A motor bearing doesn't fail without warning. It whines louder, runs hotter, draws more current — for days or weeks — before it breaks. The data exists. The question is whether anything is close enough, fast enough, to act on it.
From reactive to predictive
Old-school maintenance meant fixing things after they broke, or replacing parts on a fixed schedule regardless of actual condition. Predictive maintenance replaces both — using sensor data to forecast the ideal maintenance window and maximize uptime. Models are trained to catch early failure signatures: vibration drift, thermal creep, abnormal current draw. One key output is Remaining Useful Life (RUL) estimation — a prediction of how much longer a component will run before failing, replacing guesswork with real equipment condition.
The cloud's fatal flaw: latency
Streaming sensor data to the cloud for analysis seems obvious. It isn't, here. Cloud round-trips — relay, process, send back — are invisible for a monthly report and fatal for catching a machine seconds from failure, or a hazardous condition before it escalates.
