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

Wojtasik, Arek, Bolt, Matthew, Clark, Catherine H., Nisbet, Andrew and Chen, Tao (2020) Multivariate log file analysis for multi-leaf collimator failure prediction in radiotherapy delivery Physics & Imaging in Radiation Oncology.

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Estro Paper Final.docx - Accepted version Manuscript

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Supplementary Materials 1 Methodology Flowcharts.docx - Supplemental Material

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Supplementary Material 2 Description of PCA Final.docx - Supplemental Material

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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.

Item Type: Article
Divisions : Faculty of Engineering and Physical Sciences > Chemical and Process Engineering
Authors :
Bolt, Matthew
Clark, Catherine H.
Date : 28 July 2020
Funders : National Physical Laboratory, University of Surrey
Additional Information : Embargo OK Metadata Pending
Depositing User : James Marshall
Date Deposited : 29 Jul 2020 08:39
Last Modified : 30 Jul 2020 14:50

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