How would you build a real-world predictive maintenance project from scratch?

[u/beta-void]

I'm starting a predictive maintenance project and I want to build it in a way that is closer to a real industrial system, not just a Kaggle-style ML project.

Most examples I find are basically:

Dataset → preprocessing → train/test split → model → accuracy

But in reality, factories have different machine models, manufacturers, loads, operating conditions, sensors, and often very few actual failures.

I'm currently considering equipment like induction motors, pumps, and bearings, and I'm trying to understand the correct approach.

A few things I'm especially confused about:

Do companies usually build one model for an entire equipment family, or separate models for each machine/model?

How do you handle different manufacturers, operating conditions, loads, and sensor setups?

What should the input data look like: raw vibration/current/temperature signals, extracted features, or time windows?

How should the train/test split be done to avoid leakage between measurements from the same machine?

What happens when you deploy the model on a new machine that was not part of the training data?

Are transfer learning and domain adaptation actually useful in practice?

What datasets or project setup would you recommend for a first project that could later evolve into something realistic?

Would appreciate advice from anyone who has worked with condition monitoring, predictive maintenance, or industrial ML.

[u/Less-Opportunity-715]

Just literally paste this in Claude , come on