OneData Software offers digital twin solutions that leverage AWS IoT TwinMaker and AWS IoT SiteWise, combining their expertise in IoT telemetry, analytics, machine learning, and device management to provide virtual replicas of physical assets, equipment, or processes. These digital twins are built to capture the real-world properties, to simulate, monitor, and predict behavior, enabling operational excellence, reduced downtime, and better decision making.

Core Capabilities

1. Asset & Equipment Modeling

o Define digital twin models: representation of physical assets and equipment, including their components, spatial relationships, metadata.

o Use AWS IoT SiteWise for structuring asset hierarchies, measuring equipment telemetry (e.g. temperatures, pressures, vibration), capturing historical and live data.

2. Real-Time Telemetry Ingestion & Synchronization

o Collect data from sensors / devices (via IoT Core / Greengrass), route time-series telemetry to SiteWise or TwinMaker.

o Ensure data streams are kept synchronized with the physical counterpart, dealing with latency, buffering, and edge scenarios.

3. Visualization & Dashboarding

o Build dashboards / 3D visualizations using TwinMaker’s scene composer, integrating live and historical telemetry.

o Enable monitoring of equipment state, operational KPIs, alerts via both dashboards and possibly augmented reality or 3D views.

4. Analytics, Simulation & Predictive Insights

o Use analytics (via SageMaker, Athena, EMR, IoT Analytics) to simulate behavior, run what-if scenarios, predict component failure, optimize process parameters.

o Use anomaly detection to detect deviations in twin behavior vs expected/predicted behavior.

5. Operational Integration & Actions

o Connect the digital twin to alerting, workflows, or maintenance scheduling: e.g. when a twin model predicts component wear, trigger IoT Events / Lambda based responses.

o Support device control or recommendations (for example adjust setpoints) via digital twin feedback.

6. Security & Governance

o Ensure that data used in the twin is secure: IAM, certificate authentication, encrypted communication, audit logs.

o Manage access control to models, telemetry data, visualizations.

o Ensure physical asset metadata and twin metadata are maintained and versioned.

7. Scalability & Edge / Offline Considerations

o Support operation at scale: many devices, many asset models.

o Handle edge or intermittent connectivity scenarios (using Greengrass or local buffering) so twin remains useful even with partial connectivity.

8. Lifecycle & Maintenance of Twins

o Update models as physical assets change or age.

o Maintain twin metadata, versioning, drift in models.

o Retrain predictive or simulation models based on new data.

Benefits

• Enhanced visibility: stakeholders can view operational status of physical assets virtually, in real time.

• Predictive maintenance: by simulating and detecting early anomalies.

• Process optimization: via what-if simulations, understanding impact of parameter changes.

• Reduced costs: lower downtime, more efficient maintenance, fewer surprises.

• Better decision support: data-driven insights, visualizations for both technical and non-technical users.