Research on Automatic Fault Detection of Magnetic Resonance Medical Equipment Based on Feature Fusion
The fault detection of magnetic resonance medical equipment usually needs to be carried out after the equipment has been running for a period of time,which may not be able to detect and handle some potential faults in a timely manner,resulting in the number of detected faults not matching the actual number.Therefore,a research method for automatic fault detection of magnetic resonance medical equipment based on feature fusion is proposed.Firstly,conduct a thorough analysis of the fault characteristics of magnetic resonance medical equipment circuits,and extract feature vectors related to the fault by collecting operational data of the equipment.Secondly,utilizing feature fusion technology to integrate these feature vectors and form a comprehensive feature vector.Finally,by effectively integrating multiple feature information,the accuracy of state judgment can be improved,comprehensively reflecting the equipment status.To further optimize the effectiveness of feature fusion,a feature weighting strategy is introduced,which assigns different weights to each feature,making fault detection more focused on key features while suppressing the influence of irrelevant or redundant features,in order to achieve magnetic resonance medical equipment fault detection.The results show that compared with threshold based fault detection methods and probability statistics based fault detection methods,the fault detection results obtained by the feature fusion based magnetic resonance medical equipment fault automatic detection method are more consistent with the actual detection results,with high detection accuracy and good practicality.
feature fusionmagnetic resonance medical equipmentequipment fault detectionautomatic detection
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