That's an excellent question that gets right to the heart of what many maintenance teams are wondering about AI predictive maintenance for servo drives. From what I've researched, the real-world experience is actually quite promising, but with some important caveats.

The good news is that AI-powered predictive maintenance is definitely preventing failures in practice. Studies show systems achieving 91% anomaly detection accuracy for servo motors and 84% early fault detection rates. Companies like Ford have reduced machine failure rates by 25% and cut equipment downtime by 15% through these systems. The key benefit is turning raw equipment data into early warning signals so you can plan downtime instead of being ambushed by it.

However, your concern about false alarms is absolutely valid. Research shows that false alarms can actually erase the value of predictive maintenance efforts if not managed properly. A small fraction of incorrect predictions can drive up service costs instead of decreasing them. The challenge comes from two types of false positives: 'unnecessary work' (predicting issues that won't occur) and 'incorrect work' (predicting the wrong issue).

The complexity part is real too - successful implementation requires careful integration with maintenance workflows, tying alerts to work orders, spare parts planning, and shutdown windows. The systems need high-quality data and rigorous testing to maintain accuracy.

So in summary: Yes, it's preventing failures effectively when implemented well, but you need to manage false alarm rates carefully and be prepared for the integration complexity. The ROI comes from balancing accurate predictions with minimizing unnecessary interventions.