AI Maintenance System for Imaging Device Fleet Diagnostics
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Solution Overview
Problem
Traditional methods for maintaining and repairing fleets of machines, such as imaging systems and turbines, are inefficient due to manual processing of service requests, which leads to increased downtime and costs, as they often require on-site visits and manual intervention for diagnosis and repair.
Innovation Solution
A system that converts customer-defined symptoms into machine issues using AI models, generates targeted care packages for technicians, determines the best-suited technicians, and identifies machine fleets to utilize digital twins for virtual testing and error tracking, thereby reducing manual overhead and response time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual processing of service requests is used, then technicians can diagnose and repair machines, but downtime and costs increase
Solution Approach 1:
The system enables self-service through automated symptom-to-issue conversion using AI models. When a machine reports symptoms, the system automatically converts them into diagnostic issues without requiring immediate manual technician intervention, allowing the system to preliminarily diagnose and potentially self-correct certain problems
Solution Approach 2:
The system performs preliminary actions by automatically converting symptoms to issues and generating care packages before technicians arrive. This includes preparing diagnostic information, repair guidelines, and part requirements in advance, so technicians can immediately begin targeted repairs upon arrival
2Measurement precision
If on-site visits and manual intervention are used for diagnosis, then accurate repair can be achieved, but response time and costs increase
Solution Approach 1:
The system introduces an intermediary AI model that acts as a mediator between symptom reporting and technician diagnosis. This intermediary automatically converts customer-defined symptoms into machine-specific issues, bridging the gap between non-technical user reports and technical diagnostic requirements without delaying the process
Solution Approach 2:
The system implements feedback loops where symptom conversion results, diagnostic accuracy, and repair outcomes are continuously fed back to improve the AI models. This enables the system to learn from each service call and progressively improve diagnosis accuracy while maintaining rapid response times
3Ease of operation
If manual processing and on-site visits are required, then comprehensive service can be provided, but operational costs increase
Solution Approach 1:
The system segments the service process into distinct automated and manual components. Automated symptom conversion, issue identification, and care package generation handle routine diagnostic tasks, while technicians focus only on complex repairs requiring physical intervention. This segmentation reduces unnecessary on-site visits and associated costs
4Ease of repair
If traditional maintenance methods are used, then technicians can service machines, but efficiency decreases due to manual overhead
Solution Approach 1:
The system replaces manual mechanical processes with automated digital systems. AI models automatically convert symptoms to issues, generate diagnostic care packages, and provide repair guidance, eliminating manual overhead in symptom analysis and diagnostic preparation. Technicians receive structured, machine-specific instructions that streamline their repair processes
Data Source
AI summary
Methods, apparatus, systems and articles of manufacture to provide an image modality maintenance smart find are disclosed. The example method includes identifying an imaging device based on an image of the imaging device. The method further includes determining at least one of a make, a model, or a modality of the imaging device based on the identification. The method further includes identifying a fleet of imaging devices corresponding to the imaging device. The method further includes storing error information corresponding to an issue of the imaging device in correspondence with the fleet of imaging devices and update a model corresponding to the fleet based on the error information. The method further includes deploying the model to a device of a technician.