Imaging Modality Smart Symptom Maintenance System
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Solution Overview
Problem
Traditional remote diagnosis and repair methods for imaging systems and other machines are inefficient, requiring manual intervention and on-site visits, which are costly and time-consuming, and often disrupt machine usage, while also lacking in real-time problem-solving capabilities.
Innovation Solution
A system that uses AI models and digital twins to filter and identify symptoms, generate care packages, and dispatch resources, enabling remote diagnosis and repair by converting user-defined symptoms into machine issues, predicting solutions, and optimizing resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional remote diagnosis methods are used, then manual intervention and on-site visits can be performed, but the process is costly and time-consuming with disrupted machine usage
Solution Approach 1:
The imaging device automatically performs self-diagnosis by capturing operational data, error codes, and system logs, and transmits this information to the service center, eliminating the need for manual intervention and reducing machine downtime while maintaining diagnosis accuracy
Solution Approach 2:
The system performs preliminary data collection and analysis before technician arrival by automatically gathering operational parameters, error codes, and system logs, and pre-processing this data to identify potential issues, enabling faster on-site resolution and reducing overall downtime
2Ease of operation
If traditional remote diagnosis methods are used, then manual intervention can be performed, but response times are slow and resource allocation is inefficient
Solution Approach 1:
The system replaces manual diagnosis processes with automated electronic data transmission and AI-based analysis, where the imaging device automatically captures and transmits operational data, and the service center uses software to analyze error codes and generate diagnostic reports, significantly improving response time while maintaining ease of operation
Solution Approach 2:
The system implements automated feedback loops where operational data and error codes are continuously monitored and transmitted to the service center, which analyzes the data and provides real-time diagnostic feedback, enabling faster response times and more efficient resource allocation
3Measurement precision
If comprehensive issue analysis is performed, then accurate diagnosis can be achieved, but data processing complexity increases
Solution Approach 1:
The diagnostic system segments the comprehensive issue analysis into distinct modules: error code capture, operational data collection, symptom identification, and solution generation. Each module handles specific data types and processing tasks independently, reducing overall system complexity while maintaining high diagnostic accuracy through specialized processing at each stage
Data Source
AI summary
Methods, apparatus, systems and articles of manufacture providing an image modality smart symptom maintenance are disclosed. The example apparatus includes a system processor to identify a distinguishing symptom of a first subset of issues corresponding to an imaging device. The apparatus further includes an interface to transmit a prompt corresponding to an identification of the distinguishing symptom. The apparatus further includes a filter to filter out issues of the first subset of issues based on a response to the prompt to generate a second subset of issues. The apparatus further includes the system processor to transform the first subset of issues into a solution for servicing the imaging device by applying at least one of the symptom or the first subset of issues to an artificial intelligence model corresponding to the imaging device. The apparatus further includes a care package generator to generate a data structure based on the solution, the data structure including information to assist in the repair of the imaging device.