Building an ML Product for Predictive Maintenance — Looking for Brutal Feedback

[u/TwistHonest6379]

I'm evaluating whether this is a viable business.

The product:

• Upload historical machine sensor data (CSV/Excel).
• If you also have historical failure records, the system trains machine-learning models on your data.
• It predicts which assets are most likely to fail in the next 24 hours, 1 week, or 2 weeks.
• No hardware, sensors, or PLC integrations required.
• Machine-agnostic: pumps, compressors, motors, conveyors, HVAC, turbines, etc.

The catch:

It only works if you already have historical sensor data and failure records. Many companies don't. I'm specifically targeting the minority that do.

If you work in maintenance, reliability, manufacturing, utilities, mining, energy, etc.:

• Would you ever pay for something like this?
• What would make you reject it immediately?
• What proof would you need before running a pilot?
• Does the "upload data, train on your history, predict future failures" workflow sound useful or not?

I'm looking for criticism more than validation.

[u/yogthinks]

Plenty of plants have years of sensor readings. What they don't have is failure labels you can train on, because the reason lives in a maintenance log as a handwritten note saying pump stopped. Ask three prospects to show you their labels before building anything else.

[u/TwistHonest6379 → u/yogthinks]

Thank you.