Edge AI PLC Emulation for Legacy Industrial Control Replacement
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Solution Overview
Problem
Industrial systems face challenges in maintaining and replacing programmable logic controllers (PLCs) due to degradation in harsh environments and the loss of technical expertise, leading to the need for a solution that can emulate PLC control logic without requiring specialized technicians or documentation.
Innovation Solution
An edge computing device is used to train an artificial intelligence model that emulates the control logic of a PLC, allowing it to receive inputs, generate outputs, and control industrial devices, thereby replacing the PLC and maintaining system functionality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If general-purpose computer devices are used to replace PLCs, then cost and flexibility are improved, but reliability in harsh environments deteriorates due to component degradation
Solution Approach 1:
The patent creates a digital copy of the PLC's control logic by training an AI model to replicate the input-output behavior of the original PLC. This allows the system to maintain the reliability characteristics of PLCs while using more flexible and cost-effective computing infrastructure for the control logic implementation.
Solution Approach 2:
The patent replaces the physical PLC hardware system with a software-based AI model running on edge computing devices. This substitution eliminates the need for specialized industrial PLC components while maintaining control functionality, thereby reducing costs and improving flexibility without sacrificing reliability through proper model training and validation.
2Ease of operation
If PLCs are replaced to reduce maintenance complexity, then ease of operation is improved, but loss of technical expertise and documentation creates new challenges
Solution Approach 1:
The AI model is trained to independently replicate the PLC's control logic without requiring human expertise in PLC programming or documentation. The system serves itself by learning the control patterns directly from operational data, eliminating dependency on specialized technicians and proprietary documentation while maintaining ease of operation.
Solution Approach 2:
The patent introduces an AI model as an intermediary between the control inputs and outputs that captures the essence of PLC control logic. This intermediary learns and preserves the control knowledge implicitly through training data, preventing loss of technical expertise while simplifying operation for end users who don't need to understand the underlying control logic.
3Adaptability or versatility
If AI models are trained to emulate PLC control logic, then adaptability and maintenance are improved, but device complexity increases due to training and deployment requirements
Solution Approach 1:
The patent creates a universal AI model that can emulate different PLC control logics by training on their respective operational data. This single multi-functional model replaces the need for multiple specialized PLC systems, improving adaptability while the standardized training and deployment process manages the inherent complexity through a unified approach.
Data Source
AI summary
An edge computing device is provided that includes a plurality of input electrodes that are communicatively coupled to one or more communication channels of a programmable logic controller that implements control logic to control a controlled device. The edge computing device furthers include a processor configured to, at a training time, receive signals via the plurality of input electrodes, detect inputs to the one or more communication channels and outputs from the one or more communication channels of the programmable logic controller, generate a set of training data based on the detected inputs and outputs of the programmable logic controller, and train an artificial intelligence model using the generated set of training data. The processor is further configured to, at a run-time, emulate the control logic of the programmable logic controller using the trained artificial intelligence model.