Digital Library of the European Council for Modelling and Simulation

Title:	Towards An Active Learning Approach To Tool Condition Monitoring With Bayesian Deep Learning
Authors:	Giovanna Martinez-Arellano, Svetan Ratchev
Published in:	(2019). ECMS 2019 Proceedings Edited by: Mauro Iacono, Francesco Palmieri, Marco Gribaudo, Massimo Ficco, European Council for Modeling and Simulation. DOI: http://doi.org/10.7148/2019 ISSN: 2522-2422 (ONLINE) ISSN: 2522-2414 (PRINT) ISSN: 2522-2430 (CD-ROM) 33rd International ECMS Conference on Modelling and Simulation, Caserta, Italy, June 11^th – June 14^th, 2019
Citation format:	Giovanna Martinez-Arellano, Svetan Ratchev (2019). Towards An Active Learning Approach To Tool Condition Monitoring With Bayesian Deep Learning, ECMS 2019 Proceedings Edited by: Mauro Iacono, Francesco Palmieri, Marco Gribaudo, Massimo Ficco European Council for Modeling and Simulation. doi: 10.7148/2019-0223
DOI:	https://doi.org/10.7148/2019-0223
Abstract:	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.
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