AI Work Order Prioritization for Asset Risk-Based Maintenance
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Solution Overview
Problem
Manual work order generation in industrial maintenance is prone to errors and does not consider relevant contextual factors, leading to inefficiencies and risks in maintenance scheduling.
Innovation Solution
A work order management system that utilizes generative AI to analyze real-time and historical asset data, automatically generate maintenance tasks, and dynamically adjust schedules based on availability of resources and asset health, thereby optimizing maintenance strategies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual work order generation is used, then ease of operation is maintained, but accuracy and consideration of contextual factors deteriorate
Solution Approach 1:
The system performs self-service by automatically generating work orders and adjusting schedules based on inspection results and asset data, eliminating the need for manual intervention while maintaining high accuracy in maintenance scheduling decisions
Solution Approach 2:
The patent replaces manual mechanical processes with an automated computer-based system that uses algorithms to analyze inspection data and generate maintenance schedules, improving accuracy while managing complexity through systematic automation
2Productivity
If manual work order generation is used, then device complexity is low, but productivity and efficiency deteriorate
Solution Approach 1:
The system performs preliminary actions by pre-analyzing asset data and inspection results before generating work orders, enabling efficient maintenance scheduling that considers contextual factors and optimizes resource allocation in advance
Solution Approach 2:
The patent implements continuous monitoring and analysis of asset performance data, allowing the system to continuously adjust maintenance schedules for optimal efficiency without interruption, thereby improving productivity through sustained automated operation
3Reliability
If manual work order generation is used, then ease of operation is maintained, but reliability and risk mitigation deteriorate
Solution Approach 1:
The system incorporates feedback mechanisms by continuously monitoring inspection results and asset performance data, using this information to adjust work order schedules and ensure reliable maintenance timing that mitigates risks associated with manual scheduling errors
Solution Approach 2:
The patent applies beforehand cushioning by pre-calculating maintenance schedules that account for potential delays and risks, creating buffer time and contingency plans in advance to ensure reliable execution of maintenance tasks
Data Source
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 status and 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 dynamically updates the work orders in response to changing contextual conditions within the plant to ensure that work order priority, scheduling, and resource allocations satisfy a defined maintenance optimization criterion.