Inspiration

Appliances don’t fail suddenly—they degrade over time, but in most homes, that process is invisible. Failures only become obvious when it’s too late, leading to costly repairs and inconvenience.

We were inspired by industrial predictive maintenance and asked: Why doesn’t this exist for everyday homes?

What it does

Appliance Intelligence monitors appliance behavior and detects early signs of failure.

Instead of raw data, it translates signals into simple insights like:

“Your appliance is working harder than normal” “Possible airflow restriction”

The goal is to turn silent failures into early warnings.

How we built it

ESP32 (Freenove) for real-time processing ACS712 current sensor (5A) to measure electrical behavior Fan used as a demo appliance

We detect anomalies in current draw and convert them into human-readable alerts. A simulated fault (blocked airflow) demonstrates how stress is detected in real time.

Challenges we ran into

Differentiating real issues from normal fluctuations Reducing false positives Translating technical data into meaningful user insights Building a reliable hardware demo within time constraints

Accomplishments that we're proud of

Built a working real-time hardware prototype Successfully demonstrated live anomaly detection Made technical data understandable for non-technical users Created a non-invasive solution adaptable to any appliance

What we learned

Simplicity and clarity matter as much as technical accuracy Even basic signals like current can reveal useful patterns Bridging hardware data with user-friendly insights is key

What’s next for Appliance Intelligence

Add machine learning for personalized behavior modeling Integrate more sensors (vibration, temperature) Predict failure timelines Build a mobile app for user interaction Expand to property managers and manufacturers