Click here to


Are you sure ?

Yes, do it No, cancel

Utilizing Historical Performance Data From Trajectory Logs and Service Reports to Establish a Proactive Maintenance Model for Minimizing MLC Downtime

B Wu*, P Zhang , T LoSasso , Memorial Sloan-Kettering Cancer Center, New York, NY


(Wednesday, 8/1/2018) 10:15 AM - 12:15 PM

Room: Karl Dean Ballroom A1

Purpose: From 2015 to 2017, four TrueBeams (TBs) at our main campus had 6.4 hours/year/TB of unexpected MLC downtime, or 15.7 failures/year/TB. We propose a proactive maintenance model using historical MLC performance data to predict MLC dysfunctions and promote preemptive maintenance and thereby reduce downtime.

Methods: MLC failures (primary/secondary, trajectory and standstill deviations) are assumed to correlate with MLC performance quantified from trajectory logs. A cohort of data from service reports and trajectory logs is used to establish a correlation model for predicting MLC dysfunctions. Specifically, service reports logged by our on-site engineers record MLC status of each failure, including service date, reason (types of deviation on a leaf), and actions taken (MLC initialization or motor/T-nut replacement), while trajectory logs record the ordered/actual leaf positions in 50Hz. In quantifying leaf performance, an event is defined as detecting a leaf’s position deviation ≥ 0.1mm from a trajectory log. The distributions of the accumulative events as functions of sorted days and individual leaves are associated with logged failures to find a correlation for predicting dysfunctions. In evaluation, a model containing TB1’s 124-week data, including 48 failures and 29,806 trajectory logs, is built and applied to TB2 for validation.

Results: According to TB1’s correlation model, if a leaf scores ≥ 20 events/day in any four days during a 10-day moving window, the leaf should mark as “potential failure�. Using this criterion, our program predicts 13 failures in validating TB2’s 2016 records: 7 are true positives, 6 are false alarms, but missing 6 actual failures. Proactively addressing the predicted failures would reduce TB2’s 2016 downtime from 6 to 2.8 hours with a cost of 2.8-hour service time for the false alarms.

Conclusion: The preemptive maintenance model based on trajectory logs can capture half of the actual failures, significantly reduce downtime and smooth clinical workflow.


MLC, Quality Assurance, Modeling


TH- External beam- photons: Quality Assurance - Linear accelerator

Contact Email