Improved Principal Component Monitoring of Large-Scale Processes

This is the first paper that shows and theoretically analyses that the presence of auto-correlation can produce considerable alterations in the Type I and Type II errors in univariate and multivariate statistical control charts. To remove this undesired effect, linear inverse ARMA filter are employed and the application studies in this paper show that false alarms (increased Type I errors) and an insensitive monitoring statistics (increased Type II errors) were eliminated.

Dynamic Multivariate Statistical Process Control using Subspace Identification

This paper points out a serious flaw in dynamic multivariate statistical process control (MSPC). The principal component analysis of a linear time series model that is employed to capture auto- and cross-correlation in recorded data may produce a considerable number of variables to be analysed. To give a dynamic representation of the data (based on variable correlation) and circumvent the production of a large time-series structure, a linear state space model is used here instead. The paper demonstrates that incorporating a state space model, the number of variables to be analysed dynamically can be considerably reduced, compared to conventional dynamic MSPC techniques.

Fault reconstruction in linear dynamic systems using multivariate statistics

Treasure et al. (2004) recently proposed a new sub space-monitoring technique, based on the N4SID algorithm, within the multivariate statistical process control framework. This dynamic-monitoring method requires considerably fewer variables to be analysed when compared with dynamic principal component analysis (PCA). The contribution charts and variable reconstruction, traditionally employed for static PCA, are analysed in a dynamic context. The contribution charts and variable reconstruction may be affected by the ratio of the number of retained components to the total number of analysed variables. Particular problems arise if this ratio is large and a new reconstruction chart is introduced to overcome these. The utility of such a dynamic contribution chart and variable reconstruction is shown in a simulation and by application to industrial data from a distillation unit.

Improved reliability in diagnosing faults using multivariate statistics

This paper analyses multivariate statistical techniques for identifying and isolating abnormal process behaviour. These techniques include contribution charts and variable reconstructions that relate to the application of principal component analysis (PCA). The analysis reveals firstly that contribution charts produce variable contributions which are linearly dependent and may lead to an incorrect diagnosis, if the number of principal components retained is close to the number of recorded process variables. The analysis secondly yields that variable reconstruction affects the geometry of the PCA decomposition. The paper further introduces an improved variable reconstruction method for identifying multiple sensor and process faults and for isolating their influence upon the recorded process variables. It is shown that this can accommodate the effect of reconstruction, i.e. changes in the covariance matrix of the sensor readings and correctly re-defining the PCA-based monitoring statistics and their confidence limits. (c) 2006 Elsevier Ltd. All rights reserved.

Fault Diagnosis in Internal Combustion Engines Using Nonlinear Multivariate Statistics

This paper presents a statistical-based fault diagnosis scheme for application to internal combustion engines. The scheme relies on an identified model that describes the relationships between a set of recorded engine variables using principal component analysis (PCA). Since combustion cycles are complex in nature and produce nonlinear relationships between the recorded engine variables, the paper proposes the use of nonlinear PCA (NLPCA). The paper further justifies the use of NLPCA by comparing the model accuracy of the NLPCA model with that of a linear PCA model. A new nonlinear variable reconstruction algorithm and bivariate scatter plots are proposed for fault isolation, following the application of NLPCA. The proposed technique allows the diagnosis of different fault types under steady-state operating conditions. More precisely, nonlinear variable reconstruction can remove the fault signature from the recorded engine data, which allows the identification and isolation of the root cause of abnormal engine behaviour. The paper shows that this can lead to (i) an enhanced identification of potential root causes of abnormal events and (ii) the masking of faulty sensor readings. The effectiveness of the enhanced NLPCA based monitoring scheme is illustrated by its application to a sensor fault and a process fault. The sensor fault relates to a drift in the fuel flow reading, whilst the process fault relates to a partial blockage of the intercooler. These faults are introduced to a Volkswagen TDI 1.9 Litre diesel engine mounted on an experimental engine test bench facility.

Multivariate prediction of clarified butter composition using Raman spectroscopy

Raman spectroscopy has been used to predict the abundance of the FA in clarified butterfat that was obtained from dairy cows fed a range of levels of rapeseed oil in their diet. Partial least squares regression of the Raman spectra against FA compositions obtained by GC showed good prediction for the five major (abundance >5%) FA with R-2=0.74-0.92 and a root mean SE of prediction (RMSEP) that was 5-7% of the mean. In general, the prediction accuracy fell with decreasing abundance in the sample, but the RMSEP was 1.25%. The Raman method has the best prediction ability for unsaturated FA (R-2=0.85-0.92), and in particular trans unsaturated FA (best-predicted FA was 18:1 tDelta9). This enhancement was attributed to the isolation of the unsaturated modes from the saturated modes and the significantly higher spectral response of unsaturated bonds compared with saturated bonds. Raman spectra of the melted butter samples could also be used to predict bulk parameters calculated from standard analyzes, such as iodine value (R-2=0.80) and solid fat content at low temperature (R-2=0.87). For solid fat contents determined at higher temperatures, the prediction ability was significantly reduced (R-2=0.42), and this decrease in performance was attributed to the smaller range of values in solid fat content at the higher temperatures. Finally, although the prediction errors for the abundances of each of the FA in a given sample are much larger with Raman than with full GC analysis, the accuracy is acceptably high for quality control applications. This, combined with the fact that Raman spectra can be obtained with no sample preparation and with 60-s data collection times, means that high-throughput, on-line Raman analysis of butter samples should be possible.

