MRI Module Failure Prediction Using Sensor-Based Monitoring
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Solution Overview
Problem
Magnetic resonance apparatuses are prone to disruptions due to defects in components like flexible magnetic resonance coils and cables, which can be caused by mechanical and thermal stresses, leading to operational inefficiencies.
Innovation Solution
A module equipped with sensors to detect module parameters such as flexing and temperature, combined with an evaluation unit and a trained function, predicts potential failures by analyzing these parameters to initiate timely maintenance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If flexible magnetic resonance coils and cables are used to follow body contours and withstand mechanical stresses, then adaptability and ease of operation are improved, but reliability deteriorates due to increased risk of defects
Solution Approach 1:
The patent implements predictive analytics that continuously monitor module parameters (temperature, usage patterns, mechanical stress) and analyze them using trained functions to identify potential failures before they occur. This preliminary detection enables proactive maintenance scheduling, replacing modules before actual failure happens, thus resolving the contradiction between using flexible/adaptable components and maintaining system reliability.
2Reliability
If modules are monitored continuously to predict failures, then reliability is improved, but device complexity increases due to additional sensors and evaluation units
Solution Approach 1:
The patent integrates multiple monitoring functions (temperature sensing, usage pattern analysis, mechanical stress detection) into a unified predictive analytics system. The evaluation unit processes diverse module parameters through trained functions to generate comprehensive failure predictions, enabling one system to perform multiple monitoring and analysis tasks, thus improving reliability while controlling complexity through functional integration.
3Reliability
If modules are replaced proactively based on predictions, then reliability is improved, but loss of time increases due to unplanned maintenance interruptions
Solution Approach 1:
The predictive analytics system generates early warnings of potential module failures, enabling maintenance teams to schedule replacements during planned downtime rather than responding to unexpected failures. This advance notice allows for proactive scheduling of maintenance activities, optimizing resource allocation and minimizing disruption to operational schedules, thus resolving the contradiction between maintaining high reliability and reducing unplanned downtime.
Data Source
AI summary
A module for use in a magnetic resonance apparatus, a system, and a method for predicting a potential failure of a module are provided. The module includes at least one sensor configured to detect values of at least one module parameter of the module. The module parameter, such as detected values thereof, is suitable for predicting a potential failure of the module on the basis thereof.