How do I choose a predictive maintenance provider?

Choose a provider with validated results (not just claims), experience in your industry, a clear methodology, and evidence of cross-equipment generalization. Ask for AUC scores, false alarm rates, and detection windows.

What to Ask

What AUC score does your system achieve on benchmark datasets?
What is your false alarm rate on cross-equipment transfer?
How many days before failure can you detect degradation?
What sensor types do you support?
Can you show validation results from independent datasets?
What is the deployment timeline?
What infrastructure do we need to provide?

Red Flags

No published validation results
Claims without evidence (e.g., "99% accurate" without methodology)
No cross-equipment testing
Requires expensive proprietary hardware
Cannot explain how the model works

VLTHRLAB's Approach

VLTHRLAB publishes all validation results: AUC up to 1.000 on 8 datasets, zero false alarms on cross-dataset transfer, 5-24 day detection window, 86% cost reduction. The methodology is documented and reproducible.

Evidence

VERIFIED

VLTHRLAB validated on 8 datasets
Source: vlthrlab.app/research/mad-system.html