Loading...
hidden

View Mobile version

Meta navigation

Startseite – Hochschule Luzern

Language selection and important links

  • Contents
  • Contact
  • Login
  • De
  • En
Search

Main navigation

School navigation

  • Engineering and Architecture
  • Business
  • Computer Science
  • Social Work
  • Design, Film and Art​
  • Music
  • Health Sciences

Sub-navigation

  • Degree Programmes
  • Continuing Education
  • Research
  • International
  • Campus
  • About us
  • News

Sub-navigation

Breadcrumbs

  1. Research Research
  2. Research and Services Projects Research and Services Projects
  3. Physics Informed Anomaly Detection Physics Informed Anomaly Detection

Physics Informed Anomaly Detection

The aim of this project is to integrate a physical model for an industrial pipetting system into neural networks in order to improve anomaly detection and gain a more comprehensive understanding of the pipetting process.

Brief information

Status:

Completed

Period:

25.02.2023 - 30.06.2023

Overview

The findings of this project demonstrate compelling results in identifying pipetting anomalies in pressure time-series data using various machine learning techniques. However, the methods employed were unable to detect rare normal instances such as partial coagulation. To address this issue, more advanced and refined approaches are needed.

The objective of this master's thesis is to explore the integration of physical models of the pipetting system into neural networks to enhance anomaly detection and achieve a more comprehensive understanding of the pipetting process.

In the initial phase, a physical model of an air-pressure pipetting system will be constructed based on the Bernoulli equation. This Bernoulli equation is intended to be integrated into a neural network to estimate the resulting pipetting volume based on a pressure curve and piston motion. The resultant model should be capable of distinguishing between abnormal and rare normal instances.

In the subsequent phase, we aim to investigate the extent to which the resulting physically-informed model can provide deeper insights into the pipetting process. Furthermore, additional techniques and methods will be explored to enhance the interpretability of the pipetting process for non-experts.

Ultimately, a Minimum Viable Product will be introduced to showcase a potential MLOps architecture for the development of pipetting monitoring algorithms and to illustrate how the resulting models can be employed to devise diagnostic tools.

hidden

Facts

Type of project

Forschung

Internal organisations involved
  • Algorithmic Business F&E
Funding
  • Private / Stiftungen
hidden

Persons involved: internal

Project manager
  • Mirko Birbaumer
Project Co-Head
  • Alexander Meier
  • Alexander Meier
Member of project team
  • Simon Durrer

Brief information

Status:

Completed

Period:

02/25/2023 - 06/30/2023

Project Head

Prof. Dr. Mirko Birbaumer

Professor

+41 41 349 33 40

Show email

Footer(s)

FH Zentralschweiz

Social media links

  •  Instagram
  •  LinkedIn
  •  TikTok
  •  Facebook
  •  YouTube
  •  Flickr

Contact

Logo Lucerne University of Applied Sciences and Arts

Lucerne University of Applied Sciences and Arts


Werftestrasse 4
6002 Luzern

+41 41 228 42 42

info@hslu.ch

Direct entry

  • Bachelor’s Degree
  • Master’s Degree
  • Prospective Students (Continuing & Executive Programmes)
  • For Students
  • For Employees

Quick link

  • People Finder
  • University Buildings & Campus Locations
  • News
  • Libraries
  • Events
  • Media Relations
  • Jobs and Careers
  • Home
  • Hiring Rooms

Static links

  • Newsletter
  • Data protection notice
  • Publishing Acknowledgements
Logo Swissuniversities

QrCode

QrCode