Injection System Predictive Maintenance Using Operation Data Feedback
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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 compromised 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 users and technicians for proactive maintenance, including scheduling services, providing correct components, and ensuring regulatory compliance.
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 actions by continuously monitoring operation data and calculating prediction scores to identify potential failures before they occur. This allows maintenance to be scheduled in advance, preventing unexpected downtime and improving reliability without requiring complex real-time intervention systems.
Solution Approach 2:
The system implements feedback by continuously collecting operation data from the injection system, analyzing it through machine learning models, and providing prediction scores that feed back into maintenance scheduling. This closed-loop feedback mechanism improves reliability by enabling proactive maintenance based on actual system condition data.
2Reliability
If predictive maintenance is implemented to reduce downtime, then reliability improves, but loss of time increases due to data collection and analysis processes
Solution Approach 1:
The system performs preliminary analysis of operation data continuously in the background, calculating prediction scores before failures occur. This allows maintenance to be scheduled optimally without requiring time-consuming analysis at the moment of failure, thus improving availability while managing processing time efficiently.
Solution Approach 2:
The system performs self-diagnosis by automatically monitoring its own operation data and generating prediction scores for potential failures. This self-service capability reduces the need for external intervention and minimizes downtime without requiring extensive manual analysis time.
3Reliability
If operation data is continuously monitored to predict failures, then reliability improves, but use of energy increases due to data processing requirements
Solution Approach 1:
The system applies partial monitoring by focusing data collection and analysis on critical parameters and components most likely to fail. This selective approach maintains high predictive accuracy for reliability-critical issues while reducing overall energy consumption compared to comprehensive continuous monitoring of all system parameters.
Solution Approach 2:
The system replaces complex mechanical monitoring mechanisms with software-based analysis of existing operation data. By using machine learning models to analyze data already collected during normal operation, the system achieves high predictive accuracy without requiring additional energy-intensive sensing and processing hardware.
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.