Plant Monitoring via Time-Stamped Intervention Learning
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Solution Overview
Problem
The complexity of evaluating and managing industrial plant systems is high due to the difficulty in accounting for unknown disturbance variables, system changes, and operator variability, which affects process quality and efficiency.
Innovation Solution
A method that involves acquiring and storing data from sensor and actuator units, providing time stamps, and analyzing operating telegrams to create an intervention system that automates responses to plant states, using AI algorithms to learn and adapt interventions, and optimize control variables independently of operator experience.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If deterministic process models and control algorithms are used for plant management, then process control can be achieved, but the system complexity increases and cannot account for unknown disturbance variables and system changes
Solution Approach 1:
The system automatically monitors its own operation by capturing process data, control variable changes, and operator interventions, then uses this self-collected data to autonomously generate and refine process models without requiring external system analysis
Solution Approach 2:
The system continuously feeds back captured intervention patterns and process data into the process model, automatically refining and adapting the model based on actual plant behavior and unknown disturbance variables
2Manufacturing precision
If system analysis is performed to model the plant, then process management quality improves, but the difficulty and time required increases significantly
Solution Approach 1:
The system performs preliminary data collection and pattern recognition by continuously capturing process data and control variable changes during normal operation, preparing the foundation for automatic model generation before it is actually needed
Solution Approach 2:
Instead of performing complex system analysis, the system creates a dynamic copy of the plant's actual behavior by recording and analyzing real process data, control variable changes, and operator interventions to reconstruct process models
3Adaptability or versatility
If operator experience is relied upon for process management, then adaptability to changes is maintained, but consistency and quality depend on individual availability and condition
Solution Approach 1:
The system captures and analyzes operator interventions automatically, learning from actual operator decisions and patterns to autonomously generate control strategies, eliminating dependence on individual operator availability and condition
Solution Approach 2:
The system replaces the mechanical aspect of operator experience with an automated information processing system that captures, analyzes, and reproduces expert decision-making patterns through data-driven models
Data Source
AI summary
The present disclosure comprises a method for managing a plant of the automation technology, including a step of acquiring and storing all data transmitted via a communication network, consisting of process values, control variables and status data, and operating telegrams by a field detection unit, and providing the data and the operating telegrams with time stamps. The method also includes steps of temporally assigning the operating telegrams to the data, and analyzing the assignment and creating an intervention system. The intervention system contains plant states and interventions, wherein a plant state is created on the basis of transmitted data from a sensor unit and from an actuator unit, and wherein an intervention consists of at least one operating telegram following the transmitted data from the sensor unit and from the actuator unit.