Prognosis for Filament Degradation of X-Ray Tubes Based on IoMT Time Series Data

Abstract

The X-ray tube is the core component of computed tomography (CT) equipment, directly affecting imaging resolution and diagnostic accuracy. Degradation and failure prediction ensures the safe and reliable operation of X-ray tube. This article proposes a filament degradation prediction method for X-ray tubes based on Internet of Medical Things (IoMT) time-series data. First, this article analyzes the degradation mechanism of the filament and construct a health indicator based on filament current. Subsequently, key setting parameters are fixed to filter the original data, obtaining pure degradation information. Then, a multiscale attention prediction (MSAP) model is constructed to learn the filament degradation process from historical filament current data, and an ensemble epistemic uncertainty capture method is proposed to ascertain the uncertainty of prediction results. Finally, a failure threshold determining method is designed to predict the remaining useful life of the tube. Supported by the IoMT platform of West China Hospital, clinical monitoring data from four X-ray tubes that failed due to filament burnout were collected. Experimental results demonstrate that the proposed method outperforms existing state-of-the-art methods, achieving root mean square error and score values of 0.0249 and 0.0016, respectively. The proposed maintenance strategy is anticipated to yield economic benefits of 126 000–31 500 yuan per X-ray tube, significantly reducing downtime, and ensuring timely treatment for patients.