Development of a new soft sensor method using independent component analysis and partial least squares

Abstract

in Wiley InterScience (www.interscience.wiley.com).Soft sensors are used widely to estimate a process variable which is difficult to measure online. One of the crucial difficulties of soft sensors is that predictive accuracy drops due to changes of state of chemical plants. To cope with this problem, a regression model can be updated. However, if the model is updated with an abnormal sample, the predictive ability can deteriorate. We have applied the independent component analysis (ICA) method to the soft sensor to increase fault detection ability. Then, we have tried to increase the predictive accuracy. By using the ICA-based fault detection and classification model, the objective variable can be predicted, updating the PLS model appropriately. We analyzed real industrial data as the application of the proposed method. The proposed method achieved higher predictive accuracy than the traditional one. Furthermore, the nonsteady state could be detected as abnormal correctly by the ICA model.

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“…An inferential model is constructed between objective variable which is difficult to measure online and process variables which are easy to measure online. (Facco et al, 2009;Kadlec et al, 2009;Kaneko et al, 2009;.…”

Improvement of Process State Recognition Performance by Noise Reduction with Smoothing Methods

“…A process fault is detected when one of the T 2 or Q statistics based on PCA exceeds some threshold. Partial least squares (PLS) and independent component analysis (Kaneko et al, 2009) have also been applied to MSPC. When there is a nonlinear relationship between process variables and the data distribution is multimodal, nonlinear MSPC methods such as kernel PCA (Lee et al, 2004), kernel PLS (Godoy et al, 2014), generative topographic mapping (Escobar et al, 2015), and the one-class support vector machine (OCSVM) (Mahadevan and Shah, 2009) are more effective than linear approaches.…”