公开(公告)号：US20190257806A1

公开(公告)日：2019-08-22

申请号：US16345850

申请日：2017-10-17

Applicant: ENDRESS+HAUSER FLOWTEC AG

Inventor： Rebecca Page ,  Peter Huggenberger ,  Stefan Wiesmeier ,  Daniel Waldmann

IPC: G01N33/18 ,  G06N3/08

Abstract: A method for automated in-line detection of deviations of an actual state of a fluid from a reference state is disclosed wherein measured values captured at the same time are evaluated in a combined manner with respect to at least three measurement variables that are different measurement quantities of the fluid and/or a measurement quantity of the fluid measured at different measuring points. The method includes creating a reference data set, wherein reference measured values are mapped to a reference vector of a vector space using a neural network; in-line measurement, wherein measured values at all times are mapped to a measurement vector using the neural network; comparing the measurement vector with the reference vectors using a kernel density estimator of a predefinable window width; and creating an assessment with respect to a deviation of the actual state from the reference state on the basis of the kernel density estimator.

公开(公告)号：US11193920B2

公开(公告)日：2021-12-07

申请号：US16345850

申请日：2017-10-17

Applicant: Endress+Hauser Flowtec AG

Inventor： Rebecca Page ,  Peter Huggenberger ,  Stefan Wiesmeier ,  Daniel Waldmann

IPC: G01N33/18 ,  G06N3/08 ,  G05B13/02 ,  G16H20/10

Abstract: A method for automated in-line detection of deviations of an actual state of a fluid from a reference state is disclosed wherein measured values captured at the same time are evaluated in a combined manner with respect to at least three measurement variables that are different measurement quantities of the fluid and/or a measurement quantity of the fluid measured at different measuring points. The method includes creating a reference data set, wherein reference measured values are mapped to a reference vector of a vector space using a neural network; in-line measurement, wherein measured values at all times are mapped to a measurement vector using the neural network; comparing the measurement vector with the reference vectors using a kernel density estimator of a predefinable window width; and creating an assessment with respect to a deviation of the actual state from the reference state on the basis of the kernel density estimator.