Something I hear constantly from reliability and maintenance teams: they have a predictive maintenance program, alerts are coming in, and the team has quietly stopped acting on most of them.
Not because they don’t care. Because they’ve been burned too many times by alerts that turned out to be nothing.
That’s alarm fatigue. And in my experience, most teams think it’s a volume problem. It’s not. It’s an accuracy problem, and the difference matters a lot for how you fix it.
Predictive maintenance alert volume is only part of the problem
If you have 100 machines in your condition monitoring program and your system generates 500 alerts, but your team can only realistically action 50 of them in a week, you already have a problem. The math doesn’t work. Important signals get buried, and your reliability team spends time chasing alerts that lead nowhere.
But here’s what makes it worse: it’s not just that there are too many alerts. It’s that too many of them are pointing at the wrong things. If your team has started filtering alerts before acting on them, or if the machines that alarm most often are also the ones that never actually fail, that’s the signal you have an accuracy problem, not a volume one.
And the cost of that is real: your team starts over-maintaining in one area while the real problem goes untouched somewhere else. That’s when trust starts to erode, and once it’s gone, it’s very hard to get back.
Why process fluctuations trigger false alarms in condition monitoring
Take a high-pressure compressor. It’s one of the most variable pieces of rotating equipment in a facility, constantly speeding up, slowing down, and changing based on what’s happening around it. To an AI, that variability can look like a developing fault. To a trained vibration analyst, it’s just the machine doing its job.
This is the core challenge with AI-driven condition monitoring: individual spikes in vibration data are not the same as a real maintenance problem. A spike is a process fluctuation. A trend, a gradual and consistent change in vibration behavior over time, is what actually tells you something is wrong inside the machine.
The difference between those two things is everything. And most predictive maintenance programs aren’t built to make that distinction automatically.
The fix: human-in-the-loop vibration analysis
The way we think about it at Augury is that AI and human expertise have to work together. Our machine health AI looks at vibration trends and spectral data to generate detections. From there, every detection goes to a CAT II, III, or IV vibration analyst who reviews the information and makes a call: is this a real maintenance problem, or a process fluctuation?
If it’s a process fluctuation, we log it and don’t send an alarm. If it’s a real problem, we send the alarm with full context and supporting evidence so your team knows exactly what to do and why. From there, it’s a question of urgency: does this need immediate attention, or can it be planned into a scheduled repair window?
By the time a predictive maintenance alert reaches your team, it’s already been vetted by a vibration analysis expert. That’s what makes it actionable.
What smarter predictive maintenance alarm management looks like in practice
I had a customer with a high-pressure compressor that had been in our condition monitoring program for about a year. In that time, the machine generated 50-plus detections. Not a single one was sent to the customer as a predictive maintenance alarm.
Every detection was reviewed by a vibration analyst and confirmed to be a process fluctuation, not a maintenance problem. The machine health timeline never came out of acceptable. And because of that, when Augury does send an alarm on a machine like that, the customer knows it’s real.
That’s the kind of trust a predictive maintenance program should be building. Not just more data, but the right data reaching the right people at the right time.
Watch Episode 2 of Five Minute Maintenance to learn more
Five Minute Maintenance is a monthly series for reliability and maintenance teams, breaking down the concepts and frameworks that help you run a smarter program. Subscribe so you don’t miss the next episode.
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