Submitted by admin on Thu, 08/18/2022 - 11:39

Unplanned downtime impacting production

Sequence Number 1 Industry Manufacturing Banner Unplanned downtime impacting production How 5G enabled

Improve asset availability through forecasting early fault detection in critical components and conducting proactive replacements.

Data Flows
Title Devices Icon Devices Description
  • Sensors deployed on all critical operational assets
  • Purpose: Measure pressure, speed, flow, etc.
Title Connectivity Icon Connectivity Description
  • Time series data transport
  • Asset data (maintenance records) access
Title Edge Compute Icon Edge Compute Description
  • Not time critical at present (predictions timeframe are in days), may change when real-time prediction is required from other equipment
Title Cloud Compute & Storage Icon Cloud Compute & Storage Description
  • All data from the edge sensors (both historical and real-time)
  • Enterprise storage is company-owned
Title Applications & Services Icon Applications & Services Description
  • Predict future asset failure using ML models, initial improving model
  • PaaS-based set up, not company-owned
Title Inform Decision Makers Icon Inform Decision Makers Description
  • Only reporting potential future failure so action can be taken in time
Title Support Decision Making Icon Support Decision Making Description
  • End of process
Application Logic
Description
  • Identify critical assets such as engines, fridges, belts, compressors, etc.
  • Collect event and/or timeseries data from these critical assets, potentially using sensors.
  • Collect as much data as possible from selected assets and surroundings as ML models will be strengthened with more data.
  • If there is a lack of data, look at options of creating and using synthetic data.
Description
  • Timeseries data will be stored long-term in Enterprise storage.
  • AI (ML) application requests all the data it needs for ML model development, from Enterprise storage to copy in its own storage cache.
  • Development of the ML model is done through an iterative process and a quality ML model (fully data-driven) will require multiple steps to detect anomalies/potential failures.
  • SME involvement working with data scientists is required to develop the model.
  • Models will be stored and maintained by AI applications.
Description
  • The process from data collection to execution of models is fully automated.
  • Only alerts to operations in case of anomalous data behavior (e.g., resulting in expected asset failure in X days).
  • Operations takes action to replace suspected assets in time, therefore avoiding unplanned downtime.
  • ML model management is important to handle large number of models.
  • Various options for visualisation of entire operations (e.g., XR options on OpenXR platform).
Expected benefits Key value created