AI Plant Control Using Images and Acoustic Data
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Solution Overview
Problem
Primary industry plants, such as steel and metal-producing facilities, face challenges in achieving fully automated operation due to the need for human intervention during faults and deviations from regular operation, and there is a lack of documented expertise, with intuitive knowledge not being effectively captured by existing artificial intelligence systems.
Innovation Solution
Implementing a method that utilizes dimensional data, such as images and acoustic vibrations, processed by artificial intelligence, including neural networks, to ascertain evaluation results and control commands, which reduces the need for human intervention by automating control decisions and storing knowledge for future use.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If comprehensive artificial intelligence is implemented to process dimensional data and automate control decisions, then the extent of automation and productivity are improved, but the device complexity increases
Solution Approach 1:
The AI system is segmented into multiple specialized modules: dimensional data processing module, pattern recognition module, control command generation module, and knowledge storage module. Each module handles specific aspects of the automation task, reducing overall system complexity while maintaining high automation capability.
Solution Approach 2:
A knowledge base acts as an intermediary between the AI processing system and the control execution system. It stores documented expertise and control rules, mediating between complex AI analysis and simplified control actions, thereby reducing the complexity burden on the real-time control system.
2Manufacturing precision
If dimensional data such as images and acoustic vibrations are processed by AI, then measurement precision and manufacturing precision are improved, but the loss of time for data processing increases
Solution Approach 1:
The system performs preliminary processing of dimensional data by pre-defining patterns, thresholds, and evaluation criteria in the knowledge base. During real-time operation, the AI system only needs to compare incoming data against these pre-established standards, significantly reducing processing time while maintaining high precision in quality assessment.
3Reliability
If expert knowledge is documented and stored in the AI system, then reliability is improved through consistent decision-making, but the device complexity increases due to knowledge management requirements
Solution Approach 1:
Expert knowledge is copied from human operators into structured formats within the AI system's knowledge base. This includes copying control rules, decision logic, and operational procedures into machine-readable formats, enabling consistent reproduction of expert decisions without requiring the physical presence of experts, thereby improving reliability while managing complexity through standardization.
4Manufacturing precision
If the AI system accepts multiple data sources including measurement data, control data, and planning data, then manufacturing precision is improved through comprehensive analysis, but the device complexity and data processing requirements increase
Solution Approach 1:
The AI system is designed with universal data processing capabilities that can handle multiple data types (dimensional data, measurement data, control data, planning data) through a unified architecture. The same core AI engine processes all data types using consistent methods, reducing the need for separate specialized systems and thereby managing complexity while enabling comprehensive analysis for high manufacturing precision.
Data Source
AI summary
An automation system (1) determines control data (S′), outputs same to controlled elements (5) of the facility (ANL) and thereby controls the facility (ANL). Sensor devices (2) acquire measurement data (M) of the facility (ANL) and at least partly feed same to the automation system (1) and a man-machine interface (3). Said man-machine interface (3) receives planning data (P) from a production planning system (11) and/or control data (S′) and/or internal data (I) from the automation system (1). The interface outputs the data (M, S′, I) to a person (4). It furthermore receives control commands (S) from the person (4) and forwards them to the automation system (1). The automation system (1) processes the measurement data (M) and the control commands (S) when determining the control data (S′). An artificial intelligence unit (9) receives at least part of the measurement data (M), control data (S′) and/or internal data (I) and the data output to the person (4). It also receives the control commands (S). The artificial intelligence unit (9) processes the data (M, S′, I) and control demands (S) received and determines evaluation results (A) therefrom and makes the latter available to the person (4) and/or to the production planning system (11) and/or sets them for the automation system (1) in the form of control commands (5″) directly or via the man-machine interface (3). The data (M, S′, I) received by the artificial intelligence unit (9) are at least to some extent dimensional data. Said dimensional data (M, S′, I) comprise at least one image captured by a sensor device (2) or an image output via the man-machine interface (3), part of such an image, a time sequence of such images or a time sequence of a part of such images or an acoustic oscillation or an acoustic oscillation spectrum.