Adaptive-learning, auto-labeling method and system for predicting and diagnosing web breaks in paper machine
AssigneeABB Schweiz AG
InventorsVadthyavath Ramu, Dinesh Patil, Nandkishor Kubal
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Abstract
A system and method for labelling normal and abnormal regions in data related to a paper machine for web break prediction and labelling individual parameters for root cause analysis, using machine learning models, includes using the machine learning models in real-time to predict breaks in the paper web, analyzing root cause for the breaks in the paper web, and estimating a time to break. An auto-data-labeling framework helps in adaptive learning for autonomous model improvement of the deployed model, transfer learning, shortlisting parameters and automating feasibility study.
Claims17
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.
A method of predicting a break in a paper web in a paper machine, wherein a plurality of sensors are used to monitor parameters related to the paper machine, wherein the parameters comprise a plurality of process parameters and a plurality of operating parameters, wherein a paper web is formed in the paper machine during manufacturing of paper, wherein the method is performed by a computing unit, the method comprising:
- receiving the parameters from the plurality of sensors;
- determining a pattern in a variation of each parameter, with respect to time;
- comparing the determined pattern with a corresponding expected pattern;
- and
- predicting a break in the paper web based on the comparison;
- wherein the corresponding expected pattern is generated using a selected trained machine learning model from a plurality of machine learning models,
- wherein training of the plurality of machine learning models comprises:
- providing labelled data comprising normal patterns and abnormal patterns of the parameters, to the plurality of machine learning models,
- wherein a timestamp associated with the abnormal values is proximate to a timestamp of the break in the paper web,
- wherein the labelled data is generated using an auto-labeller independent of the plurality of machine learning models using simulated parameters and historical parameters related to the paper machine to label the historical parameters into the same normal patterns and the same abnormal patterns;
- configuring the plurality of machine learning models to detect patterns in the parameters and determine the detected patterns into at least one of normal patterns and abnormal patterns;
- configuring the plurality of machine learning models to generate the expected pattern for each parameter based on the determined patterns, wherein each expected pattern comprises at least one of, normal pattern and abnormal pattern;
- receiving an output from each of the plurality of machine learning models, wherein the output is indicative of a prediction of a break in the paper web based on a comparison of the labelled data with corresponding expected patterns;
- selecting a machine learning model from the plurality of machine learning models, based on the output of the plurality machine learning models, as the selected trained machine learning model;
- and
- performing one or more actions to the paper machine to control the paper machine to avoid the predicted break in the paper web.
Dependencies are inferred from the claim text.