Industrial Data Staging With Format Conversion for Asset Models
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing industrial automation systems face challenges in efficiently transforming and staging industrial data to align with asset models, leading to inefficiencies in data utilization and analysis.
Innovation Solution
A system and method for determining data connection conditions, converting industrial data to compatible formats, and retrieving data from sources using a processor-based model condition component and device interface, enabling data staging and transformation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If industrial data is directly used from diverse sources without transformation, then data retrieval is simple and fast, but data compatibility and quality are poor
Solution Approach 1:
The patent introduces an intermediary data transformation layer that sits between diverse industrial data sources and the asset model. This intermediary component automatically transforms data from various formats (CSV, JSON, XML, etc.) into a standardized format compatible with the asset model, resolving the contradiction by adding transformation capability without exposing complexity to end users.
Solution Approach 2:
The system performs preliminary data transformation and validation before data is consumed by analytics or predictive maintenance functions. By pre-processing data into compatible formats and validating it against the asset model structure in advance, the system ensures high data quality while keeping the actual data retrieval and usage simple.
2Adaptability or versatility
If data transformation and staging processes are implemented, then data compatibility with asset models improves, but processing time and system complexity increase
Solution Approach 1:
The patent applies parameter changes by transforming data from various formats (CSV, JSON, XML, etc.) into a standardized format that matches the asset model parameters. The system automatically adjusts data parameters including format, structure, and validation rules to ensure compatibility, resolving the adaptability requirement while maintaining efficient processing through automated parameter mapping.
3Measurement precision
If manual data alignment and transformation processes are used, then data accuracy can be verified, but productivity and efficiency decrease
Solution Approach 1:
The system implements self-service automation where the data transformation process automatically verifies alignment accuracy against the asset model without requiring manual intervention. The automated validation checks data compatibility, transforms formats appropriately, and ensures accuracy metrics are met, thereby maintaining measurement precision while dramatically improving productivity by eliminating manual data alignment tasks.
Data Source
Figure 1
Figure 2
Figure 3
AI summary
Industrial automation data staging and transformation (e.g., using a computerized tool) is enabled. For example, a system can comprise: a memory that stores executable components, and a processor, operatively coupled to the memory, that executes the executable components, the executable components comprising: a model condition component (234) that determines whether a defined data connection condition, applicable to an industrial asset model, has been satisfied (1706), and a device interface component that in response to the defined data connection condition being determined to be satisfied, determines an industrial data source that comprises industrial data associated with the defined data connection condition, sends a request (1708) to the industrial data source determined to comprise the industrial data to provide the industrial data in a second data format determined to be compatible with the industrial asset model, and retrieves the industrial data from the industrial data source, wherein the industrial data has been converted by the industrial data source from a first data format to the second data format