Cognitive Engineering Graphs for Faster Automation Program Validation

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Solution Overview

Problem

The complexity of modern automation systems in manufacturing and other industries requires high technical expertise, leading to difficulties in writing automation programs quickly and efficiently, exacerbated by staff rotation and increased demands for productivity, quality, and safety.

Innovation Solution

A cognitive engineering system that utilizes machine learning to analyze and represent knowledge through a cognitive engineering graph, allowing for pattern recognition, automatic program generation, and feedback to assist engineers in design and validation, while enabling undo actions and communication with physical automation systems.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If human experts manually write automation programs, then the quality and reliability of automation systems can be maintained, but the productivity and speed of engineering tasks are insufficient

Engineering Contradiction:

Improvespeed of writing automation programsVSAvoidquality of automation programs

Core Design Contradiction:

ProductivityVSReliability

Solution Approach 1:

The system enables automation programs to generate and validate their own cognitive engineering graphs automatically. The CEG generation module creates CEGs from automation program data without manual intervention, and the validation module automatically checks consistency against previously generated CEGs, allowing the system to serve itself in maintaining quality while improving productivity.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The cognitive engineering graph serves as an intermediary representation layer between the automation program and the validation process. Instead of directly validating complex automation programs, the system validates their CEG representations, which are simpler, standardized graphs that capture essential relationships and logic, thereby improving both speed and reliability.

Inventive Principle:

Principle #24Intermediary (Mediator)

2Reliability

If teams of human experts are used to maintain quality, then the reliability of automation systems improves, but the complexity and cost of the engineering process increases

Engineering Contradiction:

Improvequality of automation engineeringVSAvoidcomplexity of engineering process

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The system extracts the essential knowledge and relationships from complex automation programs into simplified cognitive engineering graphs. By separating the validation of structural consistency (performed automatically on CEGs) from the complex program logic, the system reduces engineering process complexity while maintaining reliability through automated validation.

Inventive Principle:

Principle #2Taking out (Extraction)

3Reliability

If manual validation of automation programs is performed, then the quality can be maintained, but the time required for engineering tasks increases

Engineering Contradiction:

Improvevalidation qualityVSAvoidtime for validation tasks

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The system replaces manual mechanical validation processes with automated computational validation. The validation module uses algorithmic comparison of cognitive engineering graphs against established patterns and previously generated CEGs to automatically detect inconsistencies, eliminating time-consuming manual review while maintaining validation quality.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

4Adaptability or versatility

If staff rotation occurs, then organizational flexibility improves, but the loss of domain expertise and increased error rate worsen

Engineering Contradiction:

Improveorganizational flexibilityVSAvoidconsistency of automation engineering

Core Design Contradiction:

Adaptability or versatilityVSReliability

Solution Approach 1:

The system creates standardized copies of knowledge representations through cognitive engineering graphs that capture domain expertise independently of individual engineers. When staff rotate, the organization retains this captured knowledge in the form of validated CEG patterns and previously generated graphs, ensuring consistency is maintained regardless of personnel changes.

Inventive Principle:

Principle #26Copying

Data Source

PatentUS20230004825A1Cognitive engineering graph

Publication Date: 2023.01.05 SIEMENS AG

AI summary

A method for representing knowledge in a cognitive engineering system (CES) includes receiving information relating to an automation engineering project from an engineering tool, storing the received information in a cognitive engineering graph (CEG) storing a plurality of previously generated CEGs for previous automation engineering projects, and establishing a communication path between the CEG storing the received information and the plurality of previously generated CEGs. The method may further include applying machine learning to the stored CEG based on the received information and the stored plurality of previously generated CEGs. The machine learning may analyze the CEG to identify at least one pattern that is representative of a given object from the automation engineering project. The CES may automatically add an element to the CEG based on the received information and a query from a user. Further, the user may request a change made by the CES be reversed.