Multivariate log file analysis for multi-leaf collimator failure prediction in radiotherapy delivery

Abstract

Background and Purpose: Motor failure in multi-leaf collimators (MLC) is a common reason for unscheduled accelerator maintenance, disrupting the workflow of a radiotherapy treatment centre. Predicting MLC replacement needs ahead of time would allow for proactive maintenance scheduling, reducing the impact MLC replacement has on treatment workflow. We propose a multivariate approach to analysis of trajectory log data, which can be used to predict upcoming MLC replacement needs. Materials and methods: Trajectory log files from two accelerators, spanning six and seven months respectively, have been collected and analysed. The average error in each of the parameters for each log file was calculated and used for further analysis. A performance index (PI) was generated by applying moving window principal component analysis to the prepared data. Drops in the PI were thought to indicate an upcoming MLC replacement requirement; therefore, PI was tracked with exponentially weighted moving average (EWMA) control charts complete with a lower control limit. Results: The best compromise of fault detection and minimising false alarm rate was achieved using a weighting parameter (λ) of 0.05 and a control limit based on three standard deviations and an 80 data point window. The approach identified eight out of thirteen logged MLC replacements, one to three working days in advance whilst, on average, raising a false alarm, on average, 1.1 times a month. Conclusions: This approach to analysing trajectory log data has been shown to enable prediction of certain upcoming MLC failures, albeit at a cost of false alarms.