PLC Process Image Buffering for Root Cause Fault Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional manufacturing and automation systems lack effective fault detection and monitoring capabilities, making it difficult for operators to diagnose the root cause of faults in automation devices, leading to increased downtime, defective products, and inefficient maintenance scheduling.
Innovation Solution
The implementation of an automation device system that includes a processor to store input and output process images in a buffer, utilizing a machine learning model to predict faults, and transmitting this data to a storage device for analysis, enabling detailed fault analysis and preventative maintenance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional error code collection is used to detect faults in automation devices, then fault detection capability is provided, but the ability to diagnose root cause is insufficient leading to increased downtime
Solution Approach 1:
The system performs preliminary actions by continuously collecting and storing process image data, alarm data, and event data before faults occur. This historical data is prepared and organized in advance, enabling rapid root cause analysis when faults happen, thereby reducing downtime without compromising reliability
Solution Approach 2:
The patent introduces an intermediary data collection system that captures process images, alarms, and events as intermediate representations of system state. This intermediary data layer enables operators to analyze the sequence of events leading up to faults, providing deeper diagnostic capability while maintaining fast fault detection
2Difficulty of detecting and measuring
If detailed fault data collection is implemented to enable root cause analysis, then diagnostic capability is improved, but system complexity increases
Solution Approach 1:
The system applies universality by using a multi-functional data collection architecture that simultaneously captures process images, alarm data, and event data through a unified interface. This universal approach improves diagnostic capability across different fault types without proportionally increasing system complexity, as the same infrastructure serves multiple diagnostic needs
3Reliability
If conventional monitoring is used to detect faults, then basic fault detection is achieved, but preventative maintenance capability is lacking
Solution Approach 1:
The system performs preliminary action by continuously collecting and analyzing operational data, alarm patterns, and event sequences before critical failures occur. This preliminary data accumulation enables the identification of trends and anomalies that predict future faults, allowing preventative maintenance to be scheduled in advance rather than reacting to failures
Solution Approach 2:
The patent implements feedback mechanisms where collected data about system operations, alarms, and events is analyzed to provide insights about system health trends. This feedback loop enables the system to recommend preventative maintenance actions based on accumulated evidence, improving maintenance scheduling efficiency while maintaining reliable fault detection
Data Source
AI summary
Provided are systems, methods and devices for automation devices. The method includes providing an automation device for controlling at least one piece of automation equipment; receiving, at an input of the automation device, input data; storing, at an input process image, the input data; executing, at a processor of the automation device, an automation program to generate output data based on the input data; storing, at an output process image of the automation device, the output data; and storing, in a buffer, the input process image and the output process image.