Decision Tree Control Logic for Real-Time Anomaly Updates

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Solution Overview

Problem

Decision trees used in industrial automation systems often output anomalous classifications, leading to inefficient retraining processes that consume resources and disrupt operations, as they are not designed to handle real-time modifications effectively.

Innovation Solution

Implement a system that receives operational parameters, identifies anomalous outputs, and generates updated decision trees and control logic, allowing for incremental modifications to the decision tree rather than full retraining, thereby improving efficiency and reducing resource usage.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If the decision tree is retrained when anomalous outputs occur, then the accuracy of the decision tree is improved, but the processing time and resource consumption increase significantly

Engineering Contradiction:

Improvedecision tree accuracyVSAvoidretraining time

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The patent segments the decision tree into multiple subtrees, allowing individual subtrees to be updated independently when anomalies are detected in specific regions, rather than retraining the entire decision tree. This segmentation enables localized modifications that reduce processing time and resource consumption while maintaining accuracy improvements.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

Instead of performing full retraining (excessive action) whenever an anomaly is detected, the system applies partial action by updating only the specific subtree or portion of the decision tree that produced the anomalous output. This partial update approach maintains sufficient accuracy while significantly reducing the time and computational resources required.

Inventive Principle:

Principle #16Partial or excessive action

2Reliability

If full retraining is performed to correct anomalous outputs, then the reliability of the decision tree is improved, but the system efficiency and productivity decrease

Engineering Contradiction:

Improvedecision tree reliabilityVSAvoidsystem efficiency

Core Design Contradiction:

ReliabilityVSProductivity

Solution Approach 1:

By dividing the decision tree into independent subtrees, the system can maintain high reliability through targeted updates of only the affected segments, preserving overall system productivity by avoiding full retraining cycles.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system dynamically determines the scope of updates based on anomaly detection results, adapting the retraining process to match the actual needs of the system. This dynamic approach maintains reliability by updating necessary portions while preserving productivity through minimized disruption to overall system operation.

Inventive Principle:

Principle #15Dynamics

3Adaptability or versatility

If the decision tree is modified in real-time to correct anomalies, then the adaptability of the system is improved, but the complexity of the control system increases

Engineering Contradiction:

Improvereal-time adaptation capabilityVSAvoiddecision tree modification complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

Segmenting the decision tree into independent subtrees simplifies real-time modification by isolating changes to specific segments, reducing the overall complexity of managing real-time adaptations while improving system adaptability.

Inventive Principle:

Principle #1Segmentation

4Loss of energy

If incremental modifications are made to the decision tree instead of full retraining, then the resource consumption is reduced, but the robustness of the decision tree may be compromised

Engineering Contradiction:

Improveprocessing power consumptionVSAvoiddecision tree robustness

Core Design Contradiction:

Loss of energyVSReliability

Solution Approach 1:

Segmentation allows incremental modifications to be applied to specific subtrees while maintaining the robustness of the overall decision tree structure, reducing energy consumption without compromising overall reliability.

Inventive Principle:

Principle #1Segmentation

Data Source

PatentUS11886152B2Automated monitoring and control using augmented streaming decision tree

Publication Date: 2024.01.30 ROCKWELL AUTOMATION TECH INC

AI summary

A non-transitory computer-readable medium comprising computer-executable instructions that, when executed, are configured to cause a processor to perform operations that include receiving operational parameters for one or more automation devices, wherein the one or more automation devices are configured to implement control logic generated based on a decision tree. The operations also include receiving an output by the decision tree based on the operational parameters. Further, the operations include determining the output is an anomalous output based on a constraint associated with the decision tree. Further still, the operations include generating an updated decision tree based on the anomalous output. Even further, the operations include generating updated control logic for the one or more automation devices based on the updated decision tree. Even further, the operations include sending the updated control logic to the one or more automation devices.