The Old Way Sucks

Industrial maintenance has basically been a coin flip for decades. You either wait for something to break (and lose money while it's down), or you service everything on a schedule (and waste money fixing things that were fine). Neither is great.

Predictive maintenance flips the script. Instead of reacting or guessing, you use sensor data and ML models to figure out what's actually about to go wrong. It's like having a mechanic who can hear the engine knock before anyone else.

How This Actually Works

The pipeline isn't magic. It's data, math, and good engineering:

1. Collect Data

IoT sensors monitor things like vibration, temperature, pressure, and current draw. Nothing fancy here, just reliable data flowing into a central system.

2. Feature Engineering

Raw sensor readings are noise. You transform them into useful signals: rolling averages, frequency analysis (FFT), statistical measures, trend lines. This is where the craft is.

3. Anomaly Detection

Models like Isolation Forests, autoencoders, or LSTMs learn what "normal" looks like for each machine. When something drifts outside that baseline, you get a flag. Not a false alarm. A real, early warning.

4. Remaining Useful Life (RUL) Estimation

The advanced stuff: models that don't just say "something's off" but estimate when a component will actually fail. This lets you plan maintenance windows precisely.

5. Making It Useful

All of this feeds into dashboards and alerts that maintenance teams can act on. Which machine, what's likely failing, how urgent, what to do. Clear answers, not data dumps.

The Numbers

Organizations doing this well typically see:

Those aren't hypotheticals. That's published data from early adopters.

The Tech Stack

A solid predictive maintenance setup usually looks something like:

Where to Start

If you're thinking about this for your operation, start small:

We're building Mantis to package this whole pipeline into a product. If you're curious, drop us a line. We like talking about this stuff.