Shinji Hasebe

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Univariate and multivariate statistical process control (USPC and MSPC) methods have been widely used in process industries for fault detection. However, their practicability and achievable performance are limited due to the assumptions that a process is operated in a steady state and that variables are normally distributed. In the present work, external(More)
Input variable scaling is one of the most important steps in statistical modeling. However, it has not been actively investigated, and autoscaling is mostly used. This paper proposes two input variable scaling methods for improving the accuracy of soft sensors. One method statistically derives the input variable scaling factors; the other one uses(More)
The current issues concerning soft-sensors are how to cope with changes in process characteristics and how to cope with parallelized, slightly different, multiple processes. To make soft-sensors adaptive and flexible, the development of practical design techniques, instead of impracticable ideas, is crucial; this is the motivation of the present research.(More)
— Softsensor design is one of 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(More)
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