I spent a weekend reviewing a maintenance log for a conveyor system that was costing thousands in "preventative" parts replacements every quarter, only to find that the technicians were throwing away bearings that had 60% of their life left. At the same time, a motor had burned out three weeks before its scheduled service because it had been running hot for a month, but the calendar said it wasn't time to check it yet.
Time-based maintenance is a gamble where you bet that the average failure rate of a component matches the actual failure rate of your specific machine. In the real world, that bet usually loses.
If you're managing industrial assets, you've likely lived through this. You either over-maintain, wasting money and introducing "infant mortality" failures by disturbing a working system, or you under-maintain and deal with unplanned downtime. The move to Condition-Based Maintenance (CBM) is the only way out, but the gap between the theory of "predictive maintenance" and a working system on the factory floor is massive.
What I tried first
My first attempt at CBM was naive. I thought I could just slap a few sensors on the equipment, pipe the data into a dashboard, and let the operators decide when to perform maintenance. I set up a basic MQTT pipeline using Mosquitto (which I've written about before regarding broker selection) and pushed raw vibration and temperature data to a Grafana dashboard.
