Industrial Data Communication with Field-Level Data Quality Correction
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Solution Overview
Problem
The quality of data produced by industrial devices poses a significant obstacle in achieving the benefits of Industry 4.0, as defective data can distort monitoring and analysis results, leading to inaccurate predictions and process control.
Innovation Solution
A method for data communication between a management level and a field level in industrial systems that includes a data communication module to receive and check data queries and responses, applying predefined quality rules to identify and correct defective data, and performing corrective measures to ensure data quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data aggregation and formatting steps are performed to prepare data for management level applications, then data handling efficiency is improved, but data quality may deteriorate due to potential defects being introduced or amplified during processing
Solution Approach 1:
The patent applies preliminary action by performing data quality checks at the field device level before data leaves the field level. The checking module validates data completeness, plausibility, and consistency at the source, preventing defective data from entering the aggregation pipeline. This upfront validation ensures that subsequent data processing operations work with high-quality data, eliminating the risk of defect amplification while maintaining processing efficiency.
2Reliability
If data quality checks are performed on all response data from field devices, then data quality is improved, but processing time and system complexity increase
Solution Approach 1:
The patent applies local quality by implementing targeted validation rules specific to each field device type and data category. Rather than applying uniform comprehensive checks to all data, the system configures appropriate validation criteria locally for each data source based on its characteristics. This selective approach maintains high data quality while minimizing unnecessary processing overhead and time loss.
Solution Approach 2:
The system performs data quality checks selectively based on risk assessment and data criticality. For high-criticality data, comprehensive validation is applied, while for lower-criticality data, streamlined checks are sufficient. This partial action approach ensures adequate quality control without the time penalty of exhaustive validation on all data points, balancing quality and efficiency.
3Reliability
If comprehensive validation rules are applied to all field devices, then data quality is improved, but device complexity and implementation difficulty increase
Solution Approach 1:
The patent implements a universal data communication module that handles multiple field device types through a standardized interface. The checking module applies consistent validation logic across diverse data sources, with configurable rules that adapt to different device characteristics. This universal approach ensures high data quality through systematic validation while reducing overall system complexity by avoiding device-specific custom validation implementations.
Data Source
AI summary
Provided is a method for data communication between a management level and a field level of an industrial system, including: a data communication module receiving a data query from a management level application on the management level to a field device on the field level; the data communication module transferring the data query from the management level application to the field device; the data communication module receiving response data from the field device to the data query; a checking module of the data communication module checking a data quality of the response data; and if an insufficient data quality is ascertained in the check, the checking module performing a corrective measure in order to correct the defective response data.