Improved fault diagnosis in multivariate systems using regression-based reconstruction

Subspace monitoring has recently been proposed as a condition monitoring tool that requires considerably fewer variables to be analysed compared to dynamic principal component analysis (PCA). This paper analyses subspace monitoring in identifying and isolating fault conditions, which reveals that the existing work suffers from inherent limitations if complex fault senarios arise. Based on the assumption that the fault signature is deterministic while the monitored variables are stochastic, the paper introduces a regression-based reconstruction technique to overcome these limitations. The utility of the proposed fault identification and isolation method is shown using a simulation example and the analysis of experimental data from an industrial reactive distillation unit.

Modelling the impact of antibiotic use and infection control practices on the incidence of hospital-acquired methicillin-resistant Staphylococcus aureus: a time-series analysis

Objectives: Methicillin-resistant Staphylococcus aureus (MRSA) is a major nosocomial pathogen worldwide. A wide range of factors have been suggested to influence the spread of MRSA. The objective of this study was to evaluate the effect of antimicrobial drug use and infection control practices on nosocomial MRSA incidence in a 426-bed general teaching hospital in Northern Ireland.

Methods: The present research involved the retrospective collection of monthly data on the usage of antibiotics and on infection control practices within the hospital over a 5 year period (January 2000–December 2004). A multivariate ARIMA (time-series analysis) model was built to relate MRSA incidence with antibiotic use and infection control practices.

Results: Analysis of the 5 year data set showed that temporal variations in MRSA incidence followed temporal variations in the use of fluoroquinolones, third-generation cephalosporins, macrolides and amoxicillin/clavulanic acid (coefficients = 0.005, 0.03, 0.002 and 0.003, respectively, with various time lags). Temporal relationships were also observed between MRSA incidence and infection control practices, i.e. the number of patients actively screened for MRSA (coefficient = -0.007), the use of alcohol-impregnated wipes (coefficient = -0.0003) and the bulk orders of alcohol-based handrub (coefficients = -0.04 and -0.08), with increased infection control activity being associated with decreased MRSA incidence, and between MRSA incidence and the number of new patients admitted with MRSA (coefficient = 0.22). The model explained 78.4% of the variance in the monthly incidence of MRSA.

Conclusions: The results of this study confirm the value of infection control policies as well as suggest the usefulness of restricting the use of certain antimicrobial classes to control MRSA.

Quasiexperimental Study of the Effects of Antibiotic Use, Gastric Acid-Suppressive Agents, and Infection Control Practices on the Incidence of Clostridium difficile-Associated Diarrhea in Hospitalized Patients

The objective of this study was to evaluate the effects of antimicrobial drug use, gastric acid-suppressive agent use, and infection control practices on the incidence of Clostridium difficile-associated diarrhea (CDAD) in a 426-bed general teaching hospital in Northern Ireland. The study was retrospective and ecological in design. A multivariate autoregressive integrated moving average (time-series analysis) model was built to relate CDAD incidence with antibiotic use, gastric acid-suppressive agent use, and infection control practices within the hospital over a 5-year period (February 2002 to March 2007). The findings of this study showed that temporal variation in CDAD incidence followed temporal variations in expanded-spectrum cephalosporin use (average delay = 2 months; variation of CDAD incidence = 0.01/100 bed-days), broad-spectrum cephalosporin use (average delay = 2 months; variation of CDAD incidence = 0.02/100 bed-days), fluoroquinolone use (average delay = 3 months; variation of CDAD incidence = 0.004/100 bed-days), amoxicillin-clavulanic acid use (average delay = 1 month; variation of CDAD incidence = 0.002/100 bed-days), and macrolide use (average delay = 5 months; variation of CDAD incidence = 0.002/100 bed-days). Temporal relationships were also observed between CDAD incidence and use of histamine-2 receptor antagonists (H2RAs; average delay = 1 month; variation of CDAD incidence = 0.001/100 bed-days). The model explained 78% of the variance in the monthly incidence of CDAD. The findings of this study highlight a temporal relationship between certain classes of antibiotics, H2RAs, and CDAD incidence. The results of this research can help hospitals to set priorities for restricting the use of specific antibiotic classes, based on the size-effect of each class and the delay necessary to observe an effect.

