Temporal trends and reliability of computed tomography scanner failures: evidence from a real-world time-series analysis

Objective We aimed to characterize temporal and component-specific patterns of CT malfunctions and identifying optimal monitoring intervals are critical to strengthening preventive maintenance and informed equipment management. Methods This study collected failure data from three CT scanners (uCT790, uCT860, uCT960+) at a tertiary hospital, collecting from installation through December 2024. Time-series analysis was used to characterized annual and monthly failure counts, and Pie charts visualized component-level contributions. Reliability performance was quantified using mean time between failures (MTBF), mean time to repair (MTTR), mean time between maintenance (MTBM), mean repair time (MTR), mean maintenance time (MMT), and mean logistics delay time (MLDT). A multi-granularity time-series Poisson-Prophet model (monthly, bi-weekly, weekly) was performed to evaluate predictive accuracy using mean absolute error (MAE). Results From 2019 to 2024, failure trajectories differed across CT scanners. The uCT790 showed a rise to five failures in 2023 followed by a decline to two in 2024. The uCT860 showed a steady increase, reaching six failures in 2024. The uCT960+ increased annually to seven failures in 2024. Failures were concentrated in the scanning table, power supply, and X-ray tube. Reliability analysis showed that the uCT790 achieved the highest MTBF (8554.03 h), the uCT860 the shortest MTTR (6.60 h), and the uCT960+ the lowest MLDT (3.47 h). Across forecasting granularities, predictive accuracy improved with finer granularity: monthly MAE 0.33–1.50, biweekly 0–0.33, and weekly 0–0.17. Conclusion Through integrated reliability assessment and time-series forecasting, this study delineated the distinct failure patterns of three CT scanners and identified bi-weekly forecasting as the most cost-effective temporal resolution, balancing predictive accuracy with resource efficiency.