Home▸AI & Machine Learning▸Detecting Faults in EV Charging Stations Before They Happen

🔌 Detecting Faults in EV Charging Stations Before They Happen

An interactive 3D lab showing an EV charging station forecourt and a live isolation-forest feature space, where you can inject sensor drift and watch an unsupervised model flag failing chargers before they go offline.

AI & Machine Learning3DAdvanced60 FPS

A live EV charger forecourt paired with a 3D isolation-forest feature space: watch simulated sensor drift push a station's telemetry point away from the healthy cluster until an unsupervised model flags it before it actually fails.

🔬 What It Demonstrates

Each charger's temperature, cycle-time jitter and power-draw readings become coordinates in a 3D feature space. An isolation-style score measures how far a point sits from the dense, normal core — the same idea Isolation Forest and autoencoder reconstruction error use to flag hardware degradation early.

🎮 How to Use

Raise the fault injection rate to make stations start drifting, then tune detector sensitivity to see the decision-boundary shell tighten or loosen. Add sensor noise to see how it complicates detection, and toggle the autoencoder overlay to see reconstruction-error vectors.

💡 Did You Know?

Because catastrophic charger failures are rare and expensive to label, real fleet-monitoring systems lean on unsupervised methods like Isolation Forest and autoencoders that need no failure examples at all — only a model of what "normal" looks like.

⚙ Under the hood

machine learningdata analysisanomaly detectionsensor datafault diagnosisThree.js

3D · Three.js / WebGL renderer · 60 FPS target · runs fully client-side, no install