That's a really insightful question that gets to the heart of what makes predictive maintenance both promising and challenging! Based on what I've seen across different industries, the reality is a mix of both scenarios - genuine success stories and frustrating false alarms.

On the positive side, I've come across some impressive cases where predictive maintenance has absolutely prevented catastrophic failures. For example, thermal imaging of electrical components has helped prevent potential fires in industrial settings, and in critical infrastructure like bridges and railways, continuous monitoring has caught structural issues that periodic inspections might have missed. Some organizations report reducing unexpected downtime by up to 50% and achieving 45-65% lifecycle cost reductions.

However, you're right to be skeptical about the data noise and false alarms. Research shows that false positives can actually erase the benefits of correct predictions if not managed properly. Maintenance teams often get frustrated chasing false alarms, and some studies indicate that employees worry these systems might undermine their expertise and job autonomy. The key challenge seems to be balancing accurate predictions with manageable alert volumes.

From what I gather, the most successful implementations combine good technology with experienced human judgment. When predictive systems work well, they're not replacing maintenance professionals but rather giving them better tools to make informed decisions. The systems that seem to deliver real value are those that have been properly calibrated to reduce false alarms (some methods claim 90% reduction) and that integrate smoothly with existing maintenance workflows.

What's your experience been like? Have you seen more success stories or more false alarms in your work?