Item Details

abstract

Statistical Process Control (SPC) is an approach to measure normal and abnormal variations of a process. Its success rests upon the fact that any process can be mapped by a series of inputs and outputs. SPC techniques are displayed using process behavior charts which are graphs that contain data from a chosen process. Traditional SPC techniques are designed to detect large variations within a process. Yet, advanced SPC techniques are designed to detect small variations within a process. Each SPC technique has its own method of summarizing the data to analyze the process and options to assess performance. SPC has shown promise in identifying early detection of performance degradation. Preventative and/or predictive maintenance relies on the ability of statistical tests to detect dysfunction when the process exceeds its tolerance levels. Yet, a specific technique must be chosen to aid in the discovery. SPC allows the characterization of a specific process through its unique data mining and analysis techniques. In turn, SPC can be used to detect machine dysfunction dependent upon a predetermined level of sensitivity set by the user. The focus of this dissertation is to determine the utility and effectiveness of advanced SPC techniques in order to predict medical linear accelerator dysfunction. It will also assess which data set is most effective to implement SPC techniques that can accurately reflect the behavior of a process. Specific research objectives are addressed in the designated chapters of the dissertation: (1) Evaluate new SPC techniques for analyzing linear accelerator system operating parameters and/or performance; (2) Determine the effectiveness of advanced SPC techniques in the detection of linear accelerator performance dysfunction; and (3) Assess and validate the quality of digital data being produced by linear accelerators against those produced by quality assurance instrumentation typically used by radiation physicists.

subject

Log Files

Medical Linear Accelerator

Performance

Quality Assurance

Statistical Process Control

contributor

Nguyen, Callistus I Huy Minh (author)

Munley, Michael T. (committee chair)

Baydush, Alan H. (committee member)

Bourland, John D. (committee member)

Weaver, Ashley A. (committee member)

Willey, Jeffrey S. (committee member)

date

2017-08-22T08:35:27Z (accessioned)

2019-08-21T08:30:14Z (available)

2017 (issued)

degree

Biomedical Engineering (discipline)

embargo

2019-08-21 (terms)

identifier

http://hdl.handle.net/10339/86350 (uri)

language

en (iso)

publisher

Wake Forest University

title

Advanced Statistical Process Control Techniques for Analysis of Medical Linear Accelerator Performance

type

Dissertation