AI Maintenance Work Orders With Robot-Assisted Asset Tracking
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Solution Overview
Problem
Existing industrial maintenance processes rely heavily on manual work order creation, which is prone to errors and can adversely affect industrial assets due to improper data entry, leading to potential risks and inefficiencies.
Innovation Solution
A work order management system that automates the scheduling of maintenance tasks by analyzing real-time and historical industrial asset data, using generative AI to detect risks and generate work orders, and integrates with mobile robots for asset identification and troubleshooting, enabling robot-assisted tasks such as part retrieval and inventory tracking.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual work order creation is used, then flexibility in maintenance scheduling is maintained, but error rate increases and reliability decreases
Solution Approach 1:
The system enables self-service by automatically generating work orders through AI analysis of asset data, eliminating the need for manual data entry while maintaining operational flexibility. The automated system serves itself by detecting risks and creating maintenance tasks without human intervention.
Solution Approach 2:
The patent replaces the mechanical process of manual data entry and work order creation with an automated AI-based system that analyzes asset data, detects risks, and generates work orders automatically, thereby improving accuracy while reducing human effort.
2Reliability
If automated work order generation is implemented, then error rate decreases and reliability improves, but system complexity increases
Solution Approach 1:
The system introduces an AI-based analysis component as an intermediary between asset data collection and work order generation. This intermediary automatically processes data, detects risks, and formulates maintenance tasks, simplifying the overall system architecture while improving reliability.
Solution Approach 2:
The system implements feedback mechanisms where asset data is continuously monitored, analyzed for risk conditions, and used to generate work orders. The automated system learns from historical data and adjusts its risk detection and work order generation based on feedback from maintenance outcomes.
3Measurement precision
If real-time data analysis is performed, then risk detection capability improves, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary actions by pre-defining risk conditions and thresholds based on historical data and asset specifications. When real-time data is collected, the system quickly compares it against pre-established criteria, enabling fast risk detection without extensive real-time computation.
Solution Approach 2:
The patent segments the data analysis process into distinct components: data collection, risk condition evaluation, and work order generation. This segmentation allows the system to process only relevant data portions for each asset, reducing overall processing time while maintaining detection accuracy.
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, as well as mobile industrial robots that traverse 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.