Let's define an "insight" as "new meaningful knowledge", just for fun. We could talk about what comprises "new" and "meaningful" but it would be beside the point I'm making.
In a supervised learning problem, the range of possible outputs is already known, meaning the model output will never be categorically different from what was in the training data. The knowledge obtained is meaningful as long as the training labels are meaningful, but it can never be new.
Unsupervised learning doesn't have a notion of "training data" but that means an unsupervised model's output requires additional interpretation in order to be meaningful. It is possible to uncover new structures and identify anomalies in new ways, but this knowledge isn't meaningful until someone comes in and interprets it.
Applied to the specific example where sensor data is used to try to generate insights about machine functionality: Either you can only predict the types of failures you've already seen, or you can identify states you've never seen but you wouldn't know whether they mean the system is likely to fail soon or not.
It's the Roth/401(k) tradeoff. For model output to be useful, someone must pay an interpretation tax. The only choice is whether it is paid upon insight deposit or withdrawal.