = A · (ΔTj)^(-n) · exp(Ea / (k·Tjm)), where ΔTj is the junction-temperature swing, Tjm is the mean junction temperature, Ea is the activation energy and k is Boltzmann's constant. InverterAI uses it to predict IGBT module fatigue from SCADA-derived temperature cycling.

Question 2

What is the Arrhenius model used for in inverter reliability?

Accepted Answer

The Arrhenius model relates chemical reaction rate to temperature: lifetime halves for roughly every 10°C rise above the rated temperature. In PV inverters it predicts electrolytic DC-link capacitor aging — the dominant failure mode of central inverter capacitor banks — from measured operating temperature.

Question 3

What is a Physics-Informed Neural Network (PINN)?

Accepted Answer

A Physics-Informed Neural Network is a neural network whose loss function includes a physics residual term, so the network is penalised when its predictions violate the governing differential equations of the system. PINNs combine the flexibility of deep learning with the consistency guarantees of first-principles physics, which is essential for extrapolating beyond the training data — the typical situation in reliability prediction.

Question 4

What SCADA data does InverterAI need?

Accepted Answer

Standard inverter-level SCADA tags at 1-15 minute granularity: DC-link voltage, IGBT junction temperature (or heatsink temperature with a calibrated model), inverter ambient temperature, AC active power, AC current per phase, switching frequency, and inverter status code. Historical depth of 12+ months gives the best initial calibration; the platform keeps improving as new data arrives.

Question 5

How accurate are the RUL predictions?

Accepted Answer

Accuracy depends on data quality and operating regime, but on calibrated pilots InverterAI delivers RUL estimates with a 90% confidence interval typically within ±15% of the observed failure window for IGBT modules and ±20% for DC-link capacitors. Every prediction ships with its uncertainty band so O&M decisions are risk-aware.

Question 6

Is InverterAI explainable?

Accepted Answer

Yes. Every RUL prediction is accompanied by component-level and feature-level attributions (SHAP, Integrated Gradients) showing which physical signals — thermal cycling intensity, mean operating temperature, partial-load hours, switching stress — drive the estimate. This makes the platform defensible in front of OEMs, lenders and insurers.

The Science Behind InverterAI

Key Technologies

Physics-Informed Neural Networks

Virtual Tj Sensing

Uncertainty Quantification

Explainable AI

Technical Specifications

Read the physics

How thermal cycling kills IGBTs

Why DC-link capacitors age first

What a physics-informed network actually does

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