AI Predictive Maintenance: Compliance for Manufacturers

Overview

AI predictive maintenance may be high-risk if managing critical infrastructure or safety equipment. Document prediction accuracy, maintain human oversight, keep logs, and have fallback procedures.

How It Works

AI predictive maintenance uses sensors and ML to monitor equipment and predict maintenance needs. Instead of fixed schedules, you maintain equipment right before failure. This saves money, reduces downtime, and can improve safety.

However, when AI predicts maintenance for equipment that could cause harm if it fails, compliance implications are significant.

Risk Classification

AI systems managing critical infrastructure are high-risk under the EU AI Act. If your predictive maintenance monitors systems whose failure could endanger people, you're likely in high-risk territory. Assess your situation: what equipment does the AI monitor? What happens if the prediction is wrong?

Establish fallback procedures for when the AI itself fails. Don't become so dependent that you can't function without it.

Documentation

Keep detailed records of every AI recommendation, whether followed, outcomes, incidents where AI failed to predict problems, and cases where humans overrode AI. These records serve regulatory compliance, continuous improvement, and due diligence in investigations.

Track prediction accuracy over time — does it actually predict failures? Document both hits and misses.

This article is for informational purposes only and does not constitute legal advice. Regulatory requirements change frequently — verify current rules with official sources. Built by Sawai Gyoseishoshi Office, Hiroshima, Japan.