Predictive Maintenance with Machine Learning

What is predictive maintenance

Predictive maintenance is a way of organizing maintenance around the actual condition of each piece of equipment. Instead of waiting for the machine to fail or servicing it on fixed dates, its signals are measured continuously and someone steps in when the data shows that something is starting to go wrong.

Corrective, preventive and predictive

• Corrective: the equipment is repaired once it has failed. The downtime is unplanned.
• Preventive: the equipment is serviced on a schedule or after a set number of hours or cycles, whether it is worn or not.
• Predictive: the equipment is serviced when the data anticipates a failure or degradation.

Between preventive and predictive sits condition-based maintenance, which acts when a signal crosses a threshold. Predictive maintenance goes one step further: it uses models that learn from historical data to detect less obvious deviations.

How it works

1. Capture. Sensors and plant systems record signals such as vibration, temperature, power draw, pressure or cycles.
2. Normal pattern. Historical data shows how the equipment behaves when it is working well.
3. Detection. A machine learning model or a rule compares new data with that pattern and flags the anomalies that anticipate a failure.
4. Decision. The alert goes to the maintenance team, which prioritizes the inspection. The model helps decide, but it does not replace the technician's judgment or trigger control actions without proper validation.

What data predictive maintenance needs

This is the key question before starting. You need:

• Stable signals from the equipment, recorded with their timestamp and unit.
• Historical examples, including periods that ended in failure or degradation.
• A well-recorded reference event, such as maintenance work orders with their cause.
• A precise definition of failure or degradation, agreed with the maintenance team.

If work orders are closed without a cause or downtime data is not standardized, the sensible move is to improve maintenance records first. Without that foundation, the model has nothing to learn from.

A simple example

A plant has several pumps with vibration and temperature sensors. For months it stores those signals along with the maintenance work orders. That history is used to train a model that knows the normal behavior of each pump. When the vibration of one of them starts rising in an unusual way, the system raises an alert and the technician inspects it at the next scheduled stop, before it fails in the middle of production.

Predictive maintenance at DPI

Predictive maintenance is part of DPI's machine learning service: monitoring equipment and machinery in real time, detecting anomalies that anticipate failures and reducing unplanned downtime. DPI's industrial process automation guide explains how it fits with the rest of the plant, from PLC and SCADA to MES and ERP.