Monitoring Sewage Treatment Plants: Some Quality Control Aspects

Berthouex, Paul Mac; Hunter, William G.; Pallesen, Lars

doi:10.1080/00224065.1978.11980842

Cited by 116 publications

(54 citation statements)

References 4 publications

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“…For Models 1 through 4, window lengths larger than 20 had little added bene®t, since by timestep 20 the fault signatures decay to very small values. From (8), if one computed the GLR statistic for a fault occurring 40 (for example) timesteps prior to the current time, very little weight would be placed on the most recent residuals.…”

Section: A Glrt For Spc Of Autocorrelated Processesmentioning

confidence: 99%

The GLRT for statistical process control of autocorrelated processes

Apley¹,

Shi²

1999

IIE Transactions

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This paper presents an on-line Statistical Process Control (SPC) technique, based on a Generalized Likelihood Ratio Test (GLRT), for detecting and estimating mean shifts in autocorrelated processes that follow a normally distributed Autoregressive Integrated Moving Average (ARIMA) model. The GLRT is applied to the uncorrelated residuals of the appropriate time-series model. The performance of the GLRT is compared to two other commonly applied residual-based tests ± a Shewhart individuals chart and a CUSUM test. A wide range of ARIMA models are considered, with the conclusion that the best residual-based test to use depends on the particular ARIMA model used to describe the autocorrelation. For many models, the GLRT performance is far superior to either a CUSUM or Shewhart test, while for others the dierence is negligible or the CUSUM test performs slightly better. Simple, intuitive guidelines are provided for determining which residual-based test to use. Additional advantages of the GLRT are that it directly provides estimates of the magnitude and time of occurrence of the mean shift, and can be used to distinguish dierent types of faults, e.g., a sustained mean shift versus a temporary spike.

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Section: A Glrt For Spc Of Autocorrelated Processesmentioning

confidence: 99%

The GLRT for statistical process control of autocorrelated processes

Apley¹,

Shi²

1999

IIE Transactions

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“…In many instances, the misplaced control limits result from the autocorrelation of the process observations, which violates a basic assumption often associated with the Shewhart chart (Woodall (2000)). Autocorrelation of process observations has been reported in many industries, including cast steel (Alwan (1992), wastewater treatment plants (Berthouex, Hunter, and Pallesen (1978)), chemical processes industries (Montgomery and Mastrangelo (1991) and many other service industries and programs. Several models have been proposed to monitor processes with auto correlated observations.…”

Section: Univariate (Shewhart) Control Chartsmentioning

confidence: 99%

“…Research on quality control charts for correlated processes focused on Univariate processes. Box, Jenkins, and Macgregor (1974) and Berthouex, Hunter and Pallesen (1978) noticed and discussed the correlated observations in production processes. Alwan and Roberts (1988) proposed a general approach to monitor residuals of Univariate auto correlated time series where the systematic patterns are filtered out and the special changes are more exposed.…”

Section: Multivariate Quality Control (Mqc)mentioning

confidence: 99%

The quality movement in hospital care

Jarrett

2013

Qual Quant

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The purpose is to introduce the demand for the quality movement practice in hospital care. . We show both the need and application of quality monitoring, especially the need monitoring activities having auto correlated data flows of which there are many in the hospital environment. The goal is to control the flow of quality care data in the dynamic behavior of these systems of acre in hospitals. These monitoring systems are designed to control and improve changes in the hospital care environment. AbstractThe purpose is to introduce the demand for the quality movement practice in hospital care. . We show both the need and application of quality monitoring, especially the need monitoring activities having auto correlated data flows of which there are many in the hospital environment. The goal is to control the flow of quality care data in the dynamic behavior of these systems of acre in hospitals. These monitoring systems are designed to control and improve changes in the hospital care environment.

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“…Montgomery and Friedman (1989) found it to occur frequently in SPC data from computer-integrated manufacturing environments. Berthouex et al (1978); MacGregor et al (1990);and Harris et al (1991) found it in continuous processes and high technology industries. Woodall et al (1993) noted that positive autocorrelation is more common in manufacturing applications than negative autocorrelation.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Two parameters (shape and scale) provide the distribution with flexibility to model systems in which the number of events (e.g., failures) increases with time (e.g., product wear), decreases with time (e.g., infant mortality) or remains constant (e.g., failures due to external shocks to the system). Likewise, many real-world processes, ranging from machining to chemical to high technology, exhibit autocorrelated behavior as cited in research by Vasilopoulos et al (1978); Ermer et al (1979); Wardell et al (1992); Box et al (1976); Alwan and Bissell (1988); Montgomery and Friedman (1989); Berthouex et al (1978), MacGregor et al (1990) and Harris et al (1991).…”

Section: Introductionmentioning

confidence: 99%

The impact of Weibull data and autocorrelation on the performance of the Shewhart and exponentially weighted moving average control charts

Black¹,

Smith²,

Wells³

2011

IJIEC

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Many real-world processes generate autocorrelated and/or Weibull data. In such cases, the independence and/or normality assumptions underlying the Shewhart and EWMA control charts are invalid. Although data transformations exist, such tools would not normally be understood or employed by naive practitioners. Thus, the question arises, "What are the effects on robustness whenever these charts are used in such applications?" Consequently, this paper examines and compares the performance of these two control charts when the problem (the model) is subjected to autocorrelated and/or Weibull data. A variety of conditions are investigated related to the magnitudes of various parameters related to the process shift, the autocorrelation coefficient and the Weibull shape parameter. Results indicate that the EWMA chart outperforms the Shewhart in 62% of the cases, particularly those cases with low to moderate autocorrelation effects. The Shewhart chart outperforms the EWMA chart in 35% of the cases, particularly those cases with high autocorrelation and zero or high process shift effects.

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Monitoring Sewage Treatment Plants: Some Quality Control Aspects

Cited by 116 publications

References 4 publications

The GLRT for statistical process control of autocorrelated processes

The GLRT for statistical process control of autocorrelated processes

The quality movement in hospital care

The impact of Weibull data and autocorrelation on the performance of the Shewhart and exponentially weighted moving average control charts

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