Injection System Predictive Maintenance for Failure And Misuse Detection
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Solution Overview
Problem
Conventional injection systems lack a mechanism for predicting operation failures and misuses, leading to potential downtime, improper functioning, and delayed or improper medical procedures.
Innovation Solution
A computer-implemented method and system for predictive maintenance that receives operation data from injection systems, determines prediction scores for potential failures or misuses, and provides maintenance data to prevent such issues.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional injection systems operate without predictive maintenance mechanisms, then device complexity is reduced, but reliability deteriorates due to unexpected failures and downtime
Solution Approach 1:
The system performs preliminary maintenance actions by predicting potential failures before they occur. The predictive maintenance system analyzes operation data to identify patterns indicating future failures, allowing maintenance to be scheduled in advance rather than reacting to actual failures, thus improving reliability without requiring complex real-time intervention mechanisms
Solution Approach 2:
The system implements feedback loops where operation data is continuously collected, analyzed, and used to generate maintenance predictions. These predictions feed back into the maintenance scheduling process, creating a closed-loop system that progressively improves reliability through learned patterns from historical data while maintaining manageable complexity through automated decision-making
2Measurement precision
If operation data is continuously monitored and analyzed, then measurement precision of failure prediction improves, but use of energy increases due to continuous data processing
Solution Approach 1:
The system uses feedback from analyzed operation data to improve prediction accuracy over time. By continuously processing operation parameters and comparing actual outcomes with predictions, the system refines its models to achieve higher measurement precision. The energy consumption is justified by the increasing accuracy that reduces false alarms and improves maintenance timing
Solution Approach 2:
The system processes operation data at varying levels of intensity based on system state and risk assessment. Rather than continuously analyzing all parameters at maximum depth, the system applies partial analysis to normal operations and more intensive analysis only when failure patterns are detected, optimizing the balance between prediction precision and energy consumption
Data Source
AI summary
A method, system, and computer program product for predictive maintenance. A method may include receiving operation data associated with one or more injection systems, wherein the operation data includes one or more operation parameters associated with one or more operations of the one or more injection systems: determining one or more prediction scores for the one or more injection systems based on the operation data, wherein the one or more prediction scores include one or more predictions of one or more operation failures or misuses for the one or more injection systems; and providing maintenance data associated with the one or more operation failures or misuses, wherein the maintenance data is based on the one or more prediction scores.