Data augmentation to improve the performance of ensemble learning for system failure prediction with limited observations

Shi, Guo and Liu, Bin and Walls, Lesley; (2022) Data augmentation to improve the performance of ensemble learning for system failure prediction with limited observations. In: 2022 13th International Conference on Reliability, Maintainability, and Safety (ICRMS). 13th International Conference on Reliability, Maintainability, and Safety: Reliability and Safety of Intelligent Systems, ICRMS 2022 . IEEE, Piscataway, NJ, pp. 296-300. ISBN 9781665486903 (https://doi.org/10.1109/ICRMS55680.2022.9944577)

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Abstract

Ensemble learning has been widely used to improve the performance and robustness of machine learning algorithms on time series data. However, in real operational processes where the observed data is limited, it hinders the capability of ensemble learning algorithms. To address the challenge of limited observed data, this paper proposes a novel three-layer ensemble learning framework by use of data augmentation. Firstly, multiple classical time series augmentation methods are applied to increase the size of the data set. Subsequently, after pre-processing, these augmented data is trained by multiple basic learners with K-fold cross-validation as the first layer of the developed ensemble learning framework. The outputs of the first layer are integrated via LASSO to further improve the prediction performance, which serves as the second layer of the developed framework. Finally, the third-layer output is generated by averaging the prediction of the second layer and the output from an improved Long-Short Term Memory model that provides prediction based on the augmented data. A case study on a real wastewater treatment plant is used to illustrate the effectiveness of the proposed method.

  • Item type:Book Section
    ID code:83382
    Dates:
    Date
    Event
    15 November 2022
    Published
    10 June 2022
    Accepted
    Notes:© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
    Subjects:Social Sciences > Industries. Land use. Labor > Management. Industrial Management
    Department:Strathclyde Business School > Management Science
    Depositing user: Pure Administrator
    Date deposited:01 Dec 2022 11:30
    Last modified:19 Apr 2024 00:07
    URI:https://strathprints.strath.ac.uk/id/eprint/83382
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