Adaptive IIoT Intelligence Layer for Digital Twin Maintenance
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Solution Overview
Problem
Industrial environments face challenges in efficiently collecting, processing, and utilizing vast amounts of data from vibration sensors and other IoT devices to predict maintenance needs and optimize operations, due to complexity and limited data availability, leading to delayed problem diagnosis and expertise loss when experienced workers leave.
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. These digital twins are updated based on real-time sensor data, using dynamic models to predict vibration fault levels and optimize industrial processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If vast amounts of data are collected from vibration sensors and IoT devices, then predictive maintenance capability is improved, but system complexity and data processing burden increase
Solution Approach 1:
The patent segments the data processing system into multiple specialized components: edge devices for initial data collection and preprocessing, cloud-based platforms for advanced analytics, and digital twin virtual models for simulation. This segmentation allows each component to handle specific tasks efficiently, reducing overall system complexity while maintaining predictive maintenance capabilities.
Solution Approach 2:
The patent introduces digital twins as intermediary virtual representations of physical assets. These digital twins act as mediators between raw sensor data and maintenance decision-making, allowing complex data to be processed in a simplified virtual environment before informing real-world maintenance actions.
2Loss of time
If real-time data processing is implemented to optimize operations, then response time to issues is improved, but computational resource requirements increase
Solution Approach 1:
The patent implements periodic data sampling and processing intervals rather than continuous real-time processing. Sensors collect data at predetermined intervals, and analysis is performed in batches, reducing computational resource requirements while maintaining timely response capabilities for critical issues.
Solution Approach 2:
The patent applies different processing qualities to different data streams based on their criticality. High-priority data from critical sensors undergoes immediate real-time processing, while lower-priority data is processed in batches, optimizing the balance between response time and computational resource usage.
3Productivity
If digital twins are created and updated with real-time data, then operational optimization is improved, but data storage and processing demands increase
Solution Approach 1:
The patent creates simplified digital twin copies of physical assets that contain only the essential data and characteristics needed for operational optimization. Rather than storing complete replicas of all asset data, the digital twins contain selectively extracted information that maintains optimization capabilities while reducing storage demands.
Solution Approach 2:
The patent pre-configures digital twins with baseline data and expected operational parameters before deployment. This preliminary action reduces the need for continuous data storage during operation, as the digital twins already contain the reference information needed for comparison and optimization.
4Loss of information
If expertise is captured from experienced workers, then knowledge retention is improved, but system complexity increases
Solution Approach 1:
The patent implements systems where experienced workers can directly input their expertise and observations into the platform through user-friendly interfaces. The system automatically processes and integrates this knowledge without requiring complex manual configuration, allowing knowledge capture while minimizing added system complexity.
Data Source
AI summary
A platform for facilitating development of intelligence in an Industrial Internet of Things (IIoT) system generally includes a plurality of distinct data-handling layers comprising an industrial monitoring systems layer that collects data from or about a plurality of industrial entities in the IIoT system; an industrial entity-oriented data storage systems layer that stores the data collected by the industrial monitoring systems layer; an adaptive intelligent systems layer that provisions available computing resources within the platform; and an industrial management application platform layer that manages the platform in a common application environment.