Towards An Active Learning Approach To Tool Condition Monitoring With Bayesian Deep Learning

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.

A Review on AI for Smart Manufacturing: Deep Learning Challenges and Solutions

“…Kirsch et al propose BatchBALD, which extends a single sample selection in BALD to a batch of samples by jointly exploiting their mutual information with the model parameters [77]. Martinez-Arellano et al implement a BNN-based active learning method for tool condition monitoring that can handle the appearance of the new sensory measurement patterns caused by degradation [78]. The uncertainty information determines which data to label and utilize for retraining in order to improve performance.…”

A sampling-based approach for information-theoretic inspection management

“…Neural networks have been applied with uncertainty sampling to classify images of defects in a dataset concerning civil structures [ 45 ]. The work [ 46 ] proposes a Bayesian convolutional neural network for tool monitoring, using MES. Finally, an adaptive probabilistic framework is proposed in [ 47 ] for active data selection to aid a particle filter-based damage-progression model.…”

Coupling digital simulation and machine learning metamodel through an active learning approach in Industry 4.0 context

“…For example, Rastogi and Sharma (2019) propose to carefully select points from a database to label them and train a Laplacian twin support vector machine, which is a classifier able to learn from both labeled and unlabeled data. Similarly, Martinez Arellano and Ratchev (2019) propose an application of pooled-based active learning in an Industry 4.0 context. They study the use of Bayesian Neural Networks for tool wear detection.…”