Prognostic and Health Management of CT Equipment via a Distance Self-Attention Network Using Internet of Things

Anomalies or failures in medical equipment may lead to severe consequences. Data-driven prognostic and health management (PHM) approaches can improve maintenance efficiency and reduce maintenance costs at hospitals while protecting patients' lives. However, currently, the research and application of PHM in medical equipment is still rather limited. The development of the Internet of Things (IoT) technology provides new opportunities for PHM, which can safely collect, analyze, and store real-time equipment data in hospitals. The data-driven models used in PHM predict anomalies or failures. However, current data-driven models' performance may be limited due to lack of consideration for the interaction of similar features and the importance of different time steps. Hence, this article proposes a new deep-learning network called similar feature interaction (SFI) with distance self-attention (SA) for the PHM of medical equipment. First, an SFI module which uses clustering algorithms and causal convolution layers is proposed to consider the interaction of similar features. Second, a distance SA mechanism is proposed to allocate more attention to important time steps. The experiments on millions of computed tomography (CT) equipment operating status instants collected by IoT in the hospital and the public data set show that the proposed model is superior to existing models. The results show that the accuracy, recall, precision, and f1-score of the proposed model on the real CT log data achieve 0.865, 0.682, 0.469, and 0.556, respectively. The proposed PHM model can assist the equipment maintenance team of hospitals in decision making under the IoT framework.