Imaging Modality Maintenance Care Package System
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Solution Overview
Problem
Traditional methods for maintaining and repairing imaging systems and other large machines are inefficient, requiring on-site visits and manual processing of service requests, which are time-consuming and costly, and often interrupt machine usage.
Innovation Solution
A system that converts customer-defined symptoms into machine issues, generates customized care packages with relevant information for technicians, determines the best-suited technicians, and uses digital twins and AI models to predict solutions and optimize maintenance processes, reducing the need for on-site visits and improving response times.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional on-site service methods are used, then technicians can directly service the machine, but machine downtime increases and service cost increases
Solution Approach 1:
The system performs preliminary actions by automatically generating customized care packages with all necessary information, parts, and tools before the technician arrives. The prediction model pre-identifies the solution and required resources based on error codes and machine data, so when the technician arrives, they can immediately execute the pre-planned service steps without waiting for diagnostics or part retrieval, thus minimizing machine downtime while maintaining service quality
Solution Approach 2:
The system introduces an intermediary automated care package generation system between the machine failure and the technician intervention. This intermediary system processes error codes, retrieves relevant information from databases, predicts solutions, and compiles customized care packages that guide the technician through the service process, reducing the need for on-site diagnostics and enabling faster, more reliable service
2Loss of information
If traditional manual service request processing is used, then comprehensive service information can be collected, but processing time increases
Solution Approach 1:
The system enables self-service by automatically processing service requests through machine learning models that extract error codes, retrieve relevant information from databases, predict solutions, and generate customized care packages without manual intervention. The system serves itself by autonomously completing the entire service request processing workflow, maintaining information completeness through automated data retrieval while dramatically increasing processing speed
Solution Approach 2:
The system replaces the mechanical manual processing system with an automated digital system. Instead of technicians manually collecting and processing service information, the system uses machine learning models and database queries to automatically extract, analyze, and process service request data, substituting human manual labor with automated computational processes that are both faster and equally comprehensive
3Ease of manufacture
If generic service packages are provided, then preparation is simplified, but service accuracy decreases
Solution Approach 1:
The system applies local quality by customizing each care package specifically for the particular machine instance and error condition. Instead of using uniform generic packages, the system tailors each care package's contents, instructions, and parts list to the specific error code, machine type, and service history, ensuring high service accuracy while maintaining ease of preparation through automated generation processes
4Measurement precision
If AI prediction models are implemented, then service accuracy improves, but system complexity increases
Solution Approach 1:
The system uses copying by creating a digital twin or virtual representation of the physical machine. The AI prediction model operates on this digital copy, analyzing error codes and machine data in the virtual space to predict solutions, which are then applied to the physical machine. This copying approach enables accurate AI-based predictions while managing system complexity by isolating the complex AI processing in a separate digital layer
Data Source
AI summary
Methods, apparatus, systems and articles of manufacture to provide an image modality maintenance care package are disclosed. An example apparatus includes a solution predictor to predict a solution for servicing an imaging device based on the at least one of an error code or an identified issue and information related to previous solutions corresponding to the at least one of the error code or the identified issue. The apparatus further includes a care package generator to generate a customized data structure corresponding to the solution, the customized data structure including automated solutions to service the imaging device. The apparatus further includes an interface to transmit, using a wireless communication, the customized data structure to at least one of the imaging device or a repair device connected to the imaging device and in response to the execution of the automated solutions by the imaging device, obtaining a response corresponding to the servicing of the imaging device using the repair device. The apparatus further includes an information updater to update at least one of the accessed information, the predicted solution, the relevant information, or a digital twin of the imaging device to update subsequent customized care package generation based on the response.