Abstract
Medical equipment always has the problem of various anomalies and failures in the working process. The emergence of the Internet of Things (IoT) technology can effectively avoid this problem, which can continuously monitor the real-time status of the target equipment and reflect the health status of medical equipment to give early warning of anomalies. In this paper, we obtain real-time status data of CT equipment from the IoT in West China Hospital and novel multivariate time series classification models are used to predict equipment operating anomalies. After the data was collected, data were preprocessed. We constructed two models for multivariate time series classification and compared them with other state-of-the-art models. The results show that our model has better performance on the dataset, with the Accuracy, Recall, and Precision reaching 0.75, 0.53 and 0.42 respectively.
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