Is it impossible to predict defects with data that are not labeled?

There is manufacturing data with 10 process variables. Normal and bad labeling are not done. It's tabular fdata.

Do you have a paper that only uses data that are not labeled to predict defects or to find variables that affect them?

I thought about using the Outlier Detection Algorithm (Isolation Forest, Autoencoder) to predict defects, but I can't find a way because I don't know the exact defect rate.

I can't think of a way to verify it, so I'd like to find a literature for reference.

Is there a study that predicted defects with time series data that were not labeled?