Metacognitive learning approach for online tool condition monitoring

Pratama, M, Dimla, E, Lai, C and Lughofer, E 2019, 'Metacognitive learning approach for online tool condition monitoring', Journal of Intelligent Manufacturing, vol. 30, no. 4, pp. 1717-1737.


Document type: Journal Article
Collection: Journal Articles

Title Metacognitive learning approach for online tool condition monitoring
Author(s) Pratama, M
Dimla, E
Lai, C
Lughofer, E
Year 2019
Journal name Journal of Intelligent Manufacturing
Volume number 30
Issue number 4
Start page 1717
End page 1737
Total pages 21
Publisher Springer
Abstract As manufacturing processes become increasingly automated, so should tool condition monitoring (TCM) as it is impractical to have human workers monitor the state of the tools continuously. Tool condition is crucial to ensure the good quality of products-worn tools affect not only the surface quality but also the dimensional accuracy, which means higher reject rate of the products. Therefore, there is an urgent need to identify tool failures before it occurs on the fly. While various versions of intelligent tool condition monitoring have been proposed, most of them suffer from a cognitive nature of traditional machine learning algorithms. They focus on the how-to-learn process without paying attention to other two crucial issues-what-to-learn, and when-to-learn. The what-to-learn and the when-to-learn provide self-regulating mechanisms to select the training samples and to determine time instants to train a model. A novel TCM approach based on a psychologically plausible concept, namely the metacognitive scaffolding theory, is proposed and built upon a recently published algorithm-recurrent classifier (rClass). The learning process consists of three phases: what-to-learn, how-to-learn, when-to-learn and makes use of a generalized recurrent network structure as a cognitive component. Experimental studies with real-world manufacturing data streams were conducted where rClass demonstrated the highest accuracy while retaining the lowest complexity over its counterparts.
Subject Neural, Evolutionary and Fuzzy Computation
Keyword(s) Concept drifts
Evolving intelligent system
Lifelong learning
Nonstationary environments
Online learning
Prognostic health management
DOI - identifier 10.1007/s10845-017-1348-9
Copyright notice © Springer Science and Business Media 2017
ISSN 0956-5515
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