Maintenance strategies fall into three broad groups: reactive (run to failure), preventive (time-based) and predictive (condition-based). Predictive maintenance aims to service equipment only when data shows it is needed.
Key Condition Monitoring Techniques
- Vibration analysis: detects imbalance, misalignment, looseness and bearing or gear defects.
- Infrared thermography: reveals hot spots in bearings, couplings and electrical connections.
- Oil analysis: measures viscosity, contamination and wear particles.
- Ultrasound: identifies early bearing friction, leaks and lubrication problems.
- Motor current analysis: highlights electrical and mechanical issues in motor-driven systems.
Building a Program
Start by ranking assets by criticality. Focus on machines whose failure would cause safety risks, major production loss or high repair costs. Establish baseline readings when equipment is healthy, then set alarm limits and a regular route for data collection.
The Role of Digital Tools
Wireless sensors and cloud analytics make continuous monitoring more affordable. Machine learning can help identify patterns, but human expertise is still needed to interpret findings and plan work correctly.
Measuring Success
Track metrics such as unplanned downtime, mean time between failures, maintenance cost per asset and the percentage of planned versus reactive work. A successful program shows a steady shift toward planned, scheduled interventions.