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Lookup NU author(s): Dr Zepeng LiuORCiD
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
© 2024 The Author(s)Feature selection plays a vital role in improving the efficiency and accuracy of condition monitoring by constructing sparse but effective models. In this study, an advanced feature selection algorithm named the local regularization assisted split augmented Lagrangian shrinkage algorithm (LR-SALSA) is proposed. The feature selection is realized by solving a l1-norm optimization problem, which usually selects more sparse and representative features at less computational costs. The proposed algorithm operates in two stages, namely variable selection and coefficient estimation. In the stage of variable selection, the primal problem is converted into three subproblems which can be solved separately. Then individual penalty parameters are applied to every coefficient of the model when dealing with the first subproblem. Under the Bayesian evidence framework, an iterative algorithm is derived to optimize these hyperparameters. During the optimization process, redundant variables will be pruned to guarantee model sparsity and improve computational efficiency at the same time. In the second stage, the coefficients for the selected model terms are determined using the least squares technique. The superior performance and efficiency of the proposed LR-SALSA method are validated through two numerical examples and a real-world cutting tool wear prediction case study. Compared with the existing methods, the proposed method can generate a sparse model and ensure a good trade-off between estimation accuracy and computational efficiency.
Author(s): Gui Y, Tang X, Liu Z
Publication type: Article
Publication status: Published
Journal: Control Engineering Practice
Year: 2024
Volume: 147
Print publication date: 01/06/2024
Online publication date: 22/03/2024
Acceptance date: 18/03/2024
Date deposited: 10/04/2024
ISSN (print): 0967-0661
ISSN (electronic): 1873-6939
Publisher: Elsevier Ltd
URL: https://doi.org/10.1016/j.conengprac.2024.105923
DOI: 10.1016/j.conengprac.2024.105923
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