The impact of biomarkers in multivariate algorithms for bladder cancer diagnosis in patients with hematuria

BACKGROUND: We appraised 23 biomarkers previously associated with urothelial cancer in a case-control study. Our aim was to determine whether single biomarkers and/or multivariate algorithms significantly improved on the predictive power of an algorithm based on demographics for prediction of urothelial cancer in patients presenting with hematuria. METHODS: Twenty-two biomarkers in urine and carcinoembryonic antigen (CEA) in serum were evaluated using enzyme-linked immunosorbent assays (ELISAs) and biochip array technology in 2 patient cohorts: 80 patients with urothelial cancer, and 77 controls with confounding pathologies. We used Forward Wald binary logistic regression analyses to create algorithms based on demographic variables designated prior predicted probability (PPP) and multivariate algorithms, which included PPP as a single variable. Areas under the curve (AUC) were determined after receiver-operator characteristic (ROC) analysis for single biomarkers and algorithms. RESULTS: After univariate analysis, 9 biomarkers were differentially expressed (t test; P

The impact of nonadherence to inhaled long-acting β - adrenoceptor agonist/corticosteroid combination therapy on healthcare costs in difficult-to-control asthma

Background: Asthma is a leading, preventable cause of morbidity, mortality and cost. A disproportionate amount of the cost is generated by the 5-10%of patients with difficult-to-control asthma, who are prescribed treatment at step 4/5 of the Global Initiative for Asthma (GINA) guidelines. We have previously demonstrated a high prevalence of nonadherence to inhaled combination therapy (i.e. long-acting ß -adrenoceptor agonist [ß - agonist] and corticosteroid) in this population. The aim of this study was to examine the costs of healthcare utilization in a nonadherent group of patients with difficult-to-control asthma compared with adherent subjects. We also wished to examine potential savings if nonadherence to inhaled combination therapy could be addressed. All costs were measured from the perspective of a publicly funded health service Methods: Adherence was determined through examination of patient prescription refill behaviour and validated with a medical concordance interview. Data on healthcare use were collected from a patient survey and hospital records that included prescribed medicines, hospital admissions, intensive care unit (ICU) admissions and other unscheduled healthcare visits associated with asthma care. Activity was monetized using standard UK references and between-group comparisons based on a series of univariate and multivariate regression analyses. Results: Cost differences were identified for inhaled combination therapy, nebulizer, short acting b2-agonists and hospital costs excluding and including ICU admissions between adherent and nonadherent subjects. Compared with a group who have refractory asthma and who are adherent with medication, additional healthcare costs in nonadherent subjects are offset by the reduction in costs associated with reduced medication utilization. However, if nonadherence can be successfully targeted and hospital admissions avoided in this population, there is a potential $475 ($843-$368) saving per patient, per annum. Conclusion: Nonadherence is an important cause of difficult-to-control asthma. A uniform cost for subjects with difficult-to-control disease can be applied to economic analyses, independent of adherence, as increased healthcare utilization costs are offset by the reduced medication cost due to poor adherence. However, there are substantial potential savings in subjects with difficult-to-control asthma, who are nonadherent to inhaled combination therapy, if cost effective strategies for nonadherence are developed. © 2011 Adis Data Information BV. All rights reserved.

Predictive Factors for Local Control in Primary and Metastatic Lung Tumours after Four to Five Fraction Stereotactic Ablative Body Radiotherapy: A Single Institution's Comprehensive Experience

AIMS: We report the outcomes of a large lung stereotactic ablative body radiotherapy (SABR) programme for primary non-small cell lung cancer (NSCLC) and pulmonary metastases. The primary study aim was to identify factors predictive for local control.

MATERIALS AND METHODS: In total, 311 pulmonary tumours in 254 patients were treated between 2008 and 2011 with SABR using 48-60 Gy in four to five fractions. Local, regional and distant failure data were collected prospectively, whereas other end points were collected retrospectively. Potential clinical and dosimetric predictors of local control were evaluated using univariate and multivariate analyses.

RESULTS: Of the 311 tumours, 240 were NSCLC and 71 were other histologies. The 2 year local control rate was 96% in stage I NSCLC, 76% in colorectal cancer (CRC) metastases and 91% in non-lung/non-CRC metastases. Predictors of better local control on multivariate analysis were non-CRC tumours and a larger proportion of the planning target volume (PTV) receiving ≥100% of the prescribed dose (higher PTV V100). Among the 45 CRC metastases, a higher PTV V100 and previous chemotherapy predicted for better local control.

CONCLUSIONS: Lung SABR of 48-60 Gy/four to five fractions resulted in high local control rates for all tumours except CRC metastases. Covering more of the PTV with the prescription dose (a higher PTV V100) also resulted in superior local control.