Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/154226
Title: Online tool condition monitoring based on parsimonious ensemble
Authors: Pratama, Mahardhika 
Dimla, Eric
Tjahjowidodo, Tegoeh
Pedrycz, Witold
Lughofer, Edwin
Keywords: Engineering::Computer science and engineering
Issue Date: 2020
Source: Pratama, M., Dimla, E., Tjahjowidodo, T., Pedrycz, W. & Lughofer, E. (2020). Online tool condition monitoring based on parsimonious ensemble. IEEE Transactions On Cybernetics, 50(2), 664-677. https://dx.doi.org/10.1109/TCYB.2018.2871120
Project: RG130/17
Journal: IEEE Transactions on Cybernetics
Abstract: Existing methodologies for tool condition monitoring (TCM) still rely on batch approaches which cannot cope with a fast sampling rate of a metal cutting process. Furthermore, they require a retraining process to be completed from scratch when dealing with a new set of machining parameters. This paper presents an online TCM approach based on Parsimonious Ensemble+ (pENsemble+). The unique feature of pENsemble+ lies in its highly flexible principle where both the ensemble structure and base-classifier structure can automatically grow and shrink on the fly based on the characteristics of data streams. Moreover, the online feature selection scenario is integrated to actively sample relevant input attributes. This paper presents advancement of a newly developed ensemble learning algorithm, pENsemble, where the online active learning scenario is incorporated to reduce the operator's labeling effort. The ensemble merging scenario is proposed which allows reduction of ensemble complexity while retaining its diversity. Experimental studies utilizing two real-world manufacturing data streams: 1) metal turning and 2) 3-D-printing processes and comparisons with well-known algorithms were carried out. Furthermore, the efficacy of pENsemble+ was examined using benchmark concept drift data streams. It has been found that pENsemble+ incurs low structural complexity and results in a significant reduction of the operator's labeling effort.
URI: https://hdl.handle.net/10356/154226
ISSN: 2168-2267
DOI: 10.1109/TCYB.2018.2871120
DOI (Related Dataset): 10.21979/N9/GGBFMO
Rights: © 2018 IEEE. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
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