Statistical-based monitoring of multivariate non-Gaussian systems

The monitoring of multivariate systems that exhibit non-Gaussian behavior is addressed. Existing work advocates the use of independent component analysis (ICA) to extract the underlying non-Gaussian data structure. Since some of the source signals may be Gaussian, the use of principal component analysis (PCA) is proposed to capture the Gaussian and non-Gaussian source signals. A subsequent application of ICA then allows the extraction of non-Gaussian components from the retained principal components (PCs). A further contribution is the utilization of a support vector data description to determine a confidence limit for the non-Gaussian components. Finally, a statistical test is developed for determining how many non-Gaussian components are encapsulated within the retained PCs, and associated monitoring statistics are defined. The utility of the proposed scheme is demonstrated by a simulation example, and the analysis of recorded data from an industrial melter.

Moving window kernel PCA for adaptive monitoring of nonlinear processes

This paper discusses the monitoring of complex nonlinear and time-varying processes. Kernel principal component analysis (KPCA) has gained significant attention as a monitoring tool for nonlinear systems in recent years but relies on a fixed model that cannot be employed for time-varying systems. The contribution of this article is the development of a numerically efficient and memory saving moving window KPCA (MWKPCA) monitoring approach. The proposed technique incorporates an up- and downdating procedure to adapt (i) the data mean and covariance matrix in the feature space and (ii) approximates the eigenvalues and eigenvectors of the Gram matrix. The article shows that the proposed MWKPCA algorithm has a computation complexity of O(N2), whilst batch techniques, e.g. the Lanczos method, are of O(N3). Including the adaptation of the number of retained components and an l-step ahead application of the MWKPCA monitoring model, the paper finally demonstrates the utility of the proposed technique using a simulated nonlinear time-varying system and recorded data from an industrial distillation column.

Effective monitoring for ractopamine residues in samples of animal origin by SPR biosensor and mass spectrometry

Ractopamine (RCT) is a member of the beta-2-agonist (beta-agonist) family. It is licensed for use as an animal growth promoter in more than 20 countries worldwide, including the United States and Canada, but is either not licensed or prohibited by over 150 others, including those within the European Union. The issue of the use of RCT in livestock bound for human consumption has risen to prominence recently following the decision by The People's Republic of China to ban the import of pork from a number of processing plants after finding traces of RCT in shipments from the U.S.A.

Developing a simple, rapid method for identifying and monitoring jellyfish aggregations from the air

Within the marine environment, aerial surveys have historically centred on apex predators, such as pinnipeds, cetaceans and sea birds. However, it is becoming increasingly apparent that the utility of this technique may also extend to subsurface species such as pre-spawning fish stocks and aggregations of jellyfish that occur close to the surface. In light of this, we tested the utility of aerial surveys to provide baseline data for 3 poorly understood scyphozoan jellyfish found throughout British and Irish waters: Rhizostoma octopus, Cyanea capillata and Chrysaora hysoscella. Our principal objectives were to develop a simple sampling protocol to identify and quantify surface aggregations, assess their consistency in space and time, and consider the overall applicability of this technique to the study of gelatinous zooplankton. This approach provided a general understanding of range and relative abundance for each target species, with greatest suitability to the study of R. octopus. For this species it was possible to identify and monitor extensive, temporally consistent and previously undocumented aggregations throughout the Irish Sea, an area spanning thousands of square kilometres. This finding has pronounced implications for ecologists and fisheries managers alike and, moreover, draws attention to the broad utility of aerial surveys for the study of gelatinous aggregations beyond the range of conventional ship-based techniques.

Effect of a pharmacist-led multicomponent intervention focussing on the medication monitoring phase to prevent potential adverse drug events in nursing homes

OBJECTIVES: To determine the extent to which the use of a clinical informatics tool that implements prospective monitoring plans reduces the incidence of potential delirium, falls, hospitalizations potentially due to adverse drug events, and mortality.

DESIGN: Randomized cluster trial.

SETTING: Twenty-five nursing homes serviced by two long-term care pharmacies.

PARTICIPANTS: Residents living in nursing homes during 2003 (1,711 in 12 intervention; 1,491 in 13 usual care) and 2004 (1,769 in 12 intervention; 1,552 in 13 usual care).

INTERVENTION: The pharmacy automatically generated Geriatric Risk Assessment MedGuide (GRAM) reports and automated monitoring plans for falls and delirium within 24 hours of admission or as part of the normal time frame of federally mandated drug regimen review.

MEASUREMENTS: Incidence of potential delirium, falls, hospitalizations potentially due to adverse drug events, and mortality.

RESULTS: GRAM triggered monitoring plans for 491 residents. Newly admitted residents in the intervention homes experienced a lower rate of potential delirium onset than those in usual care homes (adjusted hazard ratio (HR)=0.42, 95% confidence interval (CI)=0.35–0.52), overall hospitalization (adjusted HR=0.89, 95% CI=0.72–1.09), and mortality (adjusted HR=0.88, 95% CI=0.66–1.16). In longer stay residents, the effects of the intervention were attenuated, and all estimates included unity.

CONCLUSION: Using health information technology in long-term care pharmacies to identify residents who might benefit from the implementation of prospective medication monitoring care plans when complex medication regimens carry potential risks for falls and delirium may reduce adverse effects associated with appropriate medication use.

Home-Based Monitoring and Assessment of Parkinson's Disease

As a clinically complex neurodegenerative disease, Parkinson's disease (PD) requires regular assessment and close monitoring. In our current study, we have developed a home-based tool designed to monitor and assess peripheral motor symptoms. An evaluation of the tool was carried out over a period of ten weeks on ten people with idiopathic PD. Participants were asked to use the tool twice daily over four days, once when their medication was working at its best (

Improved Nonlinear PCA for Process Monitoring Using Support Vector Data Description

Nonlinear principal component analysis (PCA) based on neural networks has drawn significant attention as a monitoring tool for complex nonlinear processes, but there remains a difficulty with determining the optimal network topology. This paper exploits the advantages of the Fast Recursive Algorithm, where the number of nodes, the location of centres, and the weights between the hidden layer and the output layer can be identified simultaneously for the radial basis function (RBF) networks. The topology problem for the nonlinear PCA based on neural networks can thus be solved. Another problem with nonlinear PCA is that the derived nonlinear scores may not be statistically independent or follow a simple parametric distribution. This hinders its applications in process monitoring since the simplicity of applying predetermined probability distribution functions is lost. This paper proposes the use of a support vector data description and shows that transforming the nonlinear principal components into a feature space allows a simple statistical inference. Results from both simulated and industrial data confirm the efficacy of the proposed method for solving nonlinear principal component problems, compared with linear PCA and kernel PCA.

The inferential monitoring of the screw disturbance torque to predict process fluctuations in polymer extrusion

Polymer extrusion is one of the major methods of processing polymer materials and advanced process monitoring is important to ensure good product quality. However, commonly used process monitoring devices, e.g. temperature and pressure sensors, are limited in providing information on process dynamics inside an extruder barrel. Screw load torque dynamics, which may occur due to changes in solids conveying, melting, mixing, melt conveying, etc., are believed to be a useful indicator of process fluctuations inside the extruder barrel. However, practical measurement of the screw load torque is difficult to achieve. In this work, inferential monitoring of the screw load torque signal in an extruder was shown to be possible by monitoring the motor current (armature and/or field) and simulation studies were used to check the accuracy of the proposed method. The ability of this signal to aid identification and diagnosis of process issues was explored through an experimental investigation. Power spectral density and wavelet frequency analysis were implemented together with a covariance analysis. It was shown that the torque signal is dominated by the solid friction in the extruder and hence it did not correlate well with melting fluctuations. However, it is useful for online identification of solids conveying issues.