Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Introduction to Statistical Process Control

Introduction to Statistical Process Control

Statistical Process Control (SPC) is a method used to monitor and control quality within processes, particularly in manufacturing and service delivery, by employing statistical methods. SPC aims to distinguish between natural (common cause) variation and variation due to specific changes or events (special cause), allowing for timely improvements and sustained quality. The control chart, a pivotal tool in SPC, visually displays data over time alongside a central line of upper and lower control...

Control Systems

Control Systems

Control systems are everywhere in contemporary society, influencing diverse applications from aerospace to automated manufacturing. These systems can be found naturally within biological processes, such as blood sugar regulation and heart rate adjustment in response to stress, as well as in man-made systems like elevators and automated vehicles. A control system is essentially a network of subsystems and processes that collaboratively convert specific inputs into desired outputs.
At the heart...

Quality Control

Quality Control

Quality control is one of the three cyclical quality assurance activities that help keep a system under statistical control. Typical quality control activities include creating quality control charts, conducting proficiency testing, and documenting and archiving results.
Quality control helps track data, visualize trends, and identify variations, making it easier to detect deviations that may affect the accuracy of an analysis. One way to do this is by generating a quality control chart, which...

Multimachine Stability

Multimachine Stability

Multimachine stability analysis is crucial for understanding the dynamics and stability of power systems with multiple synchronous machines. The objective is to solve the swing equations for a network of M machines connected to an N-bus power system.
In analyzing the system, the nodal equations represent the relationship between bus voltages, machine voltages, and machine currents. The nodal equation is given by:

The X̄ Chart

The X̄ Chart

The x̄ chart is a statistical tool for monitoring the means in a process.
The x̄ chart, often known as the individual control chart, is a crucial tool in statistical process control. It is designed to monitor process behavior and performance over time and is widely used in various industries to ensure that processes are operating at their optimum capacity and within specified limits.
A x̄ chart is constructed by plotting individual measurements of a quality...

Interpreting Run Charts

Interpreting Run Charts

Run charts, essentially line graphs plotted over time, serve as fundamental yet effective tools for process analysis. They chronicle data sequentially, facilitating the identification of trends, shifts, or cyclical movements. This graphical representation is instrumental in determining whether a process is stable or exhibits signs of potential instability indicative of special cause variation. In the healthcare domain, run charts depict infection rates over time, enabling hospitals to monitor...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Postpartum neurological deficits following neuraxial intervention: a retrospective analysis in a tertiary obstetric centre (2021-2023).

International journal of obstetric anesthesia·2024

Same author

Use of therapeutic caffeine in acute care postoperative and critical care settings: a scoping review.

BMC anesthesiology·2021

Same author

Occurrence and determinants of enterococcal bloodstream infections: a population-based study.

Infectious diseases (London, England)·2020

Same author

3D Engineering of Ocular Tissues for Disease Modeling and Drug Testing.

Advances in experimental medicine and biology·2019

Same author

Do oxidized zirconium heads decrease tribocorrosion in total hip arthroplasty? A study of retrieved components.

The bone & joint journal·2019

Same author

SU-E-T-207: Flatness and Symmetry Threshold Detection Using Statistical Process Control.

Medical physics·2017

Same journal

Correction to "On the shape of the radiation survival curve in tumor spheroids: The role of oxygen heterogeneity".

Medical physics·2026

Same journal

Multi-view constrained semi-supervised vertebra detection for 3D ultrasound spine volume.

Medical physics·2026

Same journal

Accuracy of quantitative <sup>177</sup>Lu SPECT/CT imaging: A systematic review.

Medical physics·2026

Same journal

Physics-constrained dual-domain network for CBCT reconstruction from orthogonal X-rays in gynecologic radiotherapy.

Medical physics·2026

Same journal

Decomposition-based harmonization for quantitative PET imaging across scanners and radiotracers.

Medical physics·2026

Same journal

Development and evaluation of an in vivo dose-based monitoring system for electron FLASH radiation therapy.

Medical physics·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Mar 2, 2026

Simulation of a Scaled Assembly Process with Collaboration of a Robotic Arm and Monitoring through a Vision System for Quality Control

Simulation of a Scaled Assembly Process with Collaboration of a Robotic Arm and Monitoring through a Vision System for Quality Control

Published on: August 29, 2025

SU-E-T-205: MLC Predictive Maintenance Using Statistical Process Control Analysis.

C Able¹, C Hampton¹, A Baydush¹

¹Wake Forest School of Medicine, Winston-Salem, NC 27157.

Medical Physics

|May 19, 2017

Summary

This summary is machine-generated.

Statistical Process Control (SPC) can help predict Multileaf Collimator (MLC) motor failures by analyzing leaf velocity data. This quality control method shows promise in detecting impending issues, reducing accelerator downtime.

Keywords:

Computer software Data analysis Failure analysis Multileaf collimators Process monitoring and control Quality assurance Statistical analysis

More Related Videos

Automation of Mode Locking in a Nonlinear Polarization Rotation Fiber Laser through Output Polarization Measurements

Automation of Mode Locking in a Nonlinear Polarization Rotation Fiber Laser through Output Polarization Measurements

Published on: February 28, 2016

Author Spotlight: Enhancing PSC-to-Functional Cell Differentiation Using ML Models Based on Live-Cell Bright-Field Imaging

Author Spotlight: Enhancing PSC-to-Functional Cell Differentiation Using ML Models Based on Live-Cell Bright-Field Imaging

Published on: October 4, 2024

Related Experiment Videos

Last Updated: Mar 2, 2026

Simulation of a Scaled Assembly Process with Collaboration of a Robotic Arm and Monitoring through a Vision System for Quality Control

Simulation of a Scaled Assembly Process with Collaboration of a Robotic Arm and Monitoring through a Vision System for Quality Control

Published on: August 29, 2025

Automation of Mode Locking in a Nonlinear Polarization Rotation Fiber Laser through Output Polarization Measurements

Automation of Mode Locking in a Nonlinear Polarization Rotation Fiber Laser through Output Polarization Measurements

Published on: February 28, 2016

Author Spotlight: Enhancing PSC-to-Functional Cell Differentiation Using ML Models Based on Live-Cell Bright-Field Imaging

Author Spotlight: Enhancing PSC-to-Functional Cell Differentiation Using ML Models Based on Live-Cell Bright-Field Imaging

Published on: October 4, 2024

Area of Science:

Medical Physics
Quality Control in Radiation Therapy

Background:

Multileaf Collimator (MLC) failures disrupt radiation therapy schedules and increase accelerator downtime.
Effective quality control is essential for maintaining treatment delivery consistency and patient safety.

Purpose of the Study:

To investigate the utility of Statistical Process Control (SPC) for predicting impending Multileaf Collimator (MLC) leaf motor failures.
To retrospectively analyze MLC performance data using SPC to identify failure prediction trends.

Main Methods:

Statistical Process Control (SPC) methodology was applied to analyze MLC leaf velocity data from weekly quality assurance (QA) tests.
Individual and Moving Range (I/MR) charts were utilized, with control limits set at 3 standard deviations based on the initial 20 weeks of data.
Retrospective analysis included 8 MLCs with known motor failures and 11 without motor replacement over a 71-week period.

Main Results:

MLC motor failures exhibited two trends: 5 of 8 showed no preceding data change, while 3 of 8 displayed data points exceeding SPC limits before failure.
I/MR charts indicated that 8 of 11 non-replaced MLC motors had single data points exceeding limits, identified as false positives.

Conclusions:

SPC analysis of MLC performance data shows potential for detecting a significant portion of impending MLC motor failures.
The effectiveness of SPC in failure detection may vary depending on the failure mode (gradual vs. catastrophic).
Further research is recommended to explore if increased data sampling frequency can enhance SPC's reliability in predicting MLC failures.