Statistical process control ($SPC$), an internationally recognized technique for improving product quality and productivity, has been widely employed in various industries. $SPC$ relies on the use of control charts to monitor a manufacturing process for identifying causes of process variation and signaling the necessity of corrective action for the process. Fuzzy data exist ubiquitously in the ...

Condition monitoring is the foundation of a condition based maintenance (CBM). To relate the information obtained from the condition monitoring to the actual state of the system, it is usually required a stochastic model. On the other hand, considering the interactions and similarities that exist between CBM and statistical process control (SPC), the integrated models for CBM and SPC have been ...

Control charts are widely used for process monitoring. Software reliability process can be monitored efficiently by using Statistical Process Control (SPC). It assists the software development team to identify failures and actions to be taken during software failure process and hence, assures better software reliability. In this paper we proposed a control mechanism based on order statistics of...

We consider the problem of monitoring variability of autocorrelated processes. This paper combines variance estimation techniques from simulation literature with two statistical process control charts from statistical process control (SPC) literature. The proposed SPC methods do not require any assumptions on the distribution of the underlying process and use a variance estimate from each batch...

Control charts are widely used for process monitoring. Software reliability process can be monitored efficiently by using Statistical Process Control (SPC). It assists the software development team to identify failures and actions to be taken during software failure process and hence, assures better software reliability. In this paper we propose a control mechanism based on the cumulative quant...

We consider statistical process control (SPC) of univariate processes when observed data are not normally distributed. Most existing SPC procedures are based on the normality assumption. In the literature, it has been demonstrated that their performance is unreliable in cases when they are used for monitoring non-normal processes. To overcome this limitation, we propose two SPC control charts f...

Quality is considered asthe principal factor that determines the long-termsuccess or failure of any organization. Organizations perform quality control by monitoring process output using Statistical Control, performed as part production (Statistical Process SPC) a final check (Acceptance Sampling).SPC major management statistical tool and its instruments (control charts capability analysis) are...

OBJECTIVE To investigate and illustrate the use of statistical process control (SPC) for prospective monitoring and detection of regional changes in prescription opioid abuse rates. METHODS A demonstration project was conducted by applying several types of SPC charts to Louisiana TANF (Temporary Assistance for Needy Families) applicant self-reported opioid illicit use data collected during 21...

Monitoring the stability of the software process in the lower level companies is a challenging issue to software engineers. In this paper, SPC is applied to software metrics. Defect Density, Review Performance and Rework percentage and results after applying the SPC to various processes of software are discussed and analyzed, using control charts, the most sophisticated tools of SPC. The diffic...

INTEGRATION OF DATA MJNING ALGORITHMS AND CONTROL' CBARI'S FOR MULTIVARIATE AND AUTOCORRELATED PROCESSES WEERAWAT JITPITAKLERT, Ph.D. ,The University of Texas at Arlington, 2009 Supervising Professor: Seoung Bum Kim The objective of tllli3 dissertation is to integrate state-of-the-art data mining 3lgoritbms with statistical process control (SPC) tools to a.chieve efficient 'monitoring in multiv...