Integrated Multiscale Latent Variable Regression and Application to Distillation Columns

Proper control of distillation columns requires estimating some key variables that are challenging to measure online (such as compositions), which are usually estimated using inferential models. Commonly used inferential models include latent variable regression (LVR) techniques, such as principal component regression (PCR), partial least squares (PLS), and regularized canonical correlation analysis (RCCA). Unfortunately, measured practical data are usually contaminated with errors, which degrade the prediction abilities of inferential models. Therefore, noisy measurements need to be filtered to enhance the prediction accuracy of these models. Multiscale filtering has been shown to be a powerful feature extraction tool. In this work, the advantages of multiscale filtering are utilized to enhance the prediction accuracy of LVR models by developing an integrated multiscale LVR (IMSLVR) modeling algorithm that integrates modeling and feature extraction. The idea behind the IMSLVR modeling algorithm is to filter the process data at different decomposition levels, model the filtered data from each level, and then select the LVR model that optimizes a model selection criterion. The performance of the developed IMSLVR algorithm is illustrated using three examples, one using synthetic data, one using simulated distillation column data, and one using experimental packed bed distillation column data. All examples clearly demonstrate the effectiveness of the IMSLVR algorithm over the conventional methods

A data-based technique for monitoring of wound rotor induction machines: A simulation study

Detecting faults induction machines is crucial for a safe operation of these machines. The aim of this paper is to present a statistical fault detection methodology for the detection of faults in three-phase wound rotor induction machines (WRIM). The proposed fault detection approach is based on the use of principal components analysis (PCA). However, conventional PCA-based detection indices, such as the T2 and the Q statistics, are not well suited to detect small faults because these indices only use information from the most recent available samples. Detection of small faults is one of the most crucial and challenging tasks in the area of fault detection and diagnosis. In this paper, a new statistical system monitoring strategy is proposed for detecting changes resulting from small shifts in several variables associated with WRIM. The proposed approach combines modeling using PCA modeling with the exponentially weighted moving average (EWMA) control scheme. In the proposed approach, EWMA control scheme is applied on the ignored principal components to detect the presence of faults. The performance of the proposed method is compared with those of the traditional PCA-based fault detection indices. The simulation results clearly show the effectiveness of the proposed method over the conventional ones, especially in the presence of faults with small magnitudes

On-Line Multiscale Filtering of Random and Gross Errors without Process Models

This paper presents a technique for on-line nonlinear filtering based on wavelet thresholding. Online multiscale (OLMS) rectification applies wavelet thresholding to data in a moving window of dyadic length to remove random errors. Gross errors are removed by combining wavelet thresholding with multiscale median filtering. Theoretical analysis shows that OLMS rectification using Haar wavelets subsumes mean filters of dyadic length, while rectification with smoother boundary corrected wavelets is analogous to adaptive exponential smoothing. If the rectified measurements are not needed on-line, the quality of rectification can be further improved by averaging the rectified signals in each window. The resulting approach overcomes the boundary effects encountered in translation invariant (TI) rectification of Coifman and Donoho (1995), and is called boundary corrected translation invariant (BCTI) rectification. Examples based on synthetic and industrial data demonstrate the benefits of the on-line multiscale and boundary corrected translation invariant rectification methods. ___________________________________________________________________________________ Correspondence to this article should be addressed to Bhavik R. Bakshi