• Homepage
  • Navigation
  • Search
  • Content
  • Footer
  • Contact
  • Sitemap

Intelligent Maintenance Systems

Main Navigation

  • News
    • NewsSee overview
    • Events
    • Vacancies
  • About us
    • About usSee overview
    • How to find us
  • People
    • PeopleSee overview
    • Prof. Dr. Olga Fink
    • PostDocs
      • Dr. Gaëtan Frusque
      • Dr. Pegah Rokh Foroz
    • PhD Students
      • Zhichao Han
      • Katharina Rombach
      • Yuan Tian
      • Ismail Nejjar
      • Mengjie Zhao
      • Chi-Ching Hsu
      • add Display all
    • Alumni
  • Research
    • ResearchSee overview
    • Research Projects
  • Education
    • EducationSee overview
    • Lectures
    • Master's thesis
  • Publications
    • PublicationsSee overview
    • Journal papers
    • Conference papers
    • Book chapters
  • IMS Network
    • IMS NetworkSee overview
    • Events

Search

Departments

  • ETH Zurich
  • D-BAUG
  • IBI
  • IMS

Language Selection

You are here

  • Homepage chevron_right
  • News chevron_right
  • …
  • IMS News chevron_right
  • 2021 chevron_right
  • 05

05

Gabriel Rodriguez wins the 2021 ASCE - Swiss prize

The 2021 ASCE - Swiss prize is a Swiss-wide award, awarded annually to a single winner from a pool of candidates across all Swiss Institutions that offer an MSc degree in Civil Engineering to acknowledge the high scientific quality and contribution of the thesis to civil engineering knowledge. Congratulations  to Gabriel Rodriguez for the high quality Master thesis!

31.05.2021 by Sandra Jennifer Schmid

New paper entitled "Implicit supervision for fault detection and segmentation of emerging fault types with Deep Variational Autoencoders" has been published

Neurocomputing Journal

The paper, where we propose an adapted version of the variational autoencoder that uses all available data at training time, has been published in the journal "Neurocomputing".

19.05.2021 by Sandra Jennifer Schmid

Our Paper on “Contrastive Learning for Fault Detection and Diagnostics in the Context of Changing Operating Conditions and Novel Fault Types” has been published

Contrastive Learning

Our new paper on “Contrastive Learning for Fault Detection and Diagnostics in the Context of Changing Operating Conditions and Novel Fault Types” has been accepted by the Special Issue "Artificial Intelligence for Data-Driven Fault Detection and Diagnosis" of the Journal Sensors

19.05.2021 by Sandra Jennifer Schmid

Footer

Search

Services

  • Student portal
  • Alumni association
  • Staffnet
  • Contact
  • Login

Departments

  • D-ARCH Architecture
  • D-BAUG Civil, Environmental and Geomatic Engineering
  • D-BIOL Biology
  • D-BSSE Biosystems Science and Engineering
  • D-CHAB Chemistry and Applied Biosciences
  • D-EAPS Earth and Planetary Sciences
  • D-GESS Humanities, Social and Political Sciences
  • D-HEST Health Sciences and Technology
  • D-INFK Computer Science
  • D-ITET Information Technology and Electrical Engineering
  • D-MATH Mathematics
  • D-MATL Department of Materials
  • D-MAVT Mechanical and Process Engineering
  • D-MTEC Management, Technology and Economics
  • D-PHYS Physics
  • D-USYS Environmental Systems Science

Table of contents and legal

  • Sitemap
  • Imprint
  • Accessibility Statement
  • Disclaimer & Copyright
  • Privacy Policy
© 2025  Eidgenössische Technische Hochschule Zürich
JavaScript has been disabled in your browser