Outlier Detection for Soft-Sensor Modeling Data Based on k-Nearest Neighbor

Qiang Da Yang; Zhen Quan Liu

doi:10.4028/www.scientific.net/AMR.468-471.2504

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 468-471Outlier Detection for Soft-Sensor Modeling Data...

Outlier Detection for Soft-Sensor Modeling Data Based on k-Nearest Neighbor

Abstract:

The on-line estimation of some key hard-to-measure process variables by using soft-sensor technique has received extensive concern in industrial production process. The precision of on-line estimation is closely related to the accuracy of soft-sensor model, while the accuracy of soft-sensor model depends strongly on the accuracy of modeling data. Aiming at the special character of the definition for outliers in soft-sensor modeling process, an outlier detection method based on k-nearest neighbor (k-NN) is proposed in this paper. The proposed method can be realized conveniently from data without priori knowledge and assumption of the process. The simulation result and practical application show that the proposed outlier detection method based on k-NN has good detection effect and high application value.