Search

Abstract: Projection to latent structures or partial least squares (PLS) produces output-supervised decomposition on input X, while principal component analysis (PCA) produces unsupervised decomposition of input X. In this paper, the effect of output Y on the X-space decomposition in PLS is analyzed and geometric properties of the PLS structure are revealed. Several PLS algorithms are compared in a geometric way for the purpose of process monitoring. A numerical example and a case study are given to illustrate the analysis results. [Copyright &y& Elsevier]

Abstract: This paper presents a new method to perform fault diagnosis for data-correlation based process monitoring. As an alternative to the traditional contribution plot method, a reconstruction-based contribution for fault diagnosis is proposed based on monitored indices, SPE, and a combined index . Analysis of the diagnosability of the traditional contributions and the reconstruction-based contributions is performed. The lack of diagnosability of traditional contributions is analyzed for the case of single sensor faults with large fault magnitudes, whereas for the same case the proposed reconstruction-based contributions guarantee correct diagnosis. Monte Carlo simulation results are provided for the case of modest fault magnitudes by randomly assigning fault sensors and fault magnitudes. [Copyright &y& Elsevier]

Abstract: It is known that many subspace algorithms give biased estimates for closed-loop data due to the existence of feedback. In this paper we present a new subspace identification method using the parity space employed in fault detection in the past. The basic algorithm, known as subspace identification method via principal component analysis (SIMPCA), gives consistent estimation of the deterministic part and stochastic part of the system under closed loop. Column weighting for SIMPCA is introduced which shows improved efficiency/accuracy. A simulation example is given to illustrate the performance of the proposed algorithm in closed-loop identification and the effect of column weighting. [Copyright &y& Elsevier]

Abstract: Subspace identification methods (SIMs) for estimating state-space models have been proven to be very useful and numerically efficient. They exist in several variants, but all have one feature in common: as a first step, a collection of high-order ARX models are estimated from vectorized input–output data. In order not to obtain biased estimates, this step must include future outputs. However, all but one of the submodels include non-causal input terms. The coefficients of them will be correctly estimated to zero as more data become available. They still include extra model parameters which give unnecessarily high variance, and also cause bias for closed-loop data. In this paper, a new model formulation is suggested that circumvents the problem. Within the framework, the system matrices and Markov parameters can be estimated separately. It is demonstrated through analysis that the new methods generally give smaller variance in the estimate of the observability matrix and it is supported by simulation studies that this gives lower variance also of the system invariants such as the poles. [Copyright &y& Elsevier]

A novel control framework for batch and repetitive processes is proposed. The currently practiced methods to combine real-time feedback control (RFC) with iterative learning control (ILC) share a problem that RFC causes ILC to digress from its convergence track along the run index when there occur real-time disturbances. The proposed framework provides a pertinent means to incorporate RFC into ILC so that the performance of ILC is virtually separated from the effects of real-time disturbances. As a prototypical algorithm, a two-stage algorithm has been devised by modifying and combining the existing QILC and BMPC techniques. [Copyright &y& Elsevier]