Industrial Asset Model Updates for Reliable Data Extraction
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Solution Overview
Problem
Existing industrial data extraction methods are inefficient and lack comprehensive data quality management, leading to suboptimal industrial asset modeling and analysis.
Innovation Solution
A system and method for industrial data extraction that includes generating an industrial asset model, applying contextual metadata, and utilizing machine learning for data quality management, enabling enhanced data analysis and predictive maintenance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional industrial data extraction methods are used, then the extraction process is simple, but data quality and reliability are poor
Solution Approach 1:
The patent introduces contextual metadata as an intermediary layer between raw industrial data and the data extraction system. This metadata includes data quality indicators, source information, and contextual relationships that mediate the extraction process, enabling the system to assess and improve data reliability without fundamentally complicating the extraction architecture
Solution Approach 2:
The patent replaces traditional mechanical data extraction methods with machine learning-based approaches. Instead of relying on rigid, pre-programmed extraction rules, the system uses trained ML models that can adaptively identify and extract high-quality data based on learned patterns from historical data and contextual metadata
2Reliability
If comprehensive data quality management is implemented, then data reliability improves, but processing time increases
Solution Approach 1:
The patent applies preliminary action by pre-processing and enriching industrial data with contextual metadata before the main extraction and analysis processes. This includes tagging data with quality indicators, source information, and contextual relationships in advance, so that during actual data extraction, the system can quickly assess data quality without performing comprehensive analysis from scratch
Solution Approach 2:
The patent substitutes time-consuming manual or rule-based data quality assessment with machine learning models that can rapidly evaluate data quality. The ML models, trained on historical data and contextual metadata, can predict data quality metrics in real-time, dramatically reducing processing time while maintaining comprehensive quality management
3Measurement precision
If machine learning is applied for data quality management, then data analysis accuracy improves, but computational resources required increase
Solution Approach 1:
The patent applies local quality by using machine learning selectively rather than uniformly across all data processing tasks. The system uses ML specifically for data quality assessment and extraction where it provides the most value, while relying on traditional methods for routine processing. This localized application of ML maintains high accuracy where needed while controlling overall computational resource consumption
Solution Approach 2:
The patent employs parameter changes by dynamically adjusting ML model complexity and processing intensity based on data characteristics and quality requirements. For high-value or critical data, the system applies more computationally intensive ML analysis, while for routine data, it uses lighter processing. This adaptive parameter adjustment optimizes the balance between analysis accuracy and computational resource usage
Data Source
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AI summary
Industrial data extraction (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 device interface component (206) that matches industrial data (416), accessible via an industrial asset model (418) and determined not to be represented in the industrial asset model, to an industrial device (120) represented in the industrial asset model, and in response to matching the industrial data to the industrial device, extracts the industrial data into the industrial asset model, and a model update component that updates the industrial asset model, resulting in an updated industrial asset model, wherein updating the industrial asset model comprises associating the industrial data with the industrial device.