Digital twin in the context of the Heavy Equipment Monitoring

Cognecto digital twin build for Heavy Equipment Tracking is delivering value to construction company by leveraging data from sensors and providing timely insights to operator and management.

Heavy Construction Equipment Digital Twin

Digital twin in the context of the construction heavy equipment is a virtual but dynamic representation of the operations of the heavy construction equipment fleet owned by a customer to leverage opportunities of improvement and optimization in the areas of Productivity, Reliability, and Safety in addition to ensuring that all asset related contextual information is readily available.

Real-time asset tracking & monitoring data serves as the fundamental building block of the Digital Twin model in the Cognecto platform.

The Digital Twin model in Cognecto delivers Descriptive, Analytical, Diagnostic and Predictive value for their assets under monitoring.

The Cognecto digital twin model delivers descriptive value through the visual status of the asset making it easier to interpret data from a distance.

The diagnostic system uses the measured or derived data to highlight a specific asset state or behaviour based on explicit rules or by leveraging historical data through machine learning algorithms.

Simulation capabilities are leveraged from Cognecto’s analytical engine to assist with areas like performance optimization.

The same analytical engine provides capabilities to deploy complex analytics to provide predictive insights for specific use cases for customers. Cognecto’s digital twin model leans towards asset productivity & performance, providing customers insights into how quickly and efficiently the equipment performs its task, its ability to operate reliably in a safe environment.

Digital Twin for Heavy Equipment monitoring allows a customer to have a 360 degree virtual view of the various operational parameters and ensure that the asset fleet is working within the defined operational limits and delivering on the critical metrics related to productivity, utilization and maintenance, and collaborating to manage and close open issues.

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