Rule-Based to ML PHM Migration: 4-Week Python Roadmap

The Threshold That Breaks Rule-Based Systems Your vibration alarm triggered 47 times last month. Twelve were false positives. Three real failures slipped through because they didn't cross your fixed 10mm/s RMS threshold. Rule-based condition monitoring works until it doesn't. The moment you add a second machine model, change bearing suppliers, or shift production schedules, those carefully tuned thresholds become guesses. I've seen teams spend weeks adjusting rules only to have them break again when ambient temperature changed by 10°C. Machine learning doesn't eliminate thresholds — it learns them from data. The migration isn't trivial, but it's methodical. Here's what actually changes when you move from "if vibration > X then alert" to models that predict failures weeks in advance. Photo by Pixabay on Pexels What Rule-Based Systems Actually Do Well Before ripping out your existing logic, understand where rules still win. Continue reading the full article on TildAlice