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<p>Due to physical and monetary constraints, quality-related variables in the process industries are generally difficult to measure using hardware sensors. Therefore, they are often not available as frequently as the other variables since obtaining them typically via laboratory analysis are time-consuming. On the other hand, the dynamic nature of easy-to-measure process data contains valuable predictive information about the quality variables. Consequently, modelling sequential data is beneficial in building a soft sensor that can predict the quality variable. Several techniques were proposed to extract dynamic features as measured data often suffers from noise and collinearity. This paper presents a semi-supervised oscillating slow feature inference network to address certain shortcomings of the existing solutions. The proposed learning algorithm uses a gated recurrent unit to learn a combined inference network for missing quality variable imputation and oscillating slow feature extraction from nonlinear process data. We evaluate the efficacy of the proposed methodology on a simulated and an industrial process.</p>

<p>Due to physical and monetary constraints, quality-related variables in the process industries are generally difficult to measure using hardware sensors. Therefore, they are often not available as frequently as the other variables since obtaining them typically via laboratory analysis are time-consuming. On the other hand, the dynamic nature of easy-to-measure process data contains valuable predictive information about the quality variables. Consequently, modelling sequential data is beneficial in building a soft sensor that can predict the quality variable. Several techniques were proposed to extract dynamic features as measured data often suffers from noise and collinearity. This paper presents a semi-supervised oscillating slow feature inference network to address certain shortcomings of the existing solutions. The proposed learning algorithm uses a gated recurrent unit to learn a combined inference network for missing quality variable imputation and oscillating slow feature extraction from nonlinear process data. We evaluate the efficacy of the proposed methodology on a simulated and an industrial process.</p>

<p>Due to physical and monetary constraints, quality-related variables in the process industries are generally difficult to measure using hardware sensors. Therefore, they are often not available as frequently as the other variables since obtaining them typically via laboratory analysis are time-consuming. On the other hand, the dynamic nature of easy-to-measure process data contains valuable predictive information about the quality variables. Consequently, modelling sequential data is beneficial in building a soft sensor that can predict the quality variable. Several techniques were proposed to extract dynamic features as measured data often suffers from noise and collinearity. This paper presents a semi-supervised oscillating slow feature inference network to address certain shortcomings of the existing solutions. The proposed learning algorithm uses a gated recurrent unit to learn a combined inference network for missing quality variable imputation and oscillating slow feature extraction from nonlinear process data. We evaluate the efficacy of the proposed methodology on a simulated and an industrial process.</p>