Asset Performance Management for Rolling Stock

Sasken Solution

Scope: To enable predictive maintenance for heavy assets in order to prolong their lifespan as well as to reduce the operational/maintenance cost


  • Sasken’s approach focuses on Vibration Analysis to detect anomalies as vibration signature is most widely used methodology of anomaly detection in heavy machinery
  • Edge: Data acquisition using Accelerometer-based sensors, Protocol translation, Ingestion to the Cloud
  • Cloud and Analytics: Data assimilation on the Cloud enabling unified view of data
  • Supervised & Unsupervised Machine Learning models
  • Condition Monitoring: Current health of the asset and anomaly detection


  • Enhanced visualization of machine critical parameters and analytics based key insights
  • Proactive repairs and maintenance planning
Asset Performance Management for Rolling Stock

Customer: Leading UK-based provider of aftermarket automotive ancillary products

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