Intelligent PLC Control Model for Runtime Diagnostics
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Solution Overview
Problem
Conventional programmable logic controllers (PLCs) lack the ability to capture and utilize knowledge about control program execution, making it difficult to diagnose errors, optimize operations, and ensure runtime intelligence, as control flow information is not captured for diagnostics and statistical analysis is not linked to symbolic code information.
Innovation Solution
The development of an Intelligent PLC that uses a control model to represent structural and flow information of control programs, including dependency and performance indicators, allowing for fault identification, debugging, and algorithm adjustment, which is automatically extracted from program code and used for monitoring and optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If conventional PLC programming is used with graphical or textual specification tools, then the control program can be implemented, but control flow information is not captured for runtime diagnostics and statistical analysis cannot be linked to symbolic code information
Solution Approach 1:
The control model is generated in advance during the programming phase, capturing control flow information, variable dependencies, and program structure before runtime. This preliminary action ensures that diagnostic information is already structured and ready for efficient runtime analysis without adding complexity during execution.
Solution Approach 2:
A symbolic control model is created as a representation copy of the actual control program. This model mirrors the program structure, variables, and control flow, enabling diagnostic analysis on the model without affecting the running program. The copy allows statistical information to be linked to symbolic code information for improved diagnostics.
2Loss of information
If a historian records the state of the process at runtime, then process data is stored, but there is no way to link statistical information about program execution to symbolic information representing the code
Solution Approach 1:
The control model serves as an intermediary between the historian's process data and the symbolic code information. It contains variable names, data types, and control flow structures that link runtime statistical information from the historian to the corresponding symbolic representations in the program code, enabling contextualized diagnostics without direct complex linking mechanisms.
3Reliability
If control flow information is captured for runtime diagnostics, then diagnostic capability is improved, but the system complexity increases
Solution Approach 1:
All control flow information, variable dependencies, and program structure are captured during the programming phase and stored in the control model. This preliminary action transfers the complexity of information capture to the programming phase rather than requiring complex runtime mechanisms, improving diagnostic capability while maintaining manageable system architecture.
Solution Approach 2:
A symbolic representation (control model) is created that mirrors the control program structure. This copy contains all necessary control flow information for diagnostics but is separate from the running program, allowing comprehensive diagnostic capability without increasing the complexity of the actual control system architecture.
Data Source
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AI summary
A method of operating an intelligent programmable logic controller includes executing, by a processor included in the intelligent programmable logic controller, a control program. A non-volatile storage medium included in the intelligent programmable logic controller stores a control model comprising control knowledge related to the control program. The control model is used to perform a monitoring operation related to execution of the control program on the intelligent programmable logic controller.