Lifelong learning meets dynamic processes: an emerging streaming process prediction framework with delayed process output measurement
Lifelong learning meets dynamic processes: an emerging streaming process prediction framework with delayed process output measurement
As an emerging machine learning technique, lifelong learning is capable of solving multiple consecutive tasks based on previously accumulated knowledge. Although this is highly desired for streaming process prediction in industry, lifelong learning methods have so far failed to gain applications to mainstream adaptive predictive modeling of time-varying industrial processes. This is because when faced with a new data batch, existing lifelong learning approaches need both input and output data to construct local predictors before knowledge transfer can succeed. But in many process industries, the process output data are hard to measure online and it often takes time to acquire them from off-site laboratory analysis. This delayed acquisition of target output data makes it challenging to apply lifelong learning and other existing adaptive mechanisms to dynamic industrial processes with delayed process output measurement. To overcome this difficulty, this article proposes a novel lifelong learning framework that can rapidly predict new data batches with input data only before the arrival of the process output measurement. Specifically, we propose to incorporate process input information into lifelong learning via coupled dictionary learning, to enable the prediction of new batches without target output data. The input feature is linked with a local predictor through two dictionaries that are coupled by a joint sparse representation. Because of the learned coupling between the two spaces, the local predictor for the new batch can be reconstructed by knowledge transfer given only process inputs. Two industrial case studies are used to evaluate the effectiveness of our proposed framework and reveal the intrinsic learning mechanism of our lifelong process modeling to perform knowledge base (KB) adaptation.
Delayed output measurement, dynamic industrial processes, knowledge transfer, lifelong learning, process drifts
384-398
Liu, Tong
17f1a70b-449d-4078-af64-957a5b374698
Chen, Sheng
9310a111-f79a-48b8-98c7-383ca93cbb80
Yang, Po
5d8c6606-f845-4c52-8116-8d4a8cd22463
Zhu, Yunpeng
363c26ba-f671-48f6-a771-26bf881d0d1a
Mercangöz, Mehmet
3a96390e-e6a7-41b9-859d-43b109422d4c
Harris, Chris J.
daa59e88-2e26-42df-bf2e-f8e6792ecb18
1 March 2024
Liu, Tong
17f1a70b-449d-4078-af64-957a5b374698
Chen, Sheng
9310a111-f79a-48b8-98c7-383ca93cbb80
Yang, Po
5d8c6606-f845-4c52-8116-8d4a8cd22463
Zhu, Yunpeng
363c26ba-f671-48f6-a771-26bf881d0d1a
Mercangöz, Mehmet
3a96390e-e6a7-41b9-859d-43b109422d4c
Harris, Chris J.
daa59e88-2e26-42df-bf2e-f8e6792ecb18
Liu, Tong, Chen, Sheng, Yang, Po, Zhu, Yunpeng, Mercangöz, Mehmet and Harris, Chris J.
(2024)
Lifelong learning meets dynamic processes: an emerging streaming process prediction framework with delayed process output measurement.
IEEE Transactions on Control Systems Technology, 32 (2), .
(doi:10.1109/TCST.2023.3312850).
Abstract
As an emerging machine learning technique, lifelong learning is capable of solving multiple consecutive tasks based on previously accumulated knowledge. Although this is highly desired for streaming process prediction in industry, lifelong learning methods have so far failed to gain applications to mainstream adaptive predictive modeling of time-varying industrial processes. This is because when faced with a new data batch, existing lifelong learning approaches need both input and output data to construct local predictors before knowledge transfer can succeed. But in many process industries, the process output data are hard to measure online and it often takes time to acquire them from off-site laboratory analysis. This delayed acquisition of target output data makes it challenging to apply lifelong learning and other existing adaptive mechanisms to dynamic industrial processes with delayed process output measurement. To overcome this difficulty, this article proposes a novel lifelong learning framework that can rapidly predict new data batches with input data only before the arrival of the process output measurement. Specifically, we propose to incorporate process input information into lifelong learning via coupled dictionary learning, to enable the prediction of new batches without target output data. The input feature is linked with a local predictor through two dictionaries that are coupled by a joint sparse representation. Because of the learned coupling between the two spaces, the local predictor for the new batch can be reconstructed by knowledge transfer given only process inputs. Two industrial case studies are used to evaluate the effectiveness of our proposed framework and reveal the intrinsic learning mechanism of our lifelong process modeling to perform knowledge base (KB) adaptation.
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Accepted/In Press date: 28 August 2023
Published date: 1 March 2024
Additional Information:
This work was supported in part by the Innovative U.K. under Project 107462.
Publisher Copyright:
© 1993-2012 IEEE.
Keywords:
Delayed output measurement, dynamic industrial processes, knowledge transfer, lifelong learning, process drifts
Identifiers
Local EPrints ID: 481600
URI: http://eprints.soton.ac.uk/id/eprint/481600
ISSN: 1063-6536
PURE UUID: cbc81bc4-90d5-4ad7-b828-37b02079c5a2
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Date deposited: 04 Sep 2023 16:53
Last modified: 22 Apr 2024 16:49
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Contributors
Author:
Tong Liu
Author:
Sheng Chen
Author:
Po Yang
Author:
Yunpeng Zhu
Author:
Mehmet Mercangöz
Author:
Chris J. Harris
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