The awards have been announced during the Gala Dinner on Thursday evening, at the Museo di Risorgimento.
Best paper awards:
First price:
Jannik ZGRAGGEN, Gianmarco PIZZA, Lilach GOREN HUBER
(Zurich University of Applied Sciences, Switzerland)
For the paper entitled: Uncertainty Informed Anomaly Scores with Deep Learning: Robust Fault Detection with Limited Data
Second price:
Juseong LEE, Ingeborg DE PATER, Stan BOEKWEIT, Mihaela MITICI
(Delft University of Technology, Netherlands)
For the paper entitled: Remaining-Useful-Life prognostics for opportunistic grouping of maintenance of landing gear brakes for a fleet of aircraft
Doctoral Symposium awards:
First price:
Maximilian-Peter RADTKE
(Technische Hochschule Ingolstadt, Germany)
For the paper entitled: Combining Knowledge and Deep Learning for Prognostics and Health Management
Second price:
Wei Kun DENG
(National Engineering School of Tarbes, France)
For the paper entitled: Physics Informed Self Supervised Learning For Fault Diagnostics and Prognostics in the Context of Sparse and Noisy Data
Third price:
Lilin JIA
(Cranfield University, United Kingdom)
For the paper entitled: A Novel Way to Apply Transfer Learning to Aircraft System Fault Diagnosis
Data Challenge awards:
First price:
Alexandre GAFFET, Nathalie BARBOSA ROA, Pauline RIBOT, Elodie CHANTERY, Christophe MERLE
For their work on: Hierarchical XGBoost Early Detection Method for Quality and Productivity Improvement
of Electronics Manufacturing Systems
Second price:
John TACO LOPEZ, Prayag GORE, Takanobu MINAMI, Pradeep KUNDU, Alexander SUER, Jay LEE
For their work on: A Novel Methodology for Health Assessment in Printed Circuit Boards
Third price:
Haichuan TANG, Yin TIAN, Junyan DAI, Yuan WANG, Jianli CONG, Qi LIU, Xuejun ZHAO, Yunxiao FU
For their work on: Prediction of Production Line Status for Printed Circuit Boards
Fourth price:
Immo SCHMIDT, Lorenz DINGELDEIN, David HUNEMOHR, Henrik SIMON, Max WEIGERT
For their work on: Application of Machine Learning Methods to Predict the Quality of Electric Circuit Boards of a Production Line