Most factories still use threshold alarms because nobody's shown them the bridge
You've got a vibration sensor on your pump. When RMS velocity hits 7.1 mm/s (per ISO 10816), an alarm fires. Your maintenance team runs over, shuts it down, finds nothing wrong 60% of the time. The other 40%? The bearing's already toast.
This isn't a technology problem. It's a migration problem.
Every CBM guide jumps straight to LSTM networks and transformer architectures. But if you're running threshold-based monitoring today, you don't need a research paper — you need a 4-week roadmap that keeps your alarms running while you build confidence in ML predictions. That's what this post is: the actual Python migration path I'd follow if I walked into a factory floor tomorrow with nothing but a CSV export and a reliability engineer who's (rightfully) skeptical of AI.
Week 1: Augment thresholds with statistical baselines (no ML yet)
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