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.

High tolerance to gate misalignment in low voltage gate-underlap double gate MOSFETs

.In this letter, we demonstrate for the first time that gate misalignment is not a critical limiting factor for low voltage operation in gate-underlap double gate (DG) devices. Our results show that underlap architecture significantly extends the tolerable limit of gate misalignment in 25 nm devices. DG MOSFETs with high degree of gate misalignment and optimal gate-underlap design can perform comparably or even better than self-aligned nonunderlap devices. Results show that spacer-to-straggle (s/sigma) ratio, a key design parameter for underlap devices, should be within the range of 2.3-3.0 to accommodate back gate misalignment. These results are very significant as the stringent process control requirements for achieving self-alignment in nanoscale planar DG MOSFETs are considerably relaxed

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.