Alarming Server for Medical Imaging Failure Prediction
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Solution Overview
Problem
Conventional methods for failure detection in medical imaging systems are limited in predicting failures before they affect normal operation, leading to costly and resource-intensive maintenance.
Innovation Solution
A system and method that utilize an alarming server connected to a target device and user terminal, which generates a second failure detection result based on operation parameter values, enabling prediction and analysis of failures not detected by the device itself, and uses a federated learning algorithm to improve failure detection accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional failure detection methods are used by the target device itself, then the device can detect failures that affect normal operation, but it cannot predict failures before they occur, leading to costly maintenance
Solution Approach 1:
An alarming server is introduced as an intermediary between the target device and the user terminal. The server collects operation parameter values from the target device, analyzes them using preset rules or machine learning models to generate failure detection results, and sends warnings to the user terminal. This intermediary approach enables sophisticated failure prediction without increasing the complexity of the target device itself.
Solution Approach 2:
The system moves failure detection from a single-device perspective to a multi-dimensional analysis by collecting operation parameter values across different time points and comparing them against historical data, preset rules, or machine learning models. This dimensional expansion enables predictive capability by analyzing trends and patterns beyond immediate device state.
2Measurement precision
If the alarming server uses machine learning models for failure detection, then detection accuracy is improved, but the system complexity and computational resources increase
Solution Approach 1:
The system offers flexible implementation where preset rules can be used alone or in combination with machine learning models. The machine learning aspect is implemented partially through federated learning, where only model parameters are updated and transmitted, not the entire model architecture or raw data. This partial implementation achieves improved accuracy while controlling system complexity.
Solution Approach 2:
Federated learning is used to create virtual copies of the machine learning model across multiple devices. Each device trains a local copy of the model using its own data, and only model parameters (not raw data) are shared and aggregated. This copying approach enables sophisticated AI-based detection without centralizing sensitive data or requiring excessive computational resources at any single point.
3Measurement precision
If federated learning is used to train failure detection models, then model accuracy is improved through multiple devices, but communication overhead and coordination complexity increase
Solution Approach 1:
The base failure detection model is pre-trained and deployed to multiple devices before actual use. During federated learning, only incremental updates to model parameters are performed and exchanged, rather than training from scratch. This preliminary action significantly reduces the time and communication overhead required for continuous model improvement while maintaining accuracy benefits.
Data Source
AI summary
Systems and methods for failure warning are provided in the present disclosure. The methods may include obtaining, from the target device, log information including a first failure detection result and one or more first parameter values of one or more operation parameters of the target device. The first failure detection result may be generated by the target device and indicate whether the target device has a failure. The methods may further include determining, based on the one or more first parameter values, a second failure detection result regarding the target device. The methods may further include sending, to the user terminal, a failure analysis report regarding the target device based on the first failure detection result and the second failure detection result.