Descriptive Time Series Analysis for Downtime Prediction Using the Maintenance Data of a Medical Linear Accelerator

Abstract

A medical linear accelerator (LINAC) delivers high-energy X-rays or electrons to the patient’s tumor. In this study, we categorized failures and predicted downtime leading to discontinuous radiation treatment using a descriptive time series analysis of a 20-year maintenance dataset of a medical LINAC. A LINAC dataset of failure records for 359 instances was collected from 2001 to 2021. Next, we performed institution-specific seasonal autoregressive integrated moving average (ARIMA) modeling to analyze the causes of the failure categories and predict the downtime. Furthermore, we evaluated the performance of the predictive model using standard error metrics and statistical methods. Our results show that the downtime will increase by 95 h/year after 2022 and 100 h/year after 2023. The accumulated downtime in 2029 is predicted to be a maximum of 2820 h. The modeled seasonal ARIMA showed statistical significance (p σ2 (328.33 ± 9.4). In addition, the forecasting performance of the model was assessed using the mean absolute percentage error (MAPE). The failure parts where the major downtime occurred were the multileaf collimator (25.2%), gantry and couch motion part (15.4%), dosimetric part (11.7%), and computer console (10.0%). Using the development of the ARIMA model specific to our institution, the downtime is predicted to reach up to 2820 h.

Keywords

• LINAC
• time series analysis
• ARIMA
• quality assurance
• descriptive prediction