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.

Neural modelling, control and optimisation of an industrial grinding process

This paper describes the development of neural model-based control strategies for the optimisation of an industrial aluminium substrate disk grinding process. The grindstone removal rate varies considerably over a stone life and is a highly nonlinear function of process variables. Using historical grindstone performance data, a NARX-based neural network model is developed. This model is then used to implement a direct inverse controller and an internal model controller based on the process settings and previous removal rates. Preliminary plant investigations show that thickness defects can be reduced by 50% or more, compared to other schemes employed. (c) 2004 Elsevier Ltd. All rights reserved.

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.

Application of poly(ethylene glycol)-based aqueous biphasic systems as reaction and reactive extraction media

Poly(ethylene glycol)-based aqueous biphasic systems (PEG-ABSs) have been investigated as tunable reaction media, in the example presented here, to control the oxidation of cyclohexene to adipic acid with hydrogen peroxide. The production of adipic acid was found to increase from the monophasic to the biphasic regimes, was greatest at short tie-line lengths (close to the system's critical point), and demonstrates how control of the ABS media, through changes in system composition, PEG, salt, and tie-line length, can be used to readily tune and control reactivity and product isolation in these aqueous biphasic reactive extraction systems. Challenges in using this system, including possible oxidation reactions of the PEG-OH end groups, are also discussed.

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.

The determinants of performance appraisal systems: A note (Do Brown and Heywood's results for Australia hold up for Britain?)

This article offers a replication for Britain of Brown and Heywood's analysis of the determinants of performance appraisal in Australia. Although there are some important limiting differences between our two datasets - the Australia Workplace Industrial Relations Survey (AWIRS) and the Workplace Employment Relations Survey (WERS) - we reach one central point of agreement and one intriguing shared insight. First, performance appraisal is negatively associated with tenure: where employers cannot rely on the carrot of deferred pay or the stick of dismissal to motivate workers, they will tend to rely more on monitoring, ceteris paribus. Second, employer monitoring and performance pay may be complementary. However, consonant with the disparate results from the wider literature, there is more modest agreement on the contribution of specific human resource management practices, and still less on the role of job control.

Exploring the adoption and implementation of discrete and integrated IT systems: A Framework for analysis

Despite the substantial organisational benefits of integrated IT, the implementation of such systems – and particularly Enterprise Resource Planning (ERP) systems – has tended to be problematic, stimulating an extensive body of research into ERP implementation. This research has remained largely separate from the main IT implementation literature. At the same time, studies of IT implementation have generally adopted either a factor or process approach; both have major limitations. To address these imitations, factor and process perspectives are combined here in a unique model of IT implementation. We argue that • the organisational factors which determine successful implementation differ for integrated and traditional, discrete IT • failure to manage these differences is a major source of integrated IT failure. The factor/process model is used as a framework for proposing differences between discrete and integrated IT.

Firms and Innovative Conflict Management Systems in Ireland

This article examines the practices adopted by firms in the Republic of Ireland to manage conflict involving groups of employees, focusing in particular on the uptake of 'alternative dispute resolution practices'. The article reveals that conflict management practices take the form of 'systems', and it estimates the incidence of innovative conflict management systems, incorporating alternative dispute resolution (ADR) practices, as involving about 30 per cent of firms. A series of influences are shown to be associated with innovative, ADR-based conflict management systems, especially commitment-oriented HRM practices and whether unions are recognized. © Blackwell Publishing Ltd/London School of Economics 2010.