AI Maintenance Scheduling for Automated Asset Risk Work Orders

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Solution Overview

Problem

Existing maintenance management systems in industrial facilities rely on manual creation of work orders, which are prone to errors and do not effectively address current or predicted risks to industrial assets, leading to potential operational issues and inefficiencies.

Innovation Solution

A work order management system that utilizes monitoring, analysis, and generative AI to automatically generate maintenance tasks based on real-time and historical asset data, considering interdependencies and contextual information to mitigate risks.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Manufacturing precision

If manual creation of work orders is used, then system complexity is reduced, but error rate increases and maintenance efficiency deteriorates

Engineering Contradiction:

Improvework order accuracyVSAvoidsystem complexity

Core Design Contradiction:

Manufacturing precisionVSDevice complexity

Solution Approach 1:

The system enables self-service automated work order generation by monitoring industrial asset data and automatically creating maintenance work orders when risk conditions are detected, eliminating manual creation processes and reducing human error while maintaining manageable system complexity through rule-based automation

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent replaces the manual mechanical process of work order creation with an automated electronic system that monitors asset data, analyzes risk conditions, and generates work orders through software algorithms, thereby improving accuracy while the modular architecture keeps system complexity controlled

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Extent of automation

If manual work order creation is used, then automation level is reduced, but response time to asset risks increases

Engineering Contradiction:

Improvework order automation levelVSAvoidresponse time to asset risks

Core Design Contradiction:

Extent of automationVSLoss of time

Solution Approach 1:

The system performs preliminary action by continuously monitoring industrial asset data and pre-identifying risk conditions before they manifest as actual failures, automatically generating work orders in advance to mitigate potential issues, thereby achieving both high automation and timely response

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent implements feedback mechanisms where the system continuously monitors asset performance data, compares it against risk thresholds, and automatically triggers work order generation when conditions indicate potential failures, creating a closed-loop system that responds rapidly to asset risks with high automation

Inventive Principle:

Principle #23Feedback

3Adaptability or versatility

If traditional maintenance scheduling is used, then interdependencies between assets are ignored, but comprehensive risk mitigation is reduced

Engineering Contradiction:

Improverisk mitigation comprehensivenessVSAvoidmaintenance scheduling complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The system applies universality by using a single integrated platform that simultaneously monitors multiple assets, analyzes interdependencies, and generates coordinated work orders across the industrial facility, achieving comprehensive risk mitigation while managing complexity through unified architecture

Inventive Principle:

Principle #6Universality (Multi-functionality)

Solution Approach 2:

The patent segments the maintenance scheduling system into modular components that individually assess each asset's risk profile and interdependencies, then integrate these assessments to generate coordinated work orders, allowing comprehensive analysis to be achieved through manageable modular processing

Inventive Principle:

Principle #1Segmentation

4Measurement precision

If manual error checking is used, then automation level is low, but error detection capability is insufficient

Engineering Contradiction:

Improveerror detection capabilityVSAvoiderror checking automation

Core Design Contradiction:

Measurement precisionVSExtent of automation

Solution Approach 1:

The patent replaces manual error checking with automated electronic validation systems that verify work order data accuracy, validate risk assessment logic, and ensure data integrity through software-based checking mechanisms, thereby achieving both high error detection capability and full automation

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Data Source

PatentUS20260030568A1Maintenance scheduling and work order generation

Publication Date: 2026.01.29 ROCKWELL AUTOMATION TECH INC

AI summary

A work order management system automates the process of scheduling maintenance tasks and generating corresponding work orders via analysis of monitored data generated by the industrial assets. The work order management system can monitor control, status, or operational data from industrial devices on the plant floor, and initiate creation of work orders based on a determination that the monitored industrial data indicates a current or predicted performance risk requiring investigation or maintenance. The system can leverage generative artificial intelligence (AI) or other types of AI in connection with determining when and how to schedule a maintenance task intended to mitigate asset risk. The system can also factor contextual information when determining whether to create and schedule a work order, such as the cost of operator or maintenance time, scheduled plant downtimes, environmental factors (e.g., humidity), time of year, supplier issues, and other considerations.