Condition based and predictive Maintenance
Reducing Lifecycle Costs of Mechanically Stressed Components
Unexpected failures and fixed maintenance intervals can result in unnecessary material usage, premature component replacement, and increased lifecycle costs.
Our solution enables condition-based maintenance through continuous monitoring of critical components directly within the vehicle and in daily operation. Maintenance is therefore performed based on actual condition rather than predefined schedules.
The result:
- Extended component lifespan
- Reduced spare parts and consumables usage
- Avoidance of unnecessary maintenance interventions
- Planned servicing instead of reactive repairs
- Lower total lifecycle costs
Sensors can be configured and adapted to different applications via our RET measurement system. Data processing is performed locally to only transmit relevant information and thereby enabling long life battery life.
The high cost of reactive maintenance in rail operations
Traditional maintenance strategies involve servicing either at fixed intervals (preventive) or after a fault has occurred (corrective). Both approaches have significant drawbacks—fixed intervals often lead to unnecessary maintenance, while unplanned outages cause costly downtime and emergency repairs.
Predictive maintenance addresses these challenges by using real-time data and analytics to predict failures. With the right sensors and intelligent data processing, rail operators can shift from reactive to proactive maintenance, minimizing risks and costs.
Key challenges solved by predictive maintenance:
- Bearings, motors, brakes, and other components wear out over time. Detecting early stages of wear prevents serious failures.
Conclusion: Understanding the limitations of traditional maintenance approaches is crucial for safe, efficient, and cost-effective railway operations. Predictive maintenance, supported by real-time data and intelligent analytics, enables operators to detect anomalies early, plan interventions proactively, and prevent failures before they occur. This transformation not only improves reliability and safety, but also optimizes resource utilization and extends the service life of critical components.