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