IMPROVING ESTIMATION PERFORMANCE OF SOFTSENSORS THROUGH TWO-STAGE SUBSPACE IDENTIFICATION

Abstract

Softsensors or virtual sensors are among the key technologies in industry, because important variables such as product quality are not always measured on-line. Therefore, to reduce off-specification products and enhance productivity, the development of an accurate softsensor is crucial. In the present work, two-stage subspace identification (SSID) is proposed to develop highly accurate softsensors that can take into account the influence of unmeasured disturbances on estimated key variables. The two-stage SSID procedure is as follows: 1) identify a state space model by using measured input and output variables, 2) estimate unmeasured disturbance variables from residual variables, and 3) identify a state space model to estimate key variables from the estimated disturbance variables and the other measured input variables. The proposed two-stage SSID can estimate unmeasured disturbances without the assumptions that the conventional Kalman filtering technique must make. Thus it can outperform the Kalman filtering technique when innovations are not Gaussian white noises or the characteristics of disturbances do not stay constant with time. The superiority of the proposed method over the conventional method is demonstrated through a numerical example.