PLC Log Data Abnormality Prediction System
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Solution Overview
Problem
Conventional PLC data log modules are inefficient in analyzing large datasets over time, failing to provide timely abnormality sign indications and lacking the ability to predict abnormality occurrences by not analyzing data trends, variations, or correlations.
Innovation Solution
A system comprising a controller, analyzer, and analysis result storage that analyzes PLC log data for abnormality occurrences, generates prediction signals when similar conditions are detected, and includes an alarm unit to notify users of predicted abnormalities, with features like pattern analysis and storage of time-related data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data is stored for a long time in the conventional PLC data log module, then more historical data is available for analysis, but the time required for analyzing the data amount increases significantly
Solution Approach 1:
The system performs preliminary filtering and categorization of data during the storage phase. The controller pre-processes incoming data to identify and separate abnormality-related data from normal operational data, storing them in different data structures. This preliminary action reduces the volume of data that requires detailed analysis later, thereby reducing analysis time when historical data needs to be examined.
Solution Approach 2:
The data storage and analysis system is segmented into multiple components: normal data storage, abnormality data storage, and different analysis modules. By segmenting the data into categories (normal operations vs. abnormality events) and using specialized analysis routines for each category, the system avoids the need to analyze entire datasets, significantly reducing analysis time while maintaining comprehensive data availability.
2Device complexity
If the conventional PLC data log module only stores device values without analysis functions, then the device complexity is reduced, but the ability to provide abnormality predictions and data trends is lost
Solution Approach 1:
The patent introduces an intermediary analysis system that sits between the simple data logging function and the user. This intermediary layer includes controllers and analyzers that automatically process stored data to generate trends, correlations, and abnormality predictions. The intermediary handles the complex analysis work, allowing the core PLC logging module to remain simple while the system as a whole gains advanced analytical capabilities.
Solution Approach 2:
The system implements self-service analysis capabilities where the controller and analyzer automatically process stored data without requiring user intervention. The system autonomously identifies patterns, detects abnormalities, generates predictions, and presents results to users. This self-service approach adds sophisticated analysis functionality while maintaining ease of operation, as users receive processed insights rather than raw data requiring manual analysis.
3Speed
If the system analyzes all PLC log data in real-time, then abnormality detection speed is improved, but the processing load and energy consumption increase
Solution Approach 1:
The system applies partial analysis by focusing computational resources only on data segments that contain abnormality indicators. Rather than analyzing all incoming data at full processing intensity, the controller identifies key parameters and time periods showing abnormal patterns, then applies detailed analysis only to those specific segments. This partial action approach maintains fast abnormality detection while significantly reducing overall processing load and energy consumption.
Data Source
AI summary
The present disclosure relates to a system for predicting abnormality occurrence using a PLC log data, the system including a controller configured to receive a data from a lower-level device connected to a PLC data log module and determine the data, and to store the data in an event storage when the data is a data related to abnormality occurrence, an analyzer configured to generate an abnormality analysis result by analyzing the data related to abnormality occurrence, and an analysis result storage configured to store the abnormality analysis result, wherein the controller compares the data transmitted from the lower-level device with the abnormality analysis result stored in the analysis result storage, and generates an abnormality occurrence prediction signal, when it is determined that the data transmitted from the lower-level device is similar to the abnormality analysis result.