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Related Concept Videos

Introduction to Statistical Process Control01:15

Introduction to Statistical Process Control

168
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...
168
Quality Control01:05

Quality Control

198
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...
198
Quality Assurance01:19

Quality Assurance

159
Quality assurance is the overarching term used to describe the activities employed to ensure the proper performance of a system. These activities can be classified into three categories: quality control, quality assessment, and internal corrective measures. Typically, these activities work cyclically: quality control is performed before and during the analysis, while quality assessment occurs during and after the investigation. Internal corrective measures are implemented based on the findings...
159
The X̄ Chart00:58

The X̄ Chart

151
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...
151
Load-frequency control01:28

Load-frequency control

197
Load-frequency control (LFC) is vital for maintaining power system stability, ensuring that frequency and power flows remain within acceptable limits during load changes. Turbine-governor control eliminates rotor accelerations and decelerations following load changes. However, a steady-state frequency error persists when the change in the turbine-governor reference setting is zero. In an interconnected power system, each area agrees to export or import a scheduled amount of power through...
197
The R Chart01:02

The R Chart

106
In statistical process control, control charts, particularly R charts, are instrumental in monitoring process variations and identifying non-random patterns that run charts might miss. R charts track the variability within process subgroups, which is crucial when standard deviation use is impractical or unknown process variations exist.
R charts are pivotal for pinpointing shifts in process variability. Stability is indicated when all data points remain within the defined upper and lower...
106

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Related Experiment Video

Updated: Jul 23, 2025

Dynamic Lung Tumor Tracking for Stereotactic Ablative Body Radiation Therapy
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Predictive quality assurance for linear accelerator target failure using statistical process control.

Jingdong Li1, Dongxu Wang1, Maria Chan1

  • 1Memorial Sloan-Kettering Cancer Center at Basking Ridge, NJ, United States of America.

Biomedical Physics & Engineering Express
|July 12, 2023
PubMed
Summary
This summary is machine-generated.

Predictive quality assurance (QA) using Statistical Process Control (SPC) and AutoRegressive Integrated Moving Average (ARIMA) modeling can identify linear accelerator (Linac) target failure risks. Enhanced dynamic wedge (EDW) measurements are sensitive to target degradation, providing early warnings.

Keywords:
linacpredicative quality assurancestatistical process control

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Area of Science:

  • Medical Physics
  • Radiotherapy Engineering
  • Quality Assurance

Background:

  • Linear accelerator (Linac) performance relies heavily on x-ray target integrity.
  • Sudden target failure disrupts Linac operation and patient care.
  • Developing predictive quality assurance (QA) methods is crucial for radiotherapy.

Purpose of the Study:

  • To develop a predictive QA method using Statistical Process Control (SPC) and AutoRegressive Integrated Moving Average (ARIMA) modeling.
  • To identify the risk of x-ray target failure in Linacs before it occurs.
  • To analyze historical QA data for early warning signals of target degradation.

Main Methods:

  • Retrospective analysis of daily QA data, including open field and enhanced dynamic wedge (EDW) measurements.
  • Application of Statistical Process Control (SPC) to monitor process performance.
  • Utilizing AutoRegressive Integrated Moving Average (ARIMA) modeling for time-series forecasting.
  • Evaluating the sensitivity of EDW measurements to target integrity.

Main Results:

  • SPC applied to open beam QA data did not provide early warnings of target failure.
  • SPC applied to EDW measurements detected control limit breaches weeks before target failure.
  • EDW measurements are sensitive to target degradation due to nonuniform magnification factors.
  • ARIMA modeling shows potential for extending the warning period for predictive QA.

Conclusions:

  • Predictive QA using SPC on EDW daily data can provide early warnings of impending Linac target failure.
  • EDW measurements are more sensitive indicators of target degradation than open field measurements.
  • Combining SPC and ARIMA modeling offers a promising approach for proactive radiotherapy equipment maintenance.