Asset Data Onboarding with General Models for Technical Installations
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Solution Overview
Problem
Current solutions for maintaining industrial installations are inefficient as they primarily focus on individual assets without considering their context, leading to high effort, time, and errors in onboarding and data integration, and struggle with heterogeneous data sources and systems, resulting in incomplete information for predicting abnormal operations and maintaining asset health.
Innovation Solution
A digital platform integrates data from assets by defining general models via ontologies, mapping data from various sources to these models, and creating asset instances that can be easily configured and accessed by applications, simplifying data integration and analysis across the asset lifecycle.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If data from heterogeneous data sources are integrated into a common platform, then data comprehensiveness and analysis capability are improved, but system complexity and integration effort increase
Solution Approach 1:
The patent introduces an intermediary layer consisting of general asset models and data mapping mechanisms that mediate between heterogeneous data sources and the platform. These intermediaries translate and harmonize data from different sources into a unified structure, enabling comprehensive data integration without proportionally increasing system complexity. The general asset models act as standardized interfaces that simplify the integration process.
Solution Approach 2:
The patent segments the data integration process into distinct components: data source identification, data mapping to general models, iterative filling at runtime, and instance formation. This segmentation allows each component to be developed and maintained independently, reducing overall system complexity while achieving comprehensive data integration.
2Adaptability or versatility
If manual onboarding processes are used for asset configuration, then flexibility and customization are improved, but time consumption and error rates increase
Solution Approach 1:
The patent implements preliminary action by pre-defining general asset models that capture common asset structures and data requirements before actual asset onboarding. These pre-configured models serve as templates that automatically guide the onboarding process, reducing manual effort and time while maintaining flexibility through model customization capabilities.
Solution Approach 2:
The system enables self-service onboarding where the platform automatically performs data mapping, model instantiation, and configuration based on pre-defined general asset models. The iterative filling process at runtime allows the system to automatically adapt to specific assets without requiring extensive manual intervention, thereby reducing onboarding time while preserving adaptability.
3Reliability
If additional measurements are implemented for predicting abnormal operations, then prediction accuracy is improved, but engineering effort and control system complexity increase
Solution Approach 1:
The patent creates universal general asset models that can handle multiple data types and prediction requirements through a unified structure. These multi-functional models can accommodate various measurements and prediction algorithms without requiring separate control systems for each function, thereby improving prediction accuracy while minimizing additional complexity.
4Adaptability or versatility
If heterogeneous databases are maintained for different assets, then data specificity and asset customization are improved, but consistency and update difficulty increase
Solution Approach 1:
The patent introduces general asset models as intermediary structures that maintain a standardized schema for all assets. These intermediaries ensure database consistency by providing a unified data structure, while still allowing asset-specific customization through instance-level configurations. The mapping layer translates heterogeneous asset data into the standardized model structure, maintaining consistency across the entire database.
Data Source
AI summary
A method for integrating data from assets of a technical installation into a platform, wherein general models are initially defined for the assets of domain-specific technical installations, where data from the identified general data sources are then assigned to previously defined general models, an asset is then selected for a specific installation and the corresponding general model is imported into the platform, the data sources of the specific installation are configured such that a general model can be iteratively filled at runtime with the specific data relating to this installation for the selected asset based on data sources of specific installations, entities for the selected asset are formed by filling the general model with the specific data and stored in the platform, and where the asset entities are configurable in the platform, such that applications can access asset entities and the data thereof without any problems via programming interfaces.