PulseTrust_

Listening for the Ghosts Inside Industrial Machines

This is a submission for the "Hacktoberfest Open-Source AI Challenge Week 4: Ghost in the Machine" (https://dev.to/challenges/hacktoberfest-week4-2026-10-26).

What I Built

Machines don't always fail with a loud bang. Sometimes, the first sign of a problem is hidden in a stream of sensor readings — a small deviation, an unusual pattern, or something that looks completely normal at first.

That's the idea behind PulseTrust_, an industrial sensor-trust and DC motor monitoring prototype.

The project uses an ESP32 to collect sensor telemetry from a DC motor and an anomaly-detection model to identify readings that deviate from expected behaviour.

I interpreted Ghost in the Machine as something other than a supernatural presence. The ghost is the invisible fault hiding inside a machine — something that may go unnoticed until it becomes a serious problem.

PulseTrust_ is an attempt to make those hidden signals visible.

The objective is simple: monitor the machine, understand its behaviour, and flag unusual patterns before they go unnoticed.

Demo

[Insert your deployed demo link here]

[Insert a video demonstration here]

A short demonstration showing the ESP32 telemetry flow and anomaly detection would be especially useful here.

Code

[Insert your GitHub repository link here]

The repository contains the implementation and project setup.

How I Built It

PulseTrust_ brings together embedded hardware, backend development, and machine learning.

The main components are:

• ESP32: Collects telemetry from the DC motor and acts as the hardware interface.
• FastAPI: Handles backend communication and the application logic.
• Isolation Forest: An anomaly-detection algorithm used to identify unusual patterns in sensor telemetry.
• Supabase: Provides the backend data layer for storing and managing telemetry.

The overall flow is:

"DC Motor → ESP32 → FastAPI → Anomaly Detection → Supabase"

The machine-learning component is an Isolation Forest model. Rather than depending entirely on labelled examples of every possible motor failure, it can identify observations that differ from the patterns it has learned.

This is important because industrial environments can produce many different abnormal behaviours, and collecting labelled examples of every failure scenario is not always practical.

PulseTrust_ is a prototype, not a certified industrial safety or predictive-maintenance system. Its current purpose is to demonstrate how sensor telemetry and anomaly detection can work together to identify unusual machine behaviour.

Why Does Open Innovation Matter?

For a project like PulseTrust_, I didn't want the intelligence behind the system to be a black box.

Using an open-source machine-learning approach gives developers more control over how the model works, how it is configured, and how it can be improved.

It also makes experimentation easier. The anomaly-detection approach can be inspected, tested against different sensor data, and replaced with another model as the project evolves.

The combination of open technologies also makes the system more accessible to developers who want to experiment with industrial monitoring without depending entirely on expensive, proprietary AI services.

Open innovation matters because it gives us the freedom to understand the technology we build with — not just consume it.

The Spirit of It

There is something interesting about a machine that can show signs of trouble without making a sound.

A motor might still be running. Everything might appear normal from the outside. But somewhere inside its telemetry, a pattern could be changing.

That is the ghost PulseTrust_ is trying to find.

Not every ghost needs to haunt a house. Some hide inside machines, waiting for someone to notice.

PulseTrust_ is built to listen.