Industrial Vibration Digital Twins for Predictive Maintenance
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Solution Overview
Problem
Industrial environments face challenges in efficiently collecting and utilizing vast amounts of data from vibration sensors and other IoT devices to predict maintenance needs and optimize operations, due to data complexity and limited use of captured expertise.
Innovation Solution
A platform for the Industrial Internet of Things (IIoT) that includes distinct data-handling layers for monitoring, storage, and adaptive intelligent systems, enabling the creation and management of digital twins for industrial entities, which collect and update data in real-time using various sensors and models to simulate operations and optimize processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If vibration sensors and IoT devices are deployed to collect industrial data, then predictive maintenance capability is improved, but data complexity and processing burden increase
Solution Approach 1:
The patent creates digital twins as virtual copies of physical industrial entities. These digital twins replicate the behavior, state, and characteristics of physical assets, allowing predictive maintenance analysis to be performed on the virtual model rather than directly on complex sensor data from the physical asset. This copying approach simplifies data processing while maintaining predictive accuracy.
Solution Approach 2:
The digital twin acts as an intermediary between the physical industrial asset and the predictive maintenance system. Instead of directly processing raw vibration sensor data and other complex IoT data streams, the system uses the digital twin as a mediator that transforms physical data into actionable insights, reducing the processing burden on the predictive maintenance infrastructure.
2Productivity
If digital twins are created and updated in real-time, then operational optimization is improved, but data storage and processing requirements increase
Solution Approach 1:
The patent implements a hierarchical digital twin architecture where digital twins are created at different levels of abstraction. Rather than storing and processing all possible data for every aspect of an industrial entity, the system creates digital twins with specific qualities and data representations appropriate to their purpose. This local quality approach allows operational optimization while managing data storage requirements through selective data representation.
3Loss of information
If expertise is captured and stored for guiding workers, then knowledge transfer is improved, but system complexity increases
Solution Approach 1:
The patent captures industrial expertise by creating digital representations of expert knowledge, procedures, and decision-making processes. These expertise models are stored within the digital twin system, allowing knowledge transfer to occur through the digital twin interface rather than requiring direct mentorship. This copying of expertise reduces knowledge loss while the structured integration into the existing digital twin framework minimizes additional system complexity.
Data Source
AI summary
A platform for updating one or more properties of one or more digital twins including receiving a request for one or more digital twins; retrieving the one or more digital twins required to fulfill the request from a digital twin datastore; retrieving one or more dynamic models corresponding to one or more properties that are depicted in the one or more digital twins indicated by the request; selecting data sources from a set of available data sources based on the one or more inputs of the one or more dynamic models; obtaining data from selected data sources; determining one or more outputs using the retrieved data as one or more inputs to the one or more dynamic models; and updating the one or more properties of the one or more digital twins based on the one or more outputs of the one or more dynamic models.