The quality characteristic(s) are assumed to follow the normal distribution in many control chart constructions, although this assumption may not hold some instances. This study proposes Bayesian-I and Bayesian-II Shewhart-type charts for monitoring Maxwell scale parameter phase II study. posterior predictive distributions used construct limits proposed charts, respectively. Various performance...

Some of the most widely-investigated control charting techniques for autocorrelated data are based on time series residuals. If the mean shift in the autocorrelated process is a sudden step shift, the resulting mean shift in the residuals is time varying and has been referred to as the fault signature. Traditional residual based charts, such as a Shewhart, CUSUM, or EWMA on the residuals, do no...

One of the most powerful tools in quality control is the statistical control chart. First developed in the 1920's by Walter Shewhart, the control chart found widespread use during World War II and has been employed, with various modifications ever since. The drawbacks to multivariate charting schemes is their inability to identify which variable was the source of the signal. The multivariate ex...

SubmitJames 0. Westgard, Patricia L. Barry, and ters: Marian R. Hunt, Depts. of Medicine, Pathology, and Clinical Laboratories, University of Wisconsin, Center for Health Sciences, Madison, WI 53792 Torgny Groth, Group for Biomedical Informatics, Uppsala University Data Center, S-75002 Uppsala, Sweden ReviewRobert W. Burnett, Clinical Chemistry ers: Laboratory, Hartford Hospital, Hartford, CT 0...

Abstract Machine learning-based condition monitoring of wind turbines’ critical components is an active area research, especially for pitch systems, which suffer from a high failure rate. In this work, we successfully predicted and detected the high-temperature fault electric motor by analyzing SCADA data through ensemble approach. For that, normal behavior models to predict temperature were co...

Quality control charts indicate out of control conditions if any nonrandom pattern of the points is observed or any point is plotted beyond the control limits. Nonrandom patterns of Shewhart control charts are tested with sensitizing rules. When the processes are defined with fuzzy set theory, traditional sensitizing rules are insufficient for defining all out of control conditions. This is due...

The share nature of the transmission channel in IEEE 802.11 makes the network vulnerable to several attacks like the MAC layer misbehavior which can be similar to denial of service attack. In this way cheating node by choosing smaller backoff timer attempts to increase its resources at the expense to other stations which respect the protocol. In this paper, we suggest a novel detection scheme o...

Shewhart charts are the most commonly utilised control for process monitoring in industries with assumption that underlying distribution of quality characteristic is normal. However, this may not always hold true practice. In paper, weighted-variance mean developed and their population standard deviation estimated using three subgroup scale estimators, namely deviation, median absolute trimmed ...

Often the least appropriate assumption in traditional control charting technology is that process data constitute a random sample. In reality most process data are correlated—either temporally, spatially, or due to nested sources of variation. One approach to monitoring temporally correlated data uses a control chart on the forecast errors from a time series model of the process with, possibly,...

The presence of asymmetric data in production processes or service operations has prompted the development new monitoring schemes. In this article, an adapted version exponentially weighted moving averages (EWMA) control chart with dynamic limits is proposed to monitor mean samples from skew-normal distribution. detection ability online was investigated by simulating average run length (ARL) pe...