Facility Diagnosis Using PLC, Imaging, and IoT Failure Correlation
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Solution Overview
Problem
Conventional facility diagnosis methods struggle to precisely identify the direct cause of failures in industrial facilities, often attributing abnormalities to external factors rather than internal issues, and fail to accurately determine the generation time point of failures due to reliance on measuring process progress times.
Innovation Solution
A facility diagnosis system incorporating a diagnostic module, an imaging module, and IoT sensor units that generate and analyze events based on preset conditions, allowing for real-time data collection and storage from PLC memory areas, image recognition, and IoT monitoring to provide detailed objective data for precise failure analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional facility diagnosis methods are used, then the system is simple and easy to operate, but the measurement precision and accuracy of failure cause identification deteriorates
Solution Approach 1:
The diagnosis system is segmented into three independent modules: a diagnostic module that collects PLC data, an imaging module that captures facility images, and an IoT sensor unit that gathers environmental data. Each module operates independently and contributes specific data types to the overall diagnosis, allowing the system to achieve high measurement precision through specialized functions while managing complexity through modular architecture.
Solution Approach 2:
The patent merges multiple data sources (PLC diagnostic data, image data from imaging module, and sensor data from IoT units) into a unified diagnosis framework. By combining these different types of data and analyzing them together, the system achieves comprehensive failure cause identification that is more accurate than any single data source could provide alone.
2Productivity
If engineer monitoring is used, then the system is simple, but the response speed and productivity deteriorates due to labor fatigue and inefficiency
Solution Approach 1:
The facility diagnosis system performs self-diagnosis by automatically collecting data from PLC memory areas, imaging modules, and IoT sensors, then analyzing this data to identify failure causes without requiring continuous human monitoring. The system serves itself by autonomously detecting abnormalities and generating diagnosis results, eliminating labor fatigue and significantly improving diagnosis efficiency while reducing the time lost to manual inspection.
Solution Approach 2:
The system continuously monitors facility operations and provides real-time feedback through automated analysis of collected data. When abnormalities are detected, the system immediately generates diagnosis results and notifications, creating a closed-loop feedback mechanism that enables rapid response to failures without human intervention delays.
3Reliability
If conventional diagnosis methods are used, then the device complexity is low, but the reliability of failure cause identification deteriorates due to inability to accurately determine generation time point
Solution Approach 1:
The system performs preliminary data collection by continuously monitoring and storing data from PLC memory areas, imaging modules, and IoT sensors before failures occur. This preliminary action ensures that when a failure happens, all relevant data is already captured and timestamped, allowing for reliable backward analysis of the failure cause and its generation time point without requiring complex real-time analysis during the failure event itself.
Data Source
AI summary
The present invention relates to an equipment diagnosis method using equipment diagnosis system comprising: an imaging module (110) for collecting image data by photographing the equipment having an equipment controller, in which a PLC is loaded, embedded therein; a diagnostic module (120) including hardware having software for diagnosing whether the equipment is normal or abnormal; and a plurality of IoT sensor units (130) for monitoring an object to be monitored, and thus a user can quickly diagnose, identify, and cope with a specific cause of an equipment failure on the basis of objective data provided from a PLC memory area, and image file, and an IOT sensor unit at the occurrence of various types of events generated by a diagnostic module for each condition designated by the user according to the state of equipment.