Predictive maintenance is an approach that analyses equipment condition data to anticipate when a failure may occur and plans maintenance accordingly. The goal is to reduce both unnecessary time-based maintenance and unplanned downtime. Yet many initiatives stall at the pilot stage because basic steps are skipped.
Step 1: Identify critical equipment
Not every asset is a candidate for predictive maintenance, nor does it need to be. The first task is to identify the equipment whose failure has the highest impact. These questions help:
- When this equipment stops, does production stop too?
- Does a failure create a safety or environmental risk?
- Does sourcing spare parts take a long time?
- Does a measurable symptom (vibration, temperature, current) appear before failure?
Starting with equipment that gets a "yes" to most of these questions makes the value of the investment visible quickly.
Step 2: Put maintenance records in order
Predictive models cannot foresee the future without knowing the past. When each failure started, when it was fixed, its cause and the work carried out must all be recorded consistently. Paper forms or free-text notes make this analysis almost impossible.
Once failure codes are standardised in a maintenance management system (CMMS), core metrics such as MTBF and MTTR become reliable as well. These metrics are the baseline for measuring the impact of predictive maintenance.
Step 3: Collect the right data
In many plants, control systems already produce valuable data: motor current, temperature, pressure, flow. Before investing in new sensors, examine how much of the existing data is usable. Vibration or temperature sensors can be added where gaps remain.
In data collection, context matters as much as frequency: you need to know which equipment, which operating mode and which product a measurement belongs to.
Step 4: Start with simple rules, then move to models
Predictive maintenance immediately brings AI to mind. Yet the first value often comes from simple threshold and trend rules: raising an alert when temperature crosses a limit or vibration shows a sustained upward trend.
Once enough data and failure examples accumulate, machine learning models can learn normal behaviour and detect deviations earlier and with fewer false alarms. MLOps practices are then needed to monitor models in the field and retrain them when necessary.
Step 5: Turn alerts into work orders
This is the most frequently skipped step. If an alert does not enter the maintenance team's workflow, it remains just a notification on a screen. Alerts should automatically become maintenance work orders, be assigned to an owner, and have their outcome written back to the system.
- The alert becomes a work order with the related equipment and a suggested check.
- The maintenance window is chosen in line with the production plan.
- The outcome is recorded: was there really a problem?
- This feedback is used to improve rules and models.
Conclusion
Predictive maintenance is not a product but a capability. Choosing critical equipment, putting records in order, collecting the right data, moving from simple to complex and closing the loop with work orders are its foundations. This is the approach that brings together maintenance management in OREL IMS and the data and AI layer in OREL DM.