title: A model for preemptive maintenance of medical linear accelerators-predictive maintenance.
abstract: BACKGROUND
  Unscheduled accelerator downtime can negatively impact the quality of life of patients during their struggle against cancer. Currently digital data accumulated in the accelerator system is not being exploited in a systematic manner to assist in more efficient deployment of service engineering resources. The purpose of this study is to develop an effective process for detecting unexpected deviations in accelerator system operating parameters and/or performance that predicts component failure or system dysfunction and allows maintenance to be performed prior to the actuation of interlocks.
  
  
  METHODS
  The proposed predictive maintenance (PdM) model is as follows: 1) deliver a daily quality assurance (QA) treatment; 2) automatically transfer and interrogate the resulting log files; 3) once baselines are established, subject daily operating and performance values to statistical process control (SPC) analysis; 4) determine if any alarms have been triggered; and 5) alert facility and system service engineers. A robust volumetric modulated arc QA treatment is delivered to establish mean operating values and perform continuous sampling and monitoring using SPC methodology. Chart limits are calculated using a hybrid technique that includes the use of the standard SPC 3σ limits and an empirical factor based on the parameter/system specification.
  
  
  RESULTS
  There are 7 accelerators currently under active surveillance. Currently 45 parameters plus each MLC leaf (120) are analyzed using Individual and Moving Range (I/MR) charts. The initial warning and alarm rule is as follows: warning (2 out of 3 consecutive values ≥ 2σ hybrid) and alarm (2 out of 3 consecutive values or 3 out of 5 consecutive values ≥ 3σ hybrid). A customized graphical user interface provides a means to review the SPC charts for each parameter and a visual color code to alert the reviewer of parameter status. Forty-five synthetic errors/changes were introduced to test the effectiveness of our initial chart limits. Forty-three of the forty-five errors (95.6 %) were detected in either the I or MR chart for each of the subsystems monitored.
  
  
  CONCLUSIONS
  Our PdM model shows promise in providing a means for reducing unscheduled downtime. Long term monitoring will be required to establish the effectiveness of the model.
authors.affiliation: Department of Radiation Oncology, Wake Forest School of Medicine, Medical Center Boulevard, Winston-Salem, NC, 27157, USA. cable@wakehealth.edu.|Department of Radiation Oncology, Florida Cancer Specialist, 8763 River Crossing Boulevard, Florida, USA. cable@wakehealth.edu.
authors.forename: Charles M
authors.identifier: http://orcid.org/0000-0002-0891-9543
authors.initials: CM
authors.lastname: Able
authors.affiliation: Department of Radiation Oncology, Wake Forest School of Medicine, Medical Center Boulevard, Winston-Salem, NC, 27157, USA.
authors.forename: Alan H
authors.initials: AH
authors.lastname: Baydush
authors.affiliation: Department of Radiation Oncology, Wake Forest School of Medicine, Medical Center Boulevard, Winston-Salem, NC, 27157, USA.
authors.forename: Callistus
authors.initials: C
authors.lastname: Nguyen
authors.affiliation: Gibbs Cancer Center and Research Institute, Spartanburg Regional Medical Center, Greer, SC, USA.
authors.forename: Jacob
authors.initials: J
authors.lastname: Gersh
authors.affiliation: John Theuer Cancer Center, Hackensack University Medical Center, Hackensack, USA.
authors.forename: Alois
authors.initials: A
authors.lastname: Ndlovu
authors.affiliation: John Theuer Cancer Center, Hackensack University Medical Center, Hackensack, USA.
authors.forename: Igor
authors.initials: I
authors.lastname: Rebo
authors.affiliation: North Sydney Cancer Center, Royal North Shore Hospital, Sydney, Australia.
authors.forename: Jeremy
authors.initials: J
authors.lastname: Booth
authors.affiliation: North Sydney Cancer Center, Royal North Shore Hospital, Sydney, Australia.
authors.forename: Mario
authors.initials: M
authors.lastname: Perez
authors.affiliation: Cone Health Cancer Center, 501 N. Elam Avenue, Greensboro, NC, 27403, USA.
authors.forename: Benjamin
authors.initials: B
authors.lastname: Sintay
authors.affiliation: Department of Radiation Oncology, Wake Forest School of Medicine, Medical Center Boulevard, Winston-Salem, NC, 27157, USA.
authors.forename: Michael T
authors.initials: MT
authors.lastname: Munley
mesh_terms: D004868:Equipment Failure; D006801:Humans; D008283:Maintenance; D015233:Models, Statistical; D009369:Neoplasms / Q000532:radiotherapy*; D010315:Particle Accelerators; D011785:Quality Assurance, Health Care / Q000592:standards*; D011786:Quality Control; D018787:Radiation Oncology / Q000295:instrumentation* / Q000592:standards*; D011880:Radiotherapy Planning, Computer-Assisted / Q000379:methods*; D050397:Radiotherapy, Intensity-Modulated / Q000379:methods*; D012984:Software
keywords: Linear accelerator; Predictive maintenance; Quality assurance; Quality control; Radiation therapy; Statistical process control