Institute of Machine Tools and Manufacturing

Open Opportunities

Master/ Bachelor Thesis: Deep Learning and Uncertainty in Predictive Maintenance ETH Zurich Institute of Machine Tools and Manufacturing Introduction Predictive Maintenance is considered part of the holistic approach Prognostics and Health Management (PHM). It consists of three main tasks: fault detection, fault diagnostics, and prognostics; the prediction of the remaining useful life (RUL) of a system or component. In recent years, practitioners and researchers have turned to Deep Learning (DL)-based methods to address these tasks. This trend is supported by the increasing availability of sensor data of industrial systems. The systems in question are often safety-critical and failures can lead to expensive or catastrophic events. In such contexts, the uncertainty about an information is often more valuable than the information itself. A multitude of models with different Deep Learning architectures (CCNs, Autoencoder, RBMs, etc.) has been published to address the above-mentioned tasks. However, the great majority of approaches does not incorporate ways to express uncertainties. Problem description Deep Learning models usually only deliver point estimates of their predictions. Monte Carlo dropout is a method that overcomes this limitation: An arbitrary neural network with dropout applied to every layer is mathematically equivalent to an approximation of Bayesian inference. It provides a relatively simple way of extracting uncertainties from Deep Learning models without impeding computational efficiency or model accuracy. The objective of this thesis is to evaluate the state-of-the-art and extend existing DL models to account for uncertainty. Artificial Intelligence and Signal and Image Processing, Manufacturing Engineering, Mechanical and Industrial Engineering Bachelor Thesis, Master Thesis

Innovative process monitoring for Powder Bed Fusion of Polymers ETH Zurich Institute of Machine Tools and Manufacturing Process monitoring of industrial additive manufacturing technologies is an extremely important topic for quality assurance purposes. A new tool has been acquired, and needs to be integrated and tested in our production machine for powder bed fusion of polymers. Mechanical and Industrial Engineering, Plastics, Polymers, Printing Technology IDEA League Student Grant (IDL), Lab Practice, Master Thesis, Semester Project