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Towards an active learning approach to tool condition monitoring with Bayesian deep learning

Martinez Arellano, Giovanna; Ratchev, Svetan


Cripps Professor of Production Engineering & Head of Research Division



With the current advances in the Internet of Things (IoT), smart sensors and Artificial Intelligence (AI), a new generation of condition monitoring solutions for smart manufacturing is starting to emerge. Computer Numerical Control (CNC) machines can now be sensorised and the vast amount of data generated can be processed using Machine Learning (ML) techniques. These can provide insights about the condition of the machine or tool in real-time, which can then be used by decision makers. This is fundamental in order to reach a new level of manufacturing capabilities in the context of Industry 4.0 (Lasi et al, 2014). Most current monitoring solutions rely on the off-line generation of models before they can be used online. This is not ideal when the data holds complex evolving features. There is a lack of approaches that are capable of determining what to learn and when to learn. This paper presents preliminary results on a new deep learning approach based on Bayesian Convolutional Neural Networks (BCNN) for online tool condition classification. Based on the uncertainty of the model, the proposed approach can determine using an entropy acquisition function if the incoming data cannot be classified, and therefore needs to be labelled and used for re-training. This constitutes the first step towards an online active learning tool condition monitoring approach. We demonstrate using a machine tool data set that the active learning approach can achieve similar accuracy of a deterministic Convolutional Neutral Network (CNN) with a smaller training data set.


Martinez Arellano, G., & Ratchev, S. (2019). Towards an active learning approach to tool condition monitoring with Bayesian deep learning

Conference Name ECMS 2019: 33rd International ECMS Conference on Modelling and Simulation
Start Date Jun 11, 2019
End Date Jun 14, 2019
Acceptance Date Mar 25, 2019
Publication Date Jun 14, 2019
Deposit Date May 21, 2019
Publicly Available Date May 24, 2019
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