PLC Ladder Logic Master Patterns for Cycle Anomaly Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for detecting anomalies in automation lines controlled by programmable logic controllers (PLCs) are inadequate due to the complexity of PLC control logic and the difficulty in generating and updating reference master patterns as automation line conditions change.
Innovation Solution
A master pattern generation method that converts PLC ladder logic into a human-understandable format, analyzes the relationship between contacts, generates statistics for bars and links, and creates a master pattern based on these statistics, along with a training method for a cycle analysis model using this master pattern.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If PLC control logic is analyzed to generate reference master patterns for anomaly detection, then anomaly detection capability is improved, but the complexity of generating and updating master patterns increases due to changing automation line conditions
Solution Approach 1:
The system performs preliminary analysis of PLC control logic to pre-generate contact relationships and master patterns before actual anomaly detection is needed. This includes pre-processing ladder logic to extract contact relationships and pre-calculating master patterns from historical log data, so that when anomalies need to be detected, the system can quickly compare against pre-prepared patterns without complex real-time analysis
Solution Approach 2:
The system creates simplified copies of complex PLC control logic by generating contact relationship models and master pattern representations. Instead of analyzing the full complex ladder logic during runtime, the system uses copied and simplified contact relationship data and master pattern templates that capture the essential normal operation sequences, making anomaly detection more manageable
2Adaptability or versatility
If PLC control logic becomes more complex to handle sophisticated manufacturing lines, then automation capability is improved, but the difficulty of diagnosing and monitoring PLC programs increases
Solution Approach 1:
The system segments complex PLC control logic into individual contact relationships and operation sequences. By breaking down the ladder logic into discrete contact pairs and their relationships (normal connection, reverse connection, parallel connection, series connection), the system makes it possible to analyze and diagnose specific contact behaviors independently rather than trying to understand the entire complex program at once
Solution Approach 2:
The system introduces an intermediary layer between the raw PLC control logic and the anomaly detection process. This intermediary includes contact relationship models and master patterns that translate complex PLC logic into simplified representations showing normal operation sequences. These intermediaries serve as mediators that make the complex PLC behavior understandable and diagnosable
Data Source
AI summary
The present disclosure discloses a master pattern generation method which is a major pattern in a repeated cycle by analyzing programmable logic controller (PLC) logic, and a method for training a model that may analyze an error of a cycle using the generated master pattern.The master pattern generation method and the training method for a cycle analysis model according to the present disclosure are different from the related art in that the methods are a technology of processing a machine control language (low-level language) that is difficult for humans to analyze and converting the machine control language into an analyzable language (high-level language), i.e., a machine language processing (MLP)-based technology that analyzes the executed machine language (a language that controls a machine) with a computer and can be understood by humans.