Archive Analysis System for Message Reduction in Process Control
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Solution Overview
Problem
Modern process control systems in technical installations, such as power plants, face information overload, leading to costly operating errors and reduced reliability due to the high volume of messages that operators must analyze under time pressure, with current methods being economically justifiable only for individual cases and relying on manual, statistical evaluations.
Innovation Solution
A method that systematically analyzes archive entries related to messages, operating, and switching actions within a defined time range, determining causes and proposing solutions to reduce message frequency and unnecessary actions, incorporating statistical relevance, frequency, and trend analyses, as well as evaluating connections between function modules to optimize the technical system's operation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If comprehensive archive analysis is performed systematically, then message reduction effectiveness improves, but analysis time and computational resources increase
Solution Approach 1:
The system performs preliminary statistical evaluation of archive entries to identify frequently occurring messages before conducting detailed root cause analysis. This preliminary filtering reduces the scope of subsequent analysis while ensuring that the most significant messages are prioritized for investigation and resolution.
Solution Approach 2:
Manual analysis methods are replaced with automated computer-based analysis systems that can systematically evaluate archive data, perform statistical evaluations, and identify root causes without human intervention. This substitution dramatically reduces analysis time while maintaining or improving the quality of message reduction.
2Measurement precision
If manual analysis of archive data is performed, then detailed root cause identification is achieved, but operational effort and costs increase
Solution Approach 1:
The analysis system performs self-service by automatically evaluating archive entries, identifying statistical patterns, and determining root causes without requiring manual operational intervention. The system serves itself by using its own computational resources to analyze its own archive data and generate improvement recommendations.
Solution Approach 2:
The system implements feedback loops where analysis results are used to identify specific improvements in functional plans, which are then implemented and monitored. This continuous feedback mechanism ensures that root cause identification leads to actionable improvements while reducing the need for repeated manual analysis.
3Loss of information
If statistical evaluation is applied to all archive entries, then comprehensive message patterns are identified, but computational resources and processing time increase
Solution Approach 1:
Instead of performing exhaustive statistical evaluation on all archive entries, the system applies partial action by focusing statistical analysis on the most frequent messages identified through preliminary evaluation. This approach captures the essential message patterns while avoiding the computational burden of analyzing every single archive entry in detail.
Data Source
Figure 1
AI summary
The invention relates to method wherein archive entries, which are related to messages, control actions, or switching actions, within a time range to be defined are extensively analyzed. After a statistical relevance of the examined archive entries has been determined, a cause determination is performed for the statistically most relevant archive entries on the basis of an evaluation of the function plans and/or on the basis of an evaluation of time aspects, in which cause determination all archive entries that occurred during the examined time range are used. Solution proposals for reducing the messages, control actions, or switching actions are derived from the causes determined in such a way in order to improve the operation of the technical system.