US 12,460,349 B2
Adaptive-learning, auto-labeling method and system for predicting and diagnosing web breaks in paper machine
Vadthyavath Ramu, Bangalore (IN); Dinesh Patil, Bangalore (IN); and Nandkishor Kubal, Pune (IN)
Assigned to ABB Schweiz AG, Baden (CH)
Filed by ABB Schweiz AG, Baden (CH)
Filed on Nov. 24, 2022, as Appl. No. 17/993,924.
Application 17/993,924 is a continuation of application No. PCT/IB2021/053173, filed on Apr. 16, 2021.
Claims priority of application No. 202041021959 (IN), filed on May 26, 2020.
Prior Publication US 2024/0102242 A1, Mar. 28, 2024
Int. Cl. D21F 7/04 (2006.01); D21G 9/00 (2006.01); G06F 30/27 (2020.01); G06V 30/194 (2022.01)
CPC D21F 7/04 (2013.01) [D21G 9/0054 (2013.01); G06F 30/27 (2020.01); G06V 30/194 (2022.01)] 17 Claims
OG exemplary drawing
 
1. A method of labelling parameters related to a paper machine to predict a break in paper web in the paper machine, wherein the parameters comprise a plurality of process parameters and a plurality of operational parameters, wherein the method is performed by a computing unit, the method comprising:
providing simulated parameters related to the paper to a plurality of machine learning models, the simulated parameters comprising normal patterns and abnormal patterns, the simulated parameters being known to have caused a break in the paper web;
configuring the plurality of machine learning models to label the simulated parameters into normal patterns and abnormal patterns, wherein the abnormal patterns are proximate to a timestamp of the break in the paper web;
receiving an output from each of the plurality of machine learning models, wherein the output is indicative of labels comprising the normal patterns and the abnormal patterns;
selecting a machine learning model from the plurality of machine learning models based on one or more performance metrics and the output of the plurality of machine learning models, and storing one or more model parameters of the selected model in a memory of the computing unit;
providing a plurality of details of the selected model to an auto-labeller independent of the machine learning model and labelling, by the auto-labelller, the historical parameters into the same normal patterns and the same abnormal patterns, the historical parameters comprising at least one of the normal patterns and the abnormal patterns, wherein the labels generated by the auto-labeller are stored as labelled data in a database;
using the labelled data for predicting a break in the paper web in real-time; and
performing one or more actions to the paper machine to control the paper machine to avoid the predicted break in the paper web.