How do I choose a predictive maintenance provider?

Q: 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.

Choose a provider with proven results (not just claims), experience in your industry, a clear method, and evidence it works across different equipment. 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 wear?
What sensor types do you support?
Can you show validation results from independent datasets?
How long does setup take?
What infrastructure do we need?

Red Flags

No published validation results
Claims without evidence (e.g., "99% accurate" without a clear method)
No cross-equipment testing
Requires expensive special hardware
Cannot explain how the system 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 method is documented and can be repeated.

Evidence

VERIFIED

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