Industrial6 min read
Predictive maintenance on the factory floor with vibration data
Unplanned downtime is one of the biggest costs on a factory floor. Predictive maintenance uses machine data to spot problems before they stop production.
ALLINLET team
The challenge
Most maintenance is either reactive, fixing equipment when it breaks, or preventive, servicing it on a fixed schedule. Both waste time and money. Predictive maintenance uses condition data to service equipment when it actually needs it.
The data sources
- Vibration and temperature sensors: on motors, pumps, fans and gearboxes.
- PLCs and CNC controllers: speeds, loads, cycle counts and alarms, over OPC UA or Modbus.
- Historians: years of process history to build baselines.
- Maintenance systems (CMMS): work orders and service records.
- Energy meters: power draw that changes as equipment wears.
How it works with ALLINLET
- Connect sensors, controllers and maintenance systems, without changing control logic.
- Normalise tags so every machine is described the same way.
- Stream the data to a digital twin or analytics model that learns normal behaviour.
- Raise alerts when vibration, temperature or power drift from that baseline, and create work orders automatically.
What changes
- Maintenance planned around real condition, not the calendar.
- Fewer unplanned stops and emergency repairs.
- Spare parts ordered before they are needed.
- One view of machine health across lines and plants.
Getting started
Start with a handful of critical assets, connect their data in a pilot, and expand line by line once the model proves its value.
( _how allinlet helps )
ALLINLET connects to IoT sensors and industrial systems through 900+ connectors across 22 domains, and delivers one clean data stream, in our cloud or in your own infrastructure.