Diagnostic Device for Automation Systems Using SFC Variable Extraction
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Solution Overview
Problem
Conventional diagnostic methods for monitoring automation systems include irrelevant process variables, leading to unreliable diagnostic statements due to the evaluation of non-relevant data from field bus communication.
Innovation Solution
A diagnostic method and device that selectively evaluates only relevant process variables based on controlled and measured variables from Sequential Function Charts (SFCs) using self-organizing maps, ensuring accurate diagnosis by linking monitored process variables meaningfully for analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If all process variables from field bus communication are evaluated for diagnosis, then comprehensive monitoring is achieved, but irrelevant variables falsify the diagnostic statement and reduce reliability
Solution Approach 1:
The patent extracts only the relevant process variables needed for diagnosis by analyzing the SFC program. The evaluation device identifies controlled variables and measured variables that are actually used in the step sequence logic, and excludes all other irrelevant process variables from the diagnostic evaluation, thereby preventing falsification of diagnostic statements
Solution Approach 2:
The patent segments the complete set of process variables into relevant and irrelevant categories based on their usage in the SFC program. By dividing the variable set and selectively evaluating only the relevant portion, the system achieves reliable diagnosis without being contaminated by irrelevant data
2Reliability
If many process variables are monitored and evaluated, then comprehensive system coverage is achieved, but computational needs and data volume increase
Solution Approach 1:
The evaluation device extracts only the minimal necessary process variables that are actually used in the SFC control logic. By taking out only the essential variables (controlled variables and measured variables referenced in transitions) and excluding all others, the system reduces computational load and data volume while maintaining comprehensive diagnostic coverage of the actual control process
Data Source
AI summary
A diagnostic device and diagnostic method for monitoring operation of a technical system with an automation system, wherein values of process variables, which were previously automatically determined as relevant to a diagnosis by analyzing a program for a sequential function chart, are determined when each step of the cycle to be checked is executed and evaluated based on at least one predetermined self-organizing map acquired based on fault-free cycles during a system operation with repeatedly run step sequences such that automatic preselection of the process variables is which are relevant to the diagnosis is performed such that misdiagnoses can advantageously and largely be avoided and the reliability of the diagnostic statement can be increased.