Cross-Device Failure Prediction Sensitivity in Medical Equipment
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Solution Overview
Problem
Existing device management systems struggle to detect and mitigate potential failure risks across multiple devices, especially those not equipped with failure prediction capabilities, making it difficult to identify and address similar risks early on.
Innovation Solution
A device management apparatus and method that adjusts the detection sensitivity of failure predictions in one medical device based on the relevance to another, using a server to forward adjustment information and enhance the detection sensitivity of devices that have not issued a failure prediction, thereby facilitating early identification of potential failure risks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a device only detects failure prediction for its own device, then the detection capability is simple and device complexity is low, but the ability to grasp potential risks across multiple devices is insufficient and reliability is reduced
Solution Approach 1:
A server acts as an intermediary between multiple medical devices. The server collects failure prediction information from devices that detect it, stores this information, and distributes relevant failure prediction data to other devices that may be affected. This mediator approach enables cross-device risk detection without requiring each device to independently analyze all potential risks, thus improving reliability while maintaining manageable complexity.
Solution Approach 2:
The server provides universal functionality by serving multiple devices simultaneously. It performs data collection, storage, analysis, and distribution functions that benefit all connected medical devices. This multi-functional approach allows the system to grasp potential failure risks across the entire network of devices rather than limiting detection to individual device boundaries.
2Measurement precision
If detection sensitivity is increased for all devices, then early detection of potential failures is improved, but false alarms increase and system complexity increases
Solution Approach 1:
The system applies different detection sensitivity levels to different devices based on their specific characteristics, failure types, and relevance relationships. Instead of uniformly high sensitivity across all devices, the server adjusts sensitivity locally for each device-relevance pair. This approach enables early detection where needed while avoiding unnecessary complexity and false alarms in other contexts.
Solution Approach 2:
Detection sensitivity is dynamically adjusted based on the relevance between devices and the specific failure prediction information. The server modifies sensitivity levels in real-time according to the importance and applicability of failure data, rather than maintaining fixed high sensitivity. This dynamic adjustment improves early detection capability while preventing system complexity from becoming unmanageable.
3Loss of information
If failure prediction information is shared across all devices, then the ability to identify similar risks is improved, but information processing complexity and communication overhead increase
Solution Approach 1:
The server extracts and stores only the essential failure prediction information from multiple devices, separating critical data from redundant details. It then distributes this extracted, processed information selectively to devices that need it based on relevance. This extraction approach improves information sharing efficiency while reducing the complexity of processing and communicating complete raw data sets across all devices.
Data Source
AI summary
According to one embodiment, a device management apparatus includes processing circuitry. The processing circuitry acquires information indicating occurrence of a first failure prediction of a first device. The processing circuitry adjusts detection sensitivity of a second failure prediction of a second device relating to the information based on relevance between the first device and the second device. The second device is different from the first device.