Analytics Avatars for Cross-Platform Industrial Predictive Diagnostics
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Solution Overview
Problem
Existing predictive diagnostics for industrial assets are often developed from scratch for each asset type, leading to inefficiencies and inconsistencies across different platforms, requiring significant time and effort for deployment and reuse.
Innovation Solution
The development of 'analytics avatars' – containerized packages of predictive diagnostic models and components that can be deployed across multiple platforms, allowing for the reuse of existing models and components, enabling efficient assembly and deployment of predictive diagnostics for industrial assets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If predictive diagnostics are developed from scratch for each asset type and platform, then each solution can be optimized for its specific asset, but the development time and effort increase significantly and consistency across platforms is lost
Solution Approach 1:
The patent creates a universal predictive diagnostic platform that can serve multiple asset types and computing systems simultaneously. The system uses a common data model and machine learning framework that can be applied across different platforms (cloud, edge, embedded) and asset types, eliminating the need to develop separate solutions for each while maintaining optimized performance through platform-specific deployment configurations.
Solution Approach 2:
The system segments the predictive diagnostic functionality into modular components including data ingestion modules, machine learning model components, and platform-specific deployment packages. This segmentation allows the core diagnostic logic to be developed once and then deployed across multiple platforms independently, reducing development time while maintaining reliability through consistent core functionality.
2Adaptability or versatility
If separate predictive diagnostic products are developed for cloud, edge, and embedded platforms, then each platform can be optimized for its specific computing environment, but the complexity of managing multiple products increases and reuse becomes difficult
Solution Approach 1:
The patent implements a universal predictive diagnostic platform that can be deployed across cloud, edge, and embedded environments using a single codebase and common data models. The system maintains platform-specific optimizations through configuration files and deployment manifests rather than separate product developments, reducing portfolio complexity while preserving adaptability to different computing environments.
Solution Approach 2:
The system dynamically adapts to different computing platforms at runtime rather than requiring static platform-specific versions. The predictive diagnostic engine can adjust its operation based on the deployed environment, allocating resources and configuring processing pipelines dynamically according to the capabilities of cloud, edge, or embedded systems.
3Manufacturing precision
If predictive diagnostic models are rebuilt for each new asset deployment, then the models can be tailored to specific asset characteristics, but the engineering effort and cost increase significantly
Solution Approach 1:
The patent implements pre-configured predictive diagnostic templates and pre-trained machine learning models that can be quickly deployed to new assets. The system includes pre-built data models, feature extraction pipelines, and algorithm configurations that are prepared in advance, allowing rapid deployment to new assets while maintaining accuracy through asset-specific parameter configuration rather than complete model rebuilding.
Solution Approach 2:
The system enables copying and reusing of predictive diagnostic models across similar assets. Once a model is developed and validated for a particular asset type, it can be copied and deployed to other assets of the same type with minimal reconfiguration, maintaining model accuracy through consistent data models while dramatically improving deployment efficiency through reuse.
Data Source
AI summary
Systems and methods provide a way for users to leverage existing data-driven models, components, and functions to deploy predictive diagnostics on industrial assets. The systems and methods allow the users to retrieve and assemble existing models, components, and functions into individual packages or containers called “analytics avatars” that can be saved and stored as independent predictive diagnostic units or entities. The analytics avatars can then be deployed on virtually any analytics platforms to provide predictive diagnostics for the industrial assets. The ability to leverage existing predictive diagnostic models and components from plant to plant across different platforms for equipment of the same type provides consistency and reduces the time and effort required to engineer and deploy predictive diagnostics.