Predictive Work Order Scheduling for Asset Dependency Maintenance
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Solution Overview
Problem
Existing industrial maintenance processes rely on manual work order creation, which is prone to errors and does not effectively address current or predicted risks to industrial assets, affecting asset performance and decision-making.
Innovation Solution
A work order management system that automates the scheduling of maintenance tasks by monitoring industrial asset data, using generative AI to analyze conditions indicative of risk and generate work orders based on predictive maintenance strategies, considering contextual information and asset dependencies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual work order creation is used, then simplicity of process is maintained, but error rate increases and predictive maintenance capability is lost
Solution Approach 1:
The system enables automated self-service maintenance scheduling by monitoring asset data and automatically generating work orders without manual intervention. The analysis component continuously evaluates asset conditions and triggers maintenance tasks autonomously when risk thresholds are exceeded, eliminating human error while maintaining systematic control.
Solution Approach 2:
The patent replaces manual mechanical processes of work order creation with an automated digital system. The analysis component uses computational algorithms to evaluate asset data and generate maintenance schedules, substituting human decision-making with machine-based analytical processes that reduce errors and improve precision.
2Reliability
If reactive maintenance is performed, then immediate response to failures is achieved, but asset performance and productivity are reduced
Solution Approach 1:
The system performs preliminary maintenance actions by predicting potential asset failures before they occur. The analysis component identifies risk conditions and schedules maintenance tasks in advance, allowing maintenance to be performed during planned downtime rather than causing unexpected停产, thereby improving asset performance and reducing unplanned downtime.
Solution Approach 2:
The system implements continuous feedback loops by monitoring asset data in real-time and adjusting maintenance schedules based on actual asset conditions. This feedback mechanism enables dynamic optimization of maintenance timing, ensuring that maintenance is performed when most beneficial for asset performance while minimizing disruption to production.
3Reliability
If maintenance tasks are scheduled without considering asset dependencies, then scheduling simplicity is maintained, but overall system reliability is reduced
Solution Approach 1:
The analysis component performs multiple functions simultaneously: it monitors individual asset conditions, identifies functional dependencies between assets, predicts failure risks, and coordinates maintenance schedules across the entire system. This multi-functional approach achieves comprehensive system reliability management without proportionally increasing scheduling complexity.
Solution Approach 2:
The system segments the complex maintenance scheduling problem into manageable components: individual asset monitoring, dependency identification, risk assessment, and coordinated scheduling. By breaking down the overall system into discrete analyzable units while maintaining their interrelationships, the system achieves high reliability without overwhelming scheduling complexity.
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 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.