AI-Driven Predictive Maintenance (IIoT) & Smart Manufacturing by Tredence

de Tredence Inc

This solution is a ready-to-customize, modular offering with deep learning capabilities.

Manufacturing and remote sites have multiple rotary machines. e.g., pumps, drives, gearboxes, compressors, etc. These are critical assets; the failure of any one of them can cause severe disruption to the production lines, potentially even leading to line or even plant shutdown.
This is leading to O&M teams spending several million dollars annually as part of maintenance contracts with OEMs & service providers for maintaining critical equipment. This is apart from the even higher opportunity costs incurred due to productivity losses (up to 50%).
Earlier (in Industry 3.0), it was both cumbersome and expensive to monitor the health of this equipment. Hence such machines were not given importance vis-a-vis health monitoring. But now, with AI-IoT-based solutions, these activities can be more efficient and effective.
The solution from Tredence is designed to
  • Enable comprehensive condition monitoring of all equipment in one location that is both cost effective and adaptable to various operational environments.
  • Deliver predictive and prescriptive alerts to the plant O&M teams ahead of time to minimize failures through Remaining Useful Life (RUL) and Time to Failure (TTF) estimations.
The Tredence Advantage:
  • A deep learning-powered AI engine capable of detecting hidden patterns
  • The Azure-Databricks-led architecture aids in the efficient processing of high volume, high frequency streaming data while optimizing cloud storage and computing costs.
  • Accelerated deployment to the Azure IoT Edge, enabling high offline performance.

Other highlights:
  • Dedicated center of excellence (CoE) for AIoT, smart manufacturing, Edge AI, and digital twins
  • Deploy end-to-end, enterprise-grade AI-powered industrial IoT solutions for industry X.0 initiatives.
  • Support in creating resilient cyber-physical frameworks & solutions for stable manufacturing operation

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