Trapeze’s Concept Paper on Predictive Maintenance with EAM and IOrail platforms

Predictive maintenance is becoming increasingly common as a way to improve reliability. Trapeze EAM is able to connect with any condition monitoring and predictive systems to ensure efficient maintenance process and enable evolution of your organisations predictive maintenance approach. In this paper we describe a specific case of how a drive-by system for identifying track anomalies easily could be integrated with our EAM to manage end-to-end early problem identification through to maintenance and asset life cycle management.

In this concept paper on predictive maintenance, we cover the following topics:

• • Non-Intrusive Drive-By Technology for Track Anomaly Identification:

• • • Utilizing non-intrusive drive-by technology allows continuous monitoring during normal rail operations.
    • This technology helps identify track anomalies efficiently.

• • Continuous Condition Monitoring and Early Fault Prediction:

• • • • By combining continuous condition monitoring with machine learning, we achieve early prediction of developing faults.
      • This proactive approach enables maintenance organizations to work efficiently, avoiding unnecessary service disruptions.
    Concept Paper Predictive maintenance EAM Iorail

• • Integration of Machine Learning for Fault Prediction:
    • Integrating machine learning into Trapeze’s Enterprise Asset Management system streamlines fault management.
    • It also provides a feedback mechanism to enhance predictive capabilities further.

Digitize Your Processes, Improve Your Reliability: Trapeze’s Leading Enterprise Asset Management (EAM) Solution Teams Up with Asia Mobility’s IOrail Platform. This powerful collaboration revolutionizes rail vibrational condition monitoring, transforming it into a data-driven, end-to-end predictive maintenance system.