Extensible Asset Data Model for Predictive Fault Control
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Solution Overview
Problem
Existing data management systems require significant time and resources to develop custom data models for each industry application, leading to complex integration challenges and high costs.
Innovation Solution
An extensible homogeneous data model is developed, which is a graph data structure that can be applied to multiple industry applications. This model includes nodes representing entities and edges describing relationships, allowing for easy extension and customization for different industry applications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If custom data models are developed for each industry application, then application-specific requirements are met, but development time and resources increase significantly
Solution Approach 1:
The patent implements a universal data model framework that can serve multiple industry applications simultaneously. The system defines a core set of data models that are applicable across different domains (manufacturing, healthcare, finance, etc.), eliminating the need to build separate custom models for each application. This universal approach reduces development time while maintaining the ability to meet application-specific requirements through configuration rather than custom development.
Solution Approach 2:
The data model framework is segmented into hierarchical layers: core data models that provide foundational structures, industry-specific extensions that add domain relevance, and application-specific customizations that address unique requirements. This segmentation allows teams to reuse core models across applications while only developing the necessary extensions and customizations, significantly reducing overall development time and resources.
2Adaptability or versatility
If custom data models are developed for each industry application, then specific industry requirements are satisfied, but system complexity increases
Solution Approach 1:
The system segments the data model complexity into manageable layers: a standardized core layer that remains simple and consistent across all applications, and extension layers that contain application-specific complexity. This segmentation isolates complexity to where it is needed while maintaining simplicity in the shared foundation, reducing overall system complexity.
Solution Approach 2:
The patent introduces an intermediary mapping layer that translates between the universal data model structure and application-specific requirements. This intermediary handles the complexity of adaptation by providing standardized interfaces and transformation rules, preventing complexity from propagating throughout the entire system while still enabling industry-specific customization.
3Adaptability or versatility
If custom data models are developed for each industry application, then application-specific functionality is achieved, but integration challenges increase
Solution Approach 1:
By implementing a universal core data model that all applications adhere to, the system ensures consistent data structures, validation rules, and interfaces across different industry applications. This universality eliminates integration challenges that arise from incompatible custom models, as all applications can interact through the standardized core structure while maintaining their specific functionality through extensions.
4Adaptability or versatility
If custom data models are developed for each industry application, then specific industry needs are met, but costs increase
Solution Approach 1:
The universal data model framework allows a single development effort to serve multiple industry applications, dramatically reducing the quantity of development resources required. Instead of funding separate custom model development for each application, organizations can invest in the universal framework once and then use it across multiple projects, reducing overall resource consumption and costs.
Solution Approach 2:
The patent merges the development of data models across multiple applications by identifying and consolidating common structures and requirements into the universal core. This merging eliminates redundant development efforts where similar data models would otherwise be built separately for different applications, reducing the total quantity of resources needed for model development across the organization.
Data Source
AI summary
A system and method for monitoring and controlling assets to mitigate predicted future faults. The method includes receiving, by a processing circuit, data describing an asset from one or more data sources; generating, by the processing circuit, an asset data model based on the received data; receiving, by the processing circuit, an extensible data model describing an organizational structure of an enterprise associated with the asset; extending, by the processing circuit, the extensible data model to include the asset models executing, by the processing circuit, the extensible data model including the asset models to determine one or more key performance indicators for the asset; predicting, by the processing circuit, a future fault for the asset based on the key performance indicators; sending, by the processing circuit, an informed and prioritized notification to plant personnel regarding the predicted fault; and taking a corrective action to mitigate the predicted future fault.