Metacognitive learning approach for online tool condition monitoring

Mahardhika Pratama (), Eric Dimla (), Chow Yin Lai () and Edwin Lughofer ()
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Mahardhika Pratama: La Trobe University
Eric Dimla: Universiti Teknologi Brunei
Chow Yin Lai: RMIT University
Edwin Lughofer: Johannes Kepler University

Journal of Intelligent Manufacturing, 2019, vol. 30, issue 4, No 13, 1717-1737

Abstract: 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.

Keywords: Prognostic health management; Online learning; Evolving intelligent system; Lifelong learning; Nonstationary environments; Concept drifts (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s10845-017-1348-9

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