AI Engine for Control Loop Template Reuse in Engineering Automation
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Solution Overview
Problem
Current engineering design processes for industrial control and design applications are time-consuming and resource-intensive, relying heavily on human skill and requiring significant effort to determine if new solutions can reuse existing artifacts, with a lack of scalability due to rule-driven approaches.
Innovation Solution
A knowledge-driven artificial intelligence engine automates engineering design by querying a trained knowledge base for templates to map new control loop data, utilizing past control loop data and templates from past projects, with machine learning for training and conflict resolution, to provide configuration data for new engineering projects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If a strictly rules driven approach is applied to automate engineering design, then automation extent is improved, but device complexity and scalability worsen due to the need to design rules for every new project and type of project
Solution Approach 1:
The system performs preliminary action by training the machine learning model on historical engineering data before actual design tasks. The model learns from past control loop data, P&IDs, and engineering artifacts to automatically apply lessons learned to new projects, eliminating the need to manually design rules for each new project type.
Solution Approach 2:
The system uses copying by leveraging historical engineering data, past control loop data, and existing engineering artifacts as training data for the machine learning model. The model copies patterns and relationships from historical data to automatically generate design solutions for new projects, reducing the need to create rules from scratch.
2Extent of automation
If a strictly rules driven approach is applied to automate engineering design, then automation extent is improved, but productivity worsens due to significant time resources required to design rules for every new project
Solution Approach 1:
The system performs preliminary action by pre-training the machine learning model on historical engineering data before actual design tasks. This preliminary training enables the model to automatically apply learned patterns to new projects without requiring time-consuming manual rule design for each project, significantly improving productivity.
Solution Approach 2:
The system implements feedback by continuously learning from historical engineering data and project outcomes. The machine learning model is trained on past control loop data, P&IDs, and engineering artifacts, allowing it to improve its design recommendations over time based on accumulated experience, thereby increasing productivity.
3Manufacturing precision
If manual engineering design methods are used, then manufacturing precision and reliability are improved through human skill, but loss of time and resources worsen due to significant effort required to understand process characteristics and determine artifact reuse
Solution Approach 1:
The system uses copying by leveraging historical engineering data, past control loop data, and existing engineering artifacts as training data. The machine learning model copies successful design patterns and relationships from historical data to automatically generate precise design solutions for new projects, maintaining design quality while reducing time and resources.
Solution Approach 2:
The system replaces the mechanical system of manual human analysis with an automated machine learning-based system. The ML model automatically analyzes process characteristics, matches control loops with templates, and determines artifact reuse opportunities, maintaining design precision while eliminating time-consuming manual efforts.
4Adaptability or versatility
If manual engineering design methods are used, then adaptability is improved through human skill and judgment, but productivity worsens due to significant time resources required for each design task
Solution Approach 1:
The system performs preliminary action by pre-training the machine learning model on diverse historical engineering data from multiple projects and domains. This preliminary training enables the model to adapt to different project types and requirements automatically, maintaining versatility while improving productivity through automated design tasks.
Solution Approach 2:
The system applies parameter changes by adjusting the machine learning model's parameters and training data based on different project requirements and domains. The model can adapt its behavior and decision-making based on the specific characteristics of each project, maintaining adaptability while automating design processes to improve productivity.
Data Source
AI summary
In a method of automating engineering design a knowledge base (KB) is queried for a template to map with new control loop (CL) data of a new CL that was identified in new digitized design data for a new engineering project, the query including the new CL data. The KB is trained to map past CL data of past CLs identified in past digitized design data from past engineering projects to respective templates based on past instantiation of the respective templates with the past CLs by the past engineering projects. The method further includes receiving a selected template in response to the query, wherein the selected template is selected based on its mapping with past CL data that matches the new CL data, and providing configuration data, including an instantiation of the selected template with the new CL data, for implementation of the new CL in an engineering system.