Multivariate Statistical Process Control Charts and the Problem of Interpretation: A Short Overview and Some Applications in Industry

Woodall and Montgomery in a discussion paper, state that multivariate process control is one of the most rapidly developing sections of statistical process control. Nowadays, in industry, there are many situations in which the simultaneous monitoring or control, of two or more related quality - process characteristics is necessary. Process monitoring problems in which several related variables are of interest are collectively known as Multivariate Statistical Process Control (MSPC). This article has three parts. In the first part, we discuss in brief the basic procedures for the implementation of multivariate statistical process control via control charting. In the second part we present the most useful procedures for interpreting the out-of-control variable when a control charting procedure gives an out-of-control signal in a multivariate process. Finally, in the third, we present applications of multivariate statistical process control in the area of industrial process control, informatics, and businessQuality Control, Process Control, Multivariate Statistical Process Control, Hotelling's T², CUSUM, EWMA, PCA, PLS, Identification, Interpretation

Multivariate Statistical Process Control Charts: An Overview

In this paper we discuss the basic procedures for the implementation of multivariate statistical process control via control charting. Furthermore, we review multivariate extensions for all kinds of univariate control charts, such as multivariate Shewhart-type control charts, multivariate CUSUM control charts and multivariate EWMA control charts. In addition, we review unique procedures for the construction of multivariate control charts, based on multivariate statistical techniques such as principal components analysis (PCA) and partial lest squares (PLS). Finally, we describe the most significant methods for the interpretation of an out-of-control signal.quality control, process control, multivariate statistical process control, Hotelling's T-square, CUSUM, EWMA, PCA, PLS

Dynamic Non-parametric Monitoring of Air-Pollution

Air pollution poses a major problem in modern cities, as it has a significant effect in poor quality of life of the general population. Many recent studies link excess levels of major air pollutants with health-related incidents, in particular respiratory-related diseases. This introduces the need for city pollution on-line monitoring to enable quick identification of deviations from “normal” pollution levels, and providing useful information to public authorities for public protection. This article considers dynamic monitoring of pollution data (output of multivariate processes) using Kalman filters and multivariate statistical process control techniques. A state space model is used to define the in-control process dynamics, involving trend and seasonality. Distribution-free monitoring of the residuals of that model is proposed, based on binomial-type and generalised binomial-type statistics as well as on rank statistics. We discuss the general problem of detecting a change in pollutant levels that affects either the entire city (globally) or specific sub-areas (locally). The proposed methodology is illustrated using data, consisting of ozone, nitrogen oxides and sulfur dioxide collected over the air-quality monitoring network of Athens

Multivariate Statistical Process Control Charts and the Problem of Interpretation: A Short Overview and Some Applications in Industry

Woodall and Montgomery in a discussion paper, state that multivariate process control is one of the most rapidly developing sections of statistical process control. Nowadays, in industry, there are many situations in which the simultaneous monitoring or control, of two or more related quality - process characteristics is necessary. Process monitoring problems in which several related variables are of interest are collectively known as Multivariate Statistical Process Control (MSPC). This article has three parts. In the first part, we discuss in brief the basic procedures for the implementation of multivariate statistical process control via control charting. In the second part we present the most useful procedures for interpreting the out-of-control variable when a control charting procedure gives an out-of-control signal in a multivariate process. Finally, in the third, we present applications of multivariate statistical process control in the area of industrial process control, informatics, and busines

Real Time Monitoring of Carbon Monoxide Using Value-at-Risk Measure and Control Charting

One of the most important environmental health issues is air pollution, causing the deterioration of the population’s quality of life, principally in cities where the urbanization level seems limitless. Among ambient pollutants, carbon monoxide (CO) is well known for its biological toxicity. Many studies report associations between exposure to CO and excess mortality. In this context, the present work provides an advanced modelling scheme for real time monitoring of pollution data and especially of carbon monoxide pollution in city level. The real time monitoring is based on an appropriately adjusted multivariate time series model that is used in finance and gives accurate one-step-ahead forecasts. On the output of the time series, we apply an empirical monitoring scheme that is used for the early detection of abnormal increases of CO levels. The proposed methodology is applied in the city of Athens and as the analysis revealed has a valuable performance

Multivariate Statistical Process Control Charts: An Overview

In this paper we discuss the basic procedures for the implementation of multivariate statistical process control via control charting. Furthermore, we review multivariate extensions for all kinds of univariate control charts, such as multivariate Shewhart-type control charts, multivariate CUSUM control charts and multivariate EWMA control charts. In addition, we review unique procedures for the construction of multivariate control charts, based on multivariate statistical techniques such as principal components analysis (PCA) and partial lest squares (PLS). Finally, we describe the most significant methods for the interpretation of an out-of-control signal

Real Time Monitoring of Carbon Monoxide Using Value-at-Risk Measure and Control Charting

One of the most important environmental health issues is air pollution, causing the deterioration of the population’s quality of life, principally in cities where the urbanization level seems limitless. Among ambient pollutants, carbon monoxide (CO) is well known for its biological toxicity. Many studies report associations between exposure to CO and excess mortality. In this context, the present work provides an advanced modelling scheme for real time monitoring of pollution data and especially of carbon monoxide pollution in city level. The real time monitoring is based on an appropriately adjusted multivariate time series model that is used in finance and gives accurate one-step-ahead forecasts. On the output of the time series, we apply an empirical monitoring scheme that is used for the early detection of abnormal increases of CO levels. The proposed methodology is applied in the city of Athens and as the analysis revealed has a valuable performance

Real Time Monitoring of Carbon Monoxide Using Value-at-Risk Measure and Control Charting

One of the most important environmental health issues is air pollution, causing the deterioration of the population’s quality of life, principally in cities where the urbanization level seems limitless. Among ambient pollutants, carbon monoxide (CO) is well known for its biological toxicity. Many studies report associations between exposure to CO and excess mortality. In this context, the present work provides an advanced modelling scheme for real time monitoring of pollution data and especially of carbon monoxide pollution in city level. The real time monitoring is based on an appropriately adjusted multivariate time series model that is used in finance and gives accurate one-step-ahead forecasts. On the output of the time series, we apply an empirical monitoring scheme that is used for the early detection of abnormal increases of CO levels. The proposed methodology is applied in the city of Athens and as the analysis revealed has a valuable performance

Multivariate Statistical Process Control Charts and the Problem of Interpretation: A Short Overview and Some Applications in Industry

Woodall and Montgomery in a discussion paper, state that multivariate process control is one of the most rapidly developing sections of statistical process control. Nowadays, in industry, there are many situations in which the simultaneous monitoring or control, of two or more related quality - process characteristics is necessary. Process monitoring problems in which several related variables are of interest are collectively known as Multivariate Statistical Process Control (MSPC). This article has three parts. In the first part, we discuss in brief the basic procedures for the implementation of multivariate statistical process control via control charting. In the second part we present the most useful procedures for interpreting the out-of-control variable when a control charting procedure gives an out-of-control signal in a multivariate process. Finally, in the third, we present applications of multivariate statistical process control in the area of industrial process control, informatics, and busines

Identification and Antimicrobial Resistance of Campylobacter Species Isolated from Animal Sources

Background: Campylobacter spp. are together with Salmonella spp. the leading causes of human bacterial gastroenteritis worldwide. The most commonly isolated species in humans are Campylobacter jejuni and C. coli. The isolation, identification, and antimicrobial resistance of Campylobacter spp. from poultry and raw meat from slaughterhouses, has been investigated for the first time in Greece. During the period from August 2005 to November 2008 a total of 1080 samples were collected: (a) 830 fecal samples from five poultry farms, (b) 150 cecal samples from chicken carcasses in a slaughterhouse, and (c) 100 fecal samples from one pig farm near the region of Attica. The identification of the isolates was performed with conventional (sodium hippurate hydrolysis and commercial identification system (Api CAMPY system, bioMerieux, France), as well as with and molecular methods based on 16S rRNA species specific gene amplification by PCR and subsequent sequence analysis of the PCR products. Results: Sixteen Campylobacter strains were isolated, all collected from the poultry farms. None of the strains was identified as C. jejuni. Antimicrobial susceptibility to six antimicrobials was performed and all the strains were susceptible to ciprofloxacin, amoxicillin–clavulanic acid, and gentamicin. Thirteen out of 14 C. coli were resistant to erythromycin and all C. coli strains were resistant to ampicillin. Conclusion: Our results emphasize the need for a surveillance and monitoring system with respect to the prevalence and antimicrobial resistance of Campylobacter in poultry, as well as for the use of antimicrobials in veterinary medicine in Greece