Wojtasik Arkadiusz Mariusz, Bolt Matthew, Clark Catharine H, Nisbet Andrew, Chen Tao
Department of Chemical and Process Engineering, University of Surrey, Guildford, UK.
Radiotherapy Physics, University College London Hospitals, London, UK.
Phys Imaging Radiat Oncol. 2020 Aug 10;15:72-76. doi: 10.1016/j.phro.2020.07.011. eCollection 2020 Jul.
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.
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.
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.
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.
多叶准直器（MLC）的电机故障是加速器非计划维护的常见原因，会扰乱放射治疗中心的工作流程。提前预测MLC的更换需求可实现主动维护计划安排，减少MLC更换对治疗工作流程的影响。我们提出一种多变量方法来分析轨迹日志数据，该方法可用于预测即将到来的MLC更换需求。
收集并分析了来自两台加速器的轨迹日志文件，时间跨度分别为六个月和七个月。计算每个日志文件中每个参数的平均误差并用于进一步分析。通过对预处理后的数据应用移动窗口主成分分析生成性能指标（PI）。PI的下降被认为表明即将需要更换MLC；因此，使用带有下限控制限的指数加权移动平均（EWMA）控制图来跟踪PI。
使用加权参数（λ）为0.05以及基于三个标准差和80个数据点窗口的控制限，可实现故障检测与最小化误报率的最佳平衡。该方法在十三次记录的MLC更换中识别出八次，提前一至三个工作日发出预警，同时平均每月产生1.1次误报。
这种分析轨迹日志数据的方法已被证明能够预测某些即将发生的MLC故障，尽管会产生误报。
