A Method for Identifying Abnormal Operating States of Medical Equipment Based on Machine Learning
The anomaly recognition of the device is completed by manually matching predefined keywords with the fields of the device response data,which can easily lead to inaccurate medical device recognition results in traditional recognition methods.Based on this,a medical device abnormal operating state recognition method based on machine learning was proposed in this study.With the collection of abnormal characteristic data such as voltage,current,and working temperature of medical equipment through sensors,the recognition method for abnormal operation status of medical equipment based on machine learning overlaid and fused multiple feature data,introduced GRU network to construct a machine learning recognition model,and trained the model with input data to complete the recognition of abnormal operating states of medical equipment.The experimental results indicated that the recognition method for abnormal operation status of medical equipment based on machine learning can identify abnormal operating states of medical equipment with different types,with a false alarm rate of 4.43％,a shorter recognition time for abnormal operating states and more accurate granularity.
万方数据
维普
