Industrial Control Data Quality Indicators From Device Timestamps
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Solution Overview
Problem
Industrial automation systems face challenges in effectively analyzing and enhancing the quality of data from industrial devices, lacking comprehensive methods to determine data reliability and contextual metadata, which hinders efficient operational analytics and decision-making.
Innovation Solution
A system and method that includes a device interface to retrieve data from industrial devices, a quality component to determine a quality indicator based on timestamp and control data, and an output component to generate a quality representation, enhancing data reliability and contextualization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If industrial data is collected from multiple devices without quality assessment, then data quantity increases, but data reliability deteriorates
Solution Approach 1:
The system performs preliminary quality assessment of industrial data before it is fully processed or stored. The quality component evaluates data quality indicators (such as timestamp accuracy, data completeness, sensor calibration status) at the point of collection, preventing low-quality data from entering the analysis pipeline and thus maintaining high data reliability while allowing substantial data quantity to accumulate.
Solution Approach 2:
The quality component acts as an intermediary between data collection and data processing/storing. It mediates by assessing data quality and determining whether data meets thresholds for further processing, thereby separating the quantity accumulation function from the reliability assurance function through this intermediate quality gatekeeping layer.
2Loss of information
If comprehensive data analysis is performed without quality indicators, then analytical depth increases, but decision accuracy deteriorates
Solution Approach 1:
The system implements feedback by using quality indicators to inform and adjust the analytical process. Data quality assessments feed back into the analysis pipeline, allowing the system to weigh, filter, or prioritize data based on quality metrics. This ensures that analytical depth is achieved through comprehensive processing of high-quality data while maintaining decision accuracy by relying on validated quality indicators.
Solution Approach 2:
The system changes parameters by introducing quality indicator thresholds and weights that modify how data is processed analytically. Different data sources or data types can have different quality parameter configurations, allowing the analytical depth to be optimized for each context while maintaining overall decision accuracy through parameterized quality control.
3Productivity
If data context is not enriched with metadata, then processing speed increases, but operational analytics quality deteriorates
Solution Approach 1:
The system segments metadata enrichment from the core data processing pipeline. Quality indicators and contextual metadata are generated and attached as separate layers to the core data, allowing the core processing to maintain high speed while the enriched metadata provides additional analytical value when needed, thus resolving the contradiction between processing speed and analytics quality.
Solution Approach 2:
The system adds metadata and quality indicators as an additional dimension to the data structure rather than embedding them in the core processing flow. This dimensional separation allows fast processing of the primary data while the extended dimension of metadata provides enriched context for operational analytics when required, achieving both speed and quality.
Data Source
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AI summary
Industrial automation data quality and analysis (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 that retrieves industrial data (416) from an industrial device (120) represented in an industrial asset model, wherein the industrial data comprises control data applicable to the industrial device and timestamp data generated by the industrial device concurrently with the control data (1006), a quality component (224) that, based on the control data and the timestamp data, determines a quality indicator applicable to the control data, and an output component that (216), based on the quality indicator, generates an output representative of a quality of the control data.