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.

Labor and Place: The Contours of Freedpeoples' Mobilization in Reconstruction South Carolina

Important advances in scholarship on the post-emancipation South have made possible a new synthesis that moves beyond broad generalizations about African American agency to identify both the shared elements in black life across the region and the varying capacity of freedpeople to assert their interests in the face of white hostility. Building on a number of recent studies of Reconstruction this article seeks to demonstrate that the varying capacity of freedpeople in South Carolina to shape and defend the new society that would emerge after the end of slavery was rooted in their relative strength at work and in their communities. In Charleston and its lowcountry rural hinterland, demographic strength combined with deeply-rooted traditions of collective assertion to sustain a remarkably vibrant grassroots movement that persisted beyond the overthrow of Reconstruction. From very early on, by contrast, former slaves dispersed across the rural interior found their freedom severely circumscribed by a bellicose and heavily-armed white paramilitary campaign.

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.