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What is Predictive Maintenance?
Predictive Maintenance (PdM) uses sensor data, historical maintenance data, and machine learning to predict failures and wear in machines and equipment. The goal: to perform maintenance based on actual condition rather than on a schedule or after a failure occurs.
Data sources: Sensors on machines (vibration, temperature, pressure, current), process data from PLCs (rotational speeds, cycle times), environmental data (humidity, temperature), maintenance history (previous repairs, replaced parts), product quality (scrap, tolerances).
Typical methods: anomaly detection (deviations from normal conditions), classification (probability of failure within a time window), remaining useful life prediction, deep learning (based on sensor time series), physics-informed models (combining domain knowledge with ML).
For small and medium-sized enterprises, PdM is a particularly effective AI use case: a clear business case (downtime is expensive), available data sources (modern systems have many sensors), and mature methods. With the right approach, ROI times of 6–18 months are realistic.
Best Practices for Predictive Maintenance
These six principles have proven effective:
- Start with critical equipment: Where downtime is costly, ROI is achieved quickly.
- Incorporate domain knowledge: Maintenance technicians know the equipment—their experience is worth its weight in gold.
- Ensure data quality: Calibrated sensors, accurate timestamps, consistent maintenance history.
- Use alert levels: Not just alarms—tiered recommendations (monitor, plan, act immediately).
- Build in a feedback loop: Feed confirmed and incorrect predictions back into the model.
- Roll out iteratively: First one piece of equipment, then a type of equipment, then a plant—not all at once.
Common Mistakes in PdM Projects
We often see these pitfalls:
- No business case: Fascinated by the technology but lacking concrete benefits—the project fizzles out.
- Missing sensors: Without the right sensor data, a model can’t detect anything.
- Insufficient failure history: If failures are rare, the model has little to learn.
- Data tools only, no process: Alerts are triggered, but no one responds—useless.
- Maintenance not involved: The model is built against the technicians instead of with them—low adoption rate.
Reactive vs. Preventive vs. Predictive Maintenance
A comparison of three maintenance strategies:
- Reactive maintenance: Wait until a failure occurs—expensive, unpredictable, and leads to consequential damage.
- Preventive maintenance: Replacing parts according to a schedule—safe, but often unnecessarily early.
- Predictive Maintenance: Based on actual condition — the best balance between cost and availability.
Implement Predictive Maintenance with prodot
In a free initial consultation, we’ll identify your most promising asset for PdM and outline a pilot project—including sensor setup, modeling, and process integration.
As an AI partner for small and medium-sized businesses, we build PdM solutions in a pragmatic way—from the first sensor to plant-wide operation.
What We Offer
- AI Consulting — PdM Concept and Implementation.
- Anomaly Detection — the entry-level approach.
- Digital Twin — for complex systems.
- Predictive Analytics — the overarching framework.
