Industrial Digital Twin UI With Reduced-Dimensionality Condition Views
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Solution Overview
Problem
Industrial environments face challenges in effectively utilizing vast amounts of data from IoT sensors, such as vibration data, to improve operations and maintenance, due to complexity and the need for role-specific insights that current digital twin technologies cannot fully address.
Innovation Solution
An enterprise management platform with role-specific executive digital twins that integrate AI-enabled features and enhanced collaboration, providing real-time data curation and visualization tailored to different roles within an organization, enabling effective decision-making and operational control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If vast amounts of data from IoT sensors are collected and processed, then measurement precision and information completeness improve, but device complexity and difficulty of detecting and measuring increase
Solution Approach 1:
The patent segments the complex data processing system into multiple specialized components: digital twin modules for virtual representation, AI/ML modules for pattern recognition, role-based access control modules for different user perspectives, and hierarchical data structures. This segmentation allows each component to handle specific aspects of data processing independently, reducing overall system complexity while maintaining high measurement precision through coordinated operation of these specialized modules.
Solution Approach 2:
The patent introduces digital twins as intermediary virtual representations of physical assets, which mediate between raw sensor data and user interpretations. These digital twins serve as intermediaries that process, filter, and present data in meaningful ways, reducing the complexity of directly handling vast amounts of raw sensor data while maintaining measurement precision through faithful virtual representations.
2Loss of information
If comprehensive data from all sensors is provided to all users, then information completeness improves, but ease of operation deteriorates due to information overload
Solution Approach 1:
The patent implements role-based data presentation where different user roles (executives, operations managers, maintenance personnel) receive customized data views tailored to their specific needs and responsibilities. Each role sees locally optimized information relevant to their function, rather than a uniform comprehensive view. This maintains information completeness for each role while dramatically improving ease of operation by eliminating irrelevant information overload for each user type.
Solution Approach 2:
The system dynamically adapts data presentation based on user role, context, and operational conditions. The interface automatically adjusts which data elements are displayed, their level of detail, and their organization based on the user's role and current operational context. This dynamic adaptation ensures each user receives the appropriate level of information completeness for their needs while maintaining ease of operation through context-aware personalization.
3Productivity
If real-time data processing is implemented, then productivity and response time improve, but use of energy and computational resources increases
Solution Approach 1:
The patent implements preliminary action by pre-computing and maintaining digital twin representations of assets, pre-processing sensor data streams, and pre-establishing analysis models. This preliminary processing occurs in advance of actual operational decisions, allowing real-time responses to be generated by querying pre-computed models rather than performing full data analysis at the moment of decision. This maintains high productivity and fast response times while reducing instantaneous computational energy consumption.
Solution Approach 2:
The system dynamically adjusts processing parameters such as data sampling rates, analysis depth, and model complexity based on operational conditions, asset criticality, and system load. For non-critical assets or during low-priority periods, processing parameters are reduced to minimize energy consumption. For critical assets or during high-priority operations, parameters are increased to maintain productivity. This adaptive parameter adjustment balances operational efficiency with computational energy usage.
Data Source
AI summary
User interfaces configured to provide a list of conditions of interest to at least one entity associated with a role type stored within a role taxonomy, and to select a condition of interest in response to a user selection from the list of conditions; and a controller configured to determine a reduced dimensionality view of the data in response to a determined structure in the data and further in response to the selected condition of interest. The reduced dimensionality view a plurality of graphical elements representing mechanical portions of a machine of the industrial environment associated with the condition of interest. The reduced dimensionality view further comprises a plurality of highlighted graphical elements representing sensors from the plurality of input sensors that provided data outside an acceptable range of data. The user interface is further configured to display the reduced dimensionality view.