Asset Performance Management for Rolling Stock

Success Stories

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

Solution: 

  • 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

Impact:

  • Enhanced visualization of machine critical parameters and analytics based key insights
  • Proactive repairs and maintenance planning

Reduced time-to-market with Unified IIoT platform and Business Apps

Customer:World’s largest Tier-1 Auto and Industrial OEM

Faster time-to-market with Multi-Connectivity IoT logistic Gateway

Customer:Leading North American Semiconductor company

Enhanced User Experience with Touch Screen-based GUI for Next-Gen Kettle Product

Customer:Leading Finnish Kitchen Equipment Manufacturer